We’ve rounded up the best intrusion prevention systems to help make the selection process less daunting. Here are our top picks:

• Cisco Secure Next-Generation Intrusion Prevention System: Best for Comprehensive Network Security
• Fidelis Network: Best for Advanced Threat Detection Response
• Palo Alto Networks Advanced Threat Prevention: Best for ZeroDay Exploits
• Trellix Intrusion Prevention System: Best for On-Prem and Virtual Networks

Top Intrusion Prevention System Comparison At-a-Glance

Here’s a look at how the top IPSs compared based on key features.

Jump to:

1. Key Intrusion Prevention System Features
2. How to Choose an IPS
3. Frequently Asked Questions (FAQs)

Cisco Secure Next-Generation Intrusion Prevention System

Best for comprehensive network security

Cisco offers advanced threat protection solutions with Cisco Secure IPS. This cloud-native platform offers robust security with unified visibility and intuitive automation. It gathers and correlates global intelligence in a single view and can handle large traffic volumes without impacting the network performance.

This highly flexible solution can be easily deployed across different network environments as its open architecture supports Amazon Web Services (AWS), VMWare, Azure, and other hypervisors.

Features

• Enhanced visibility with Firepower Management Center
• Constantly updated early-warning system
• Flexible deployment options for inline inspection or passive detection
• Cisco Threat Intelligence Director for third-party data ingestion

Pros

• Real-time data inputs optimize data security
• Easy integration without major hardware changes
• High scalability with purpose-built solutions

Cons

• Expensive for small-scale organizations
• Initial integration challenges

Pricing

Cisco offers free trials for most products, including its IPS, but does not make its pricing readily available. For details, contact Sales Support.

Fidelis Network

Best for Advanced Threat Detection Response

Fidelis Network improves security efficiency by detecting advanced threats and behavioral anomalies, employing a proactive cyber-defense strategy to more quickly detect and respond to threats before they can affect a business. Fidelis Network can bolster data security with rich insights into bi-directional encrypted traffic.

This specific network defense solution helps prevent future breaches with both real-time and retrospective analysis.

Features

• Patented Deep Session Inspection for data exfiltration
• Improved response with the MITRE ATT&CK framework and intelligence feed from Fidelis Cybersecurity
• Unified network detection and response (NDR) solution for simplified network security
• Customizable real-time content analysis rules for proactive network security

Pros

• Faster threat analysis and improved security efficiency
• Deeper visibility and threat detection with more than 300 metadata attributes
• Single-view and consolidated network alerts with rich cyber terrain mapping

Cons

• Complex configuration and setup
• High-traffic environments cause network latency
• Tighter integration with other tools is required

Pricing

Fidelis Network offers a 15-day free trial, and will schedule a demo before it to show off the system’s capabilities and features.

Palo Alto Networks Advanced Threat Prevention 

Best for Zero-Day Exploits

Palo Alto Networks’ Advanced Threat Prevention is based on purpose-built, inline deep learning models that secure businesses from the most advanced and evasive threats. Powered by multi-pronged detection mechanisms that efficiently take care of unknown injection attacks and zero-day exploits, this infinitely scalable solution blocks command and control (C2) attacks in real time without compromising performance.

Features

• ML-Powered NGFWs for complete visibility
• Customized protection with Snort and Suricata signature support
• Real-time analysis with enhanced DNS Security Cloud Service
• Latest security updates from Advanced WildFire

Pros

• Ultra low-latency native cloud service
• Combined App-ID and User-ID identification technologies
• Customized vulnerability signatures
• Complete DNS threat coverage

Cons

• Overly complex implementation for simple configurations
• High upfront costs

Pricing 

Palo Alto Networks offers free trials, hands-on demos, and personalized tours for its products and solutions, but does not make its pricing models publicly available. Contact sales for details.

Trellix Intrusion Prevention System

Best for On-Prem and Virtual Networks

Trellix Intrusion Prevention System offers comprehensive and effective security for business networks, offering two variants: Trellix Intrusion Prevention System and Trellix Virtual Intrusion Prevention System. The virtual variant takes care of the private and public cloud requirements, and secures virtualized environments using advanced inspection technologies.

Features

• Botnet intrusion detection across the network
• Enhanced threat correlation with network threat behavior analysis
• Inbound and outbound SSL decryption
• East-west network visibility

Pros

• Both signature-based and signature-less intrusion detection
• Unified physical and virtual security
• Maximum security and performance (scalability up to 100 Gbps)
• Shared licensing and throughput model

Cons

• Older variants and models still exist
• Confusion pricing options
• High rates of false positives

Pricing

Schedule a demo to learn whether Trellix meets specific requirements. The vendor does not make pricing models publicly available; contact sales.

Key IPS Features

When deciding on an intrusion prevention system, make sure the features and capabilities match specific needs. Key features include the following:

Real-time alerts

Proactive threat detection and prompt incident response require real-time visibility. Timely alerts help implement preventive measures before any significant damage to the security posture. Advanced IPSs have real-time monitoring capabilities to identify potential vulnerabilities and minimize the impact of security incidents.

Integration with other security systems

Intrusion prevention systems cannot operate in isolation. For the efficient protection of the entire business security infrastructure, they must integrate with other security solutions and platforms for a coordinated response. This also helps with the centralized management of security incidents.

Type of intrusion detection

There are mainly two types of intrusion detection: network-based and host-based. While network-based intrusion detection examines and analyzes the network traffic for vulnerabilities, host-based intrusion detection checks individual systems like servers, endpoints, or particular assets.

Automatic updates

Automatic updates can help ensure an IPS adapt to the continuously evolving threat landscape of new threats and newly discovered vulnerabilities. They can also help keep pace with changing compliance and regulatory requirements and implement the latest security patches.

Threat intelligence

Threat intelligence helps an IPS enhance detection capabilities and minimize vulnerabilities with efficient mitigation strategies. With threat intelligence capabilities, IPS solutions access timely and actionable information to develop effective response strategies.

How to Choose an IPS

Here are some factors to consider when choosing an IPS:

Configuration type

There are broadly four types of IPS configurations depending on the network environment, security policies, and requirements where they will be implemented: network-based, host-based, wireless, and network behavior analysis system. Multiple configurations can also support complex pathways.

Detection capabilities

Intrusion prevention systems use different detection techniques to identify malicious activities—primarily signature-based, anomaly-based, and protocol-based. Signature-based detection helps detect consistent cyber threat patterns from a static list of known signatures, while anomaly-based detection can detect abnormalities within normal activity patterns. Protocol-based systems offer the flexibility to set references for benign protocol activities.

Integration options

Intrusion prevention systems can be integrated using dedicated hardware and software, or incorporated within existing enterprise security controls. Businesses that don’t want to upgrade system architecture or invest in products or resources can rely on managed service providers for security, but an IPS purchased and installed on the network offers more control and authority.

Frequently Asked Questions (FAQs)

What is the difference between intrusion detection systems and intrusion prevention systems?

Intrusion detection systems help detect security incidents and threats and send alerts to the Security Operations Center (SOC). Issues are investigated by security personnel and countermeasures executed accordingly. Essentially, they’re monitoring tools. While intrusion prevention systems also detect potential threats and malicious incidents, they automatically take appropriate actions, making them highly proactive, control-based cybersecurity solutions.

How do intrusion prevention systems help businesses?

Intrusion prevention systems are key to enterprise security as they help prevent serious and sophisticated attacks. Some of the key benefits of IPS for businesses are:

• Reduced strain on IT teams through automated response
• Customized security controls as per requirements
• Improved performance by filtering out malicious traffic

Do intrusion prevention systems affect network performance?

Intrusion prevention systems may slow down the network in the case of inadequate bandwidth and capacity, heavy traffic loads, or computational burdens.

Methodology

In order to provide an objective and comprehensive comparison of the various IPSs available in the market, we followed a structured research methodology. We defined evaluation criteria, conducted market research, collected data on each solution, evaluated and scored them, cross-verified our findings, and documented the results. Additionally, we considered user reviews and feedback to gain valuable insights into the real-world performance and customer satisfaction of each intrusion prevention solution.

Bottom Line: Top Intrusion Prevention Systems

The top intrusion prevention systems all work to protect enterprise networks from the ever-present, always evolving threat of cyberattack, but some stand out for different use cases. Selecting the right one will depend on the organization’s security needs, goals, and budget. Regular evaluation and updates are crucial to staying ahead of evolving threats and ensuring a robust security posture—the right IPS can enhance network security, protect sensitive data, and safeguard a business against potential cyber threats.

To help find the right tool, we’ve compared the top five data migration tools to move, transform, and optimize your organization’s data efficiently. Here are our top picks:

1. AWS Database Migration Service: Best for AWS Cloud Migration
2. IBM Informix: Best for Versatile Data Management
3. Matillion: Best for Data Productivity
4. Fivetran: Best for Automated Data Movement
5. Stitch: Best for Versatile Cloud Data Pipelines

Top 5 Data Migration Tools Comparison

Take a look at some of the top data migration tools and their features:

Jump to:

• Key Features of Data Migration Tools
• How to Choose the Best Data Migration Tools for Your Business
• Frequently Asked Questions (FAQs)
• Methodology
• Bottom Line: Choosing the Right Data Migration Tool

AWS Database Migration Service

Best for AWS Cloud Migration

The technology giant Amazon extends data migration services to customers through AWS Database Migration Service. It removes undifferentiated database management tasks to simplify the migration process. This high-performance tool offers the additional advantage of access to other AWS solutions and services. Thus, it is best suited for businesses looking for AWS cloud migration support and features.

Pricing

The AWS Free Tier plan helps users get started with the data migration service for free. See the AWS Pricing Calculator for detailed pricing plans and information.

Features

• Centralized access with AWS Management Console
• Multi-AZ and ongoing data replication and monitoring
• Homogeneous and heterogeneous migration support
• Automated migration planning with AWS DMS Fleet Advisor

Pros

• Simple and easy-to-use service
• Automatic schema assessment and conversion
• Supports migration among 20-plus databases and analytics engines

Cons

• Large-scale data migration can be costly
• Frequent changes in pricing

IBM Informix

Best for Versatile Data Management 

IBM offers data management and migration solutions through an embeddable database: IBM Informix. It is a highly versatile tool that simplifies administration and optimizes database performance. It relies on a hybrid cloud infrastructure. Informix is best for multi-tiered architectures that require device-level processing.

Pricing

IBM Informix Developer Edition is ideal for development, testing, and prototyping and can be downloaded for free. The Informix Innovator-C Edition supports small production workloads and is also freely available. Other editions are available that offer a complete suite of Informix features. Contact the team for their pricing details.

Features

• Real-time analytics for transactional workloads
• High availability data replication (HADR) for mission-critical environments
• Event-driven processing and smart triggers for automated data management
• Silent installation with a memory footprint of only 100 MB

Pros

• Robust processing and integration capabilities
• Minimal administrative requirements
• Native encryption for data protection
• Real-time analytics for fast insights

Cons

• Big data transfers can slow down the platform
• Complex pricing policies

Matillion

Best for Data Productivity

Matillion helps businesses with next-gen ETL (extract, transform, load) solutions for efficient data orchestration. It can automate and accelerate data migration with its universal data collectors and pipelines. With its advanced capabilities, it helps extract full value from a business’s existing infrastructure.

Pricing

Matillion follows a simple, predictable, and flexible pricing model along with free trial versions. It offers Free, Basic, Advanced, and Enterprise editions and pay-as-you-go options. The minimum price for paid plans is $2 per credit. Contact the vendor to speak to an expert for details.

Features

• Change data capture and batch data loading for simplified pipeline management
• Low-code/no-code GUI
• Reverse ETL and prebuilt connectors for easy data sync back
• Drag-and-drop functionality for easier usage

Pros

• Fast data ingestion and integration
• Enterprise assurance
• Post-load transformations
• Customizable configurations

Cons

• High-volume data load can cause crashes
• Support issues
• Needs better documentation

Fivetran

Best for Automated Data Movement

Fivetran offers an efficient platform for data migration. This cloud-based tool relies on a fully-managed ELT architecture that efficiently handles all data integration tasks. It has numerous database replication methods that can manage extremely large workloads.

Pricing

Fivetran offers a 14-day free trial option. It has Free, Starter, Standard, Enterprise, Business Critical, and Private Deployment plans with different features and pricing options. Contact the sales team for specific pricing details.

Features

• More than 300 prebuilt, no-code source connectors
• Quickstart data models for automated transformations
• End-to-end data monitoring with lineage graphs
• Fivetran API for programmatic scaling

Pros

• Flexible connection options for secure deployment
• Advanced role-based access control
• Data catalog integrations for metadata sharing

Cons

• Only cloud-based solutions
• Lacks support for data lakes
• Expensive option for large volumes of data

Stitch

Best for Versatile Cloud Data Pipelines

Stitch offers fully automated cloud data pipelines that can be used without any coding expertise. It helps consolidate data from a vast range of data sources. This enterprise-grade cloud ETL platform is highly trusted for extracting actionable insights.

Pricing

Stitch offers a free trial for two weeks. It follows a transparent and predictable pricing model with no hidden fees. There are three plans: Standard, Advanced, and Premium. The minimum price starts at $100 per month, if billed monthly, or $1,000 if billed annually. Contact the sales team for exact pricing details for each plan.

Features

• 140+ popular data sources
• External processing engines like MapReduce and Apache Spark
• In-app chat support

Pros

• No coding is required
• Centralized, fresh, and analysis-ready data
• Automatically updated pipelines

Cons

• Needs a more friendly user interface
• Customer support issues

Key Features of Data Migration Tools

The primary purpose of using data migration tools is to simplify data transfer across different systems, ensuring integrity and accuracy. Some of the key features they include to accomplish this goal are:

Data Transformation

Data migration tools need to consolidate data from multiple sources, which requires them to have data transformation capabilities. Having a standardized data structure or format across different environments is impossible, but data transformation features can help to make these disparate data sources more manageable and uniform. These tools must optimize data for the destination system, ensuring consistency and coherence. They must also be able to identify inconsistencies or issues and transform data as per target requirements.

Connectors

Data migration tools connect various data sources and targets. Thus, they require various connector modules to help them interact with different systems during a migration. With comprehensive connector coverage, data migration tools can establish a link between the source and targets using required protocols, APIs, or drivers. As a result, data can be efficiently extracted from the source and loaded into the target.

Real-time Analysis

Efficient data migration demands real-time insights for seamless data exchange. Real-time analysis helps in the early detection of errors and accurate data mapping between the source and target. This makes it an essential feature of data migration tools, as it helps with performance monitoring, error detection and prevention, data validation, synchronization, and consistency.

Security and Compliance

Data migrations involve substantial risks like information misuse, unauthorized access, data loss, and corruption. These incidents can lead to severe financial and reputational damages, and may also involve potential legal liabilities. Due to these risks, data migration tools must adhere to strict security and compliance standards to minimize security incidents and other risky outcomes.

Customization

Different businesses have different data requirements. To meet business expectations, data migration tools must offer customization features for changing business requirements. A strong data migration tool will also provide the flexibility and adaptability to help organizations with tailored migration processes.

How to Choose the Best Data Migration Tool for Your Business

Data migrations and similar operations are risky processes, as they involve moving your organization’s sensitive information. Thus, choosing a versatile and reliable tool that ensures a smooth and successful migration is essential.

Here are some key considerations to help select the best data migration tool for specific business needs:

Configuration Type

There are two distinct types of data tool configurations: cloud-based and on-premises. On-premises data tools do not rely on the cloud for data transfer. Instead, they migrate data within the organizational infrastructure, offering full-stack control. These are effective solutions when the business desires to restrict data within its own servers.

Cloud-based data migration tools transfer and store data using cloud platforms on cloud servers. The architecture can be expanded effectively due to the quick availability of resources. These tools also facilitate data migration from on-premises to cloud systems. In addition, they are highly secure and cost-effective.

Enterprise Cloud Migration Services

Choosing enterprise-focused cloud migration services can give you an additional edge. Data migration services that are specifically designed for enterprises can more effectively take care of industry standards and maintain top-notch IT infrastructure. Besides, they offer constant updates based on the latest advancements in technologies and methodologies. They can handle complex business projects with well-designed transformation processes.

Technical Support

When choosing a data migration tool, it is also essential to pay attention to technical support capabilities offered by the vendor. Businesses especially need post-migration support to address any issues. They must also help develop robust backup and recovery strategies to deal with system failures or other potential challenges.

Additional Considerations

There are many different types of data migration, like storage, database, cloud, application, data center, and business process migration. Therefore, you should select the most suitable migration tool based on your business goals and the types of migration you want to complete.

Apart from these aspects, it is also vital that the tool you select integrates efficiently with your current business infrastructure and supports data sources and target systems. This can reduce disruptions and compatibility issues.

Frequently Asked Questions (FAQs)

How Do Data Migration Tools Benefit Businesses?

Data migration tools benefit businesses by streamlining data transfer, storage, and management processes, ensuring accuracy. Since they automate these processes, companies can focus on other essential operational aspects. Also, these tools offer the necessary flexibility and scalability to cater to specific demands.

What Types of Data Can Data Migration Tools Handle?

Data migration tools handle enormous volumes of data in different formats and structures within different systems. They deal with both structured and unstructured data and need to work with databases, enterprise applications, data warehouses, spreadsheets, JSON, XML, CSV, and other file formats.

What Are Open-source Data Migration Tools?

Open-source data migration tools are publicly accessible, typically free-to-use solutions. The source code is available on a central repository and can be customized too. Although they require technically skilled employees for proper implementation and use, community-driven support is a major plus with open-source technology, as you can get assistance from technical experts whenever it’s needed. Therefore, these are ideal options for small-scale projects involving lesser complexities.

Methodology

We implemented a structured research methodology to analyze different data migration tools available in the current marketplace. The research was based on specified evaluation criteria and essential feature requirements.

We evaluated each tool’s real-world performance based on user reviews and performance, as customer satisfaction is crucial. After in-depth analysis with several other criteria, we finally documented the top results for the best data migration tools.

Bottom Line: Choosing the Right Data Migration Tool

Choosing the right data migration tool is crucial for aligning specific business goals. Throughout this article, we explored the top five tools, each with unique strengths. When selecting a data migration solution for your business, consider factors like data complexity, scale, real-time vs. batch processing, security, and compatibility.

Remember, the key to successful data migration lies in aligning your specific business goals with the capabilities offered by your chosen tool. Take the time to evaluate and understand your requirements, consult with stakeholders, and make an informed decision that sets your organization on the path to achieving its desired outcomes.

Also See

Also See Data Migration Trends

Organizations continue to face a critical decision when it comes to their IT infrastructure: fully embrace the cloud, or adopt an on-premises model? The question remains pertinent despite the fact that the cloud has been around for almost two decades now. This article will provide deeper context to this question with the goal of helping organizations make better-informed infrastructure decisions unique to their specific requirements and environments.

On-Premises vs. Cloud

As their name implies, on-premises environments have computing resources and systems that are physically located within an organization’s premises or facilities. This gives them direct control and ownership over their IT infrastructure, including the physical infrastructure, security measures, and network connectivity. This means they are also responsible for procuring, installing, configuring, and managing all the necessary components as well as ensuring their maintenance, upgrades, backups, and security.

In contrast, a cloud-based infrastructure involves the deployment and maintenance of servers, storage devices, networking equipment, and other hardware and software resources in the cloud service provider’s data centers. A cloud infrastructure is easier to deploy and manage initially, with no required upfront capital expenditures in hardware. Cost-wise, the cloud uses a metered, pay-per-use model, which—depending on scaling requirements and other factors—can be more cost-effective than on-premises.

Cloud Pros and Cons

The cloud has revolutionized the way organizations consume and manage data, applications, and IT resources. Some crucial benefits of the cloud include:

• Unprecedented Scalability: Cloud infrastructure offers unparalleled scalability, allowing businesses to scale resources up or down based on demand. This ensures optimal performance and cost efficiency.
• Significant Cost Savings: Cloud computing eliminates the need for capital expenditure on hardware, maintenance, and upgrades. Instead, businesses can opt for a pay-as-you-go model, reducing upfront costs and enabling predictable budgeting.
• Expanded Accessibility and Flexibility: Cloud services can be accessed from anywhere with an internet connection, providing seamless collaboration and remote access to resources. This flexibility is especially beneficial for distributed teams and remote work environments.
• Automatic Updates: Cloud providers take care of infrastructure updates and security patches, freeing up internal IT teams from routine maintenance tasks.
• Easier Disaster Recovery: Cloud-based backups and disaster recovery solutions offer data redundancy and high availability, minimizing downtime and ensuring business continuity.

Some potential drawbacks to consider when adopting cloud infrastructures include the following:

• Data Security and Privacy Concerns: Organizations that entrust sensitive data to a cloud service provider may raise security and privacy concerns with their own customers.
• Compliance Issues: Cloud service providers typically implement robust security measures; however, organizations must ensure compliance with relevant regulations and industry standards.
• Vendor Lock-in: Migrating from one cloud service provider to another can be challenging and costly, as organizations may become dependent on specific features or services offered by a particular provider.

On-Premises Pros and Cons

On-premises IT infrastructures provide organizations with significant benefits absent in cloud implementations, including the following:

• More Data Control: On-premises infrastructures provide organizations with complete control over their data and resources—a potential hard requirement in highly regulated industries or for organizations with strict compliance requirements.
• Lower Latency: On-premises infrastructures can offer lower latency, since data processing and storage occur locally.
• More Customization Options: On-premises allows organizations to custom-tailor their IT environments for their specific needs and integrate legacy systems seamlessly.

On-premises infrastructures also have their share of drawbacks:

• High Upfront Costs: Building on-premises infrastructure involves significant upfront costs, including hardware, software licenses, and dedicated IT staff.
• Maintenance and Updates: Organizations are responsible for maintaining and updating their own infrastructure, which can be resource-intensive and require skilled IT personnel.
• Scalability Challenges: Scaling on-premises infrastructures can be complex, time-consuming, and costly, requiring additional hardware purchases and configuration adjustments.
• Limited Accessibility: On-premises infrastructure may pose limitations for remote work and collaboration, restricting accessibility to data and applications.

Cloud vs. On-Premises: How to Decide

The choice between cloud and on-premises infrastructure ultimately depends on the unique needs and priorities of each organization. Here’s a look at how each solution measures up on key feature areas.

Cost

Because cloud service providers handle hardware maintenance, software updates, and security, on-premises solutions may seem costlier; however, once on-premises IT infrastructure is established, the ongoing costs can be lower compared to long-term cloud usage. Additionally, cloud computing costs can easily skyrocket if not properly configured and managed. However, for organizations that need to scale their resources according to fluctuating demand, the cloud’s pay-as-you-go pricing model can result in more predictable monthly costs, if optimized correctly.

Ease of Implementation

To implement a cloud-based infrastructure, organizations must select a cloud service provider, migrate applications and data, and configure the necessary resources. Over the years, standard best practices for migrating from on-premises to the cloud have emerged, and cloud providers offer extensive documentation, support, and tools to facilitate the migration process. However, organizations should nonetheless carefully plan and execute their cloud migrations to ensure minimal disruption and optimal performance.

Implementing on-premises infrastructures also require significant planning, hardware procurement, installation, and configuration; however, in this case organizations must allocate resources for building and maintaining the infrastructure, including skilled IT personnel for ongoing management.

Security

Cloud service providers invest heavily in security measures, including data encryption, access controls, and threat detection systems. They employ dedicated security teams and adhere to industry standards and compliance regulations. However, organizations must also take responsibility for securing their applications, data, and user access through proper configuration and robust security practices.

When it comes to on-premises, organizations are left to their own devices and have direct control over their security measures. They can implement specific security protocols, firewalls, and intrusion detection systems tailored to their requirements. However, this also means that organizations are solely responsible for ensuring the effectiveness of these security measures and staying up to date with the latest threats and vulnerabilities.

Compliance

Cloud service providers often offer compliance certifications and attestations to demonstrate their adherence to industry-specific regulations and security standards. This is crucial for organizations operating in highly regulated industries or handling sensitive data; however, firms must nonetheless ensure that their specific cloud-based IT assets are properly configured, and that any additional security measures are in place to meet specific compliance requirements. On-premises infrastructure allows organizations to maintain full control over compliance and regulatory requirements. They can implement customized security controls and monitoring processes to meet specific compliance standards.

Data Accessibility

Cloud services enable universal accessibility, allowing users to access data and applications from any location with an internet connection. This flexibility is particularly beneficial for remote workforces, enabling seamless collaboration and productivity. On-premises infrastructures may pose limitations on accessibility, especially for remote or geographically distributed teams. Organizations must establish secure remote access mechanisms to enable remote access to on-premises IT resources.

The Hybrid Cloud: Best of Both?

In some cases, organizations may opt for a hybrid cloud approach that combines elements of both cloud and on-premises infrastructures. This model allows organizations to leverage the scalability and flexibility of the cloud while maintaining sensitive data or critical applications on-premises. For many organizations, hybrid cloud environments provide the best of both worlds, allowing for a balance of cost efficiency, flexibility, and data control.

Cloud and On-Premises Use Cases

The choice between cloud and on-premises infrastructures depends on the specific needs, priorities, and circumstances of each organization. The following are several ideal use cases for cloud and on-premises IT infrastructures, with factors such as cost, scalability, data control, compliance requirements, and security all come into play when making an informed decision.

Cloud

• Startups and Small Businesses: The cloud offers a cost-effective solution for startups and small businesses, eliminating the need for substantial upfront investments in infrastructure and IT personnel.
• Scalability and Bursting: Organizations with fluctuating workloads or seasonal demand can benefit from the scalability offered by the cloud. They can easily scale resources up or down as needed, optimizing costs and performance.
• Collaboration and Remote Work: Cloud services enable seamless collaboration among distributed teams, facilitating remote work and improving productivity.

On-Premises

• Highly Regulated Industries: Organizations operating in industries with strict compliance requirements (e.g., finance or healthcare) often go with on-premises to maintain full control over data security and compliance.
• Supporting Legacy Systems: Organizations with legacy systems may go with on-premises in order to integrate and coexist with their existing environment seamlessly.
• Data Sensitivity: Organizations handling highly sensitive data (e.g., government agencies or defense contractors) may need to keep their data on-premises to minimize risks associated with external data storage.

Bottom Line: Cloud vs. On-Premises

The choice between cloud and on-premises infrastructure ultimately depends on the unique needs and priorities of each organization. Cloud computing offers scalability, flexibility, and cost savings, but it requires careful consideration of issues related to data security and potential vendor lock-in, to name a few. On-premises infrastructures provide more data control, customization options, and lower latency, but come with higher upfront costs and limited accessibility. The hybrid cloud approach can be an ideal solution for organizations seeking a balance between cost efficiency and data control. Ultimately, organizations should assess their specific requirements, compliance needs, budget, and long-term goals to determine the most suitable infrastructure model for their organization.

FAQ

What is the cloud?
The cloud refers to the delivery of computing services over the internet, allowing businesses to access and utilize resources such as storage, servers, databases, and software applications on-demand, without the need for physical infrastructure.

What does on-premises mean?
On-premises refers to hosting all hardware, servers, and applications within an organization’s own premises or data center, managed and maintained by its own IT staff.

What are the main benefits of the cloud?
The cloud offers several benefits, including scalability, cost savings, flexibility and accessibility, automatic updates, and streamlined disaster recovery options.

What are the benefits of on-premises infrastructure?
On-premises infrastructure offers complete control over data, lower latency due to the localization of IT resources, and more customization options.

Which is more cost-effective, the cloud or on-premises IT infrastructure?
The cost-effectiveness of the cloud versus on-premises infrastructure depends on various factors such as the size of the organization and workload demands, to name a few. The cloud offers cost savings in terms of upfront capital expenditure and ongoing maintenance, as organizations only pay for resources used. However, on-premises involves higher upfront costs but may result in greater long-term savings once the infrastructure is established.

Is the cloud less secure than on-premises?
Cloud providers implement robust security measures to protect data, including data encryption, access controls, and threat detection systems. However, organizations must also ensure proper configuration and adopt additional security measures to meet specific compliance requirements and protect their applications, data, and user access.

Which option is better for compliance and regulatory requirements?
Both cloud and on-premises infrastructures can be designed to meet compliance and regulatory requirements. Leading cloud service providers typically provide compliance certifications and attestations, whereas on-premises allows organizations to maintain full control over compliance by implementing customized security controls and monitoring processes.

Can I have a mix of cloud and on-premises infrastructure?
Yes, organizations can adopt a hybrid cloud approach that combines elements of both cloud and on-premises infrastructure. The hybrid cloud model allows organizations to leverage the scalability and flexibility of the cloud while maintaining sensitive data or critical applications on-premises.

What are some typical use cases for the cloud and on-premises infrastructure?
Cloud computing is suitable for startups and small businesses, organizations with fluctuating workloads, and collaboration and remote work environments. On-premises infrastructures may be more ideal for organizations that are operating in highly regulated industries, heavily reliant on legacy systems, or handling highly sensitive data.

Meanwhile, the ever-growing value of big data is driving data scientists to increase their access along with their ability to mine and analyze data for insights and information by adapting how data repositories are managed in relation to the type of data they house. While some legacy and on-premises solutions continue to be indispensable, a mass shift to the cloud is proving to be the answer to many of the problems organizations are facing in regards to data volume, compatibility, and accessibility.

Companies of various sizes and industries adapt to progress at different rates and may migrate data for different reasons. The five major trends in data migration in 2023 reflect the industry’s attitude as a whole toward solving specific problems.

1. A Shift Towards Data Lakehouses

Data lakehouses are open data management architectures that combine the flexibility, cost-efficiency, and scale of data lakes with the data management abilities of data warehouses. The result is a unified platform used for the storage, processing, and analysis of both structured and unstructured data. One reason this approach is gaining popularity is a sustained desire to break down data silos, improve quality, and accelerate data-driven decision-making within organizations.

Data lakehouses’ large capacity enables them to handle large volumes of data in real time, making them ideal for live consumer data, Internet of Things (IoT) networks, and physical sensors. Their ability to process data from multiple sources makes it easier for organizations to gain insights from multiple data streams.

Additionally, the centralization of data lakehouses allows for a unified, up-to-date view of data across an entire organization, facilitating inter-departmental collaboration on data-based projects and greatly reducing the costs and complexity of hosting multiple data storage and processing solutions.

2. A Focus on AI and Automation in Governance

Data migration helps organizations keep pace by ensuring their systems are able to accommodate the ever-increasing flow of new data. To simplify the already complex and time-consuming task of data governance, many companies are turning to artificial intelligence (AI)/machine learning (ML) algorithms and automation.

These technologies have revolutionized data migration by allowing organizations and data managers to automate some of the many manual processes it involves. It also enables them to reduce the risk of failures due to human error and execute the migration process more accurately and efficiently. With the help of smart algorithms, organizations can also better gain insights into their data than previously possible while identifying and eliminating data duplicates, which may reduce storage costs and improve performance.

Thanks to the recent boom in AI and ML-based technologies being developed and partially launched by a number of cloud computing giants, including Microsoft and Google, the role of such technologies in the more critical processes of data migration is likely to increase as the models become more and more sophisticated.

3. Expanding Storage Capacity

The world is expected to generate around 120 zettabytes of data in 2023, a nearly 24 percent increase from the prior year. This data is generated from a wide variety of sources, including IoT devices, log files, and marketing research. In this case, bigger is better—many organizations are looking to embrace big data by expanding storage capacities through novel methods of data storage.

One prominent option is cloud storage, which stands out as a scalable, reliable solution that’s also easily accessible over the internet. However, one of the challenges that arises with data migration to the cloud is maintaining security during transit. Organizations must carefully plan their migration strategies—including encryption, backup, and recovery plans—to protect financial and medical data and personal information while it is at risk.

Organizations can also benefit from an increase in agility and compounded value of structured and unstructured data by expanding their overall data storage capacity through flexible and scalable means.

4. Handling Unstructured Data

Most data sources produce semi-structured or unstructured data that cannot be easily organized and categorized. Company mergers and system updates are prominent sources of unstructured data—the initial categorization and structure of the data must be shed in order to fit into a different system. Unstructured data tends to be much larger in volume than structured data carrying the same amount of information and insights.

This poses a problem when migrating data. Not only is the massive volume costly to transfer and secure, both in-transit and at-rest, but it cannot be analyzed or stored in relational databases. However, that doesn’t make it void of value, as many organizations are seeking data science and migration solutions that would help structure incoming data.

Solving the unstructured data problem is a time-sensitive endeavor for many organizations. That’s because situational data quickly loses its value with time and gets replaced by more recent data, often in greater volume.

5. A Move From On-Premises Legacy Systems to Cloud Storage

Most data originates in the cloud, from such sources as digital logs, monitoring devices, customer transactions, and IoT devices and sensors. Many organizations are finding it more efficient to migrate entirely to the cloud rather than remaining split between legacy on-premises systems and cloud storage.

This approach would involve the integration of legacy data and systems with already-present data stored in the cloud, creating a more unified and comprehensive approach to data management and enabling remote access. A move to the cloud would also be accompanied by embracing multi-cloud architectures, allowing companies to optimize costs by working and switching between multiple cloud providers simultaneously.

Moving entirely to the cloud would also facilitate data storage segmentation, enabling data managers to differentiate data by type, purpose, and origin in addition to sensitivity and the level of security it may require. Organizations with data split between legacy and cloud systems may seek to unify the multiple sources in the cloud, enabling them to develop a richer, more holistic view of their data and how they might be able to use it.

Predictions for the Future of Data Migration

Data migration is expected to continue to grow in popularity alongside the exponential growth in the average volume of data produced annually by organizations. As businesses increasingly adopt cloud-based alternatives to everything from computing and processing to hosting software, cloud-based data solutions are likely to follow.

This will spark a wave of innovation, creating modern tools and technologies that aim to simplify the data migration process, ensuring the security and reliability of data in transit. Combined with the latest advancements in AI, ML, and automation, the migration process is likely to become faster, more efficient, and less prone to errors, making data migration as a concept more accessible to startups and emerging businesses who want to shift to the cloud and make the most out of their data.

For businesses considering a data warehouse solution, a number of competing providers offer a range of features and prices. This article will compare the top seven solutions and explain the features that differentiate them, making it easier to match them to specific needs.

Table Of Contents

• Top Data Warehouse Providers And Solutions
• Data Warehouse Providers And Solutions Comparison Table
• Amazon Redshift: Best For Deployment Optimization
• Google BigQuery: Best For Serverless Data Warehousing
• IBM Db2 Warehouse: Best For Analytic Workloads
• Azure Synapse Analytics: Best For Code-Free Offering
• Oracle Autonomous Data Warehouse: Best For Integration
• SAP Datasphere: Best For Data Warehouse Templates
• Snowflake: Best For Cloud Data Warehouses
• Key Features Of Data Warehouse Providers And Solutions
• How To Choose What Data Warehouse Provider Best For You
• Frequently Asked Questions (FAQ)
• Bottom Line: Data Warehouse Providers And Solutions

Top Data Warehouse Providers and Solutions

The top seven providers all offer feature-rich data warehousing plans at varying prices. A business’s specific needs will determine which is right for them. When selecting a provider, consider the use cases and costs for each as outlined below.

Data Warehouse Providers And Solutions Comparison Table

Amazon Redshift: Best For Deployment Options

With Amazon’s entry into the cloud data warehouse market, Redshift is an ideal solution for those organizations that have already invested in AWS tooling and deployment. Redshift deploys with Software as a Service (SaaS), cloud, and web-based solutions.

Pricing

Amazon Redshift has a pricing page where users can sign up for a trial period, request a quote, or calculate costs based on needs. Pricing starts at $0.25 an hour and can be configured using various models based on usage.

Features

• Spectrum Feature: This feature allows organizations to directly connect with data stores in the AWS S3 cloud data storage service, reducing startup time and cost.
• Strong Performance: The performance benefits companies from AWS infrastructure and large parallel processing data warehouse architecture for distributed queries and data analysis.
• Integration With AWS Glue: AWS Glue makes it easy to write or autogenerate Extract, Transform, and Load (ETL) scripts in addition to testing and running them.

See all Redshift features at https://aws.amazon.com/redshift/features.

Pros

• Parallel processing capabilities
• Contains network isolation security
• Good documentation

Cons

• Expensive
• Poorly designed user interface
• Unable to restrict duplicate records

For more on AWS: AWS Data Portfolio Review

Google BigQuery: Best For Serverless Technology

Google BigQuery is a reasonable choice for users looking to use standard SQL queries to analyze large data sets in the cloud. It is a serverless enterprise data warehouse that uses cloud, scale, Machine Learning (ML)/Artificial Intelligence (AI), and Business Intelligence (BI).

Pricing

Google BigQuery’s pricing page contains specific information about pay-as-you-go plans and longer-term (one to three year) commitments. The provider offers multiple versions of the platform, including Enterprise Edition and Enterprise Plus Edition. The Standard Edition is a pay-as-you-go plan starting at $0.04 per slot hour and the Enterprise Edition has different plans to help a company find its cloud data warehouse.

Features

• Serverless Technology: Using serverless technology, Google handles the functions of a fully managed cloud service, data warehouse setup, and resource provisioning.
• Logical Data Warehousing Capabilities: BigQuery lets users connect with other data sources, including databases and spreadsheets to analyze data.
• Integration With BigQuery ML: With BigQuery ML machine learning, workloads can be trained on data in a data warehouse.

See all BigQuery features at https://cloud.google.com/bigquery.

Pros

• Works with Google Cloud
• Full SQL query support
• Efficient management of data

Cons

• No user support
• Difficult for beginners in data warehouses
• Difficult user interface

For more information on Google: Google Data Portfolio Review

IBM Db2 Warehouse: Best For Analytic Workloads

IBM Db2 Warehouse is a strong option for organizations handling analytics workloads that can benefit from the platform’s integrated in-memory database engine and Apache Spark analytics engine.

Pricing

IBM offers a free trial for IBM Db2 Warehouse and provides a pricing page where users can ask for a quote and estimate the cost. For the flex one plan, the pricing is $1.23 per instance-hour, $0.99 per VPC-hour, and $850 per a service endpoint dedicated connectivity.

For more information, go to IBM’s pricing page.

Features

• Helpful Integration: IBM Db2 Warehouse integrates an in-memory, columnar database engine, which can be a big benefit for organizations looking for a data warehouse that includes a high-performance database.
• Netezza Technology: Db2 Warehouse benefits from IBM’s Netezza technology with advanced data lookup capabilities.
• Cloud Deployment And On-Premises: Deployment can be done in either IBM cloud or in AWS, and there is also an on-premises version of Db2 Warehouse, which can be useful for organizations that have hybrid cloud deployment needs.

See all Db2 Warehouse features at https://www.ibm.com/products/db2/warehouse.

Pros

• Includes in-memory columnar database
• Cloud deployment options
• Configuration flexibility

Cons

• Expensive
• Limited references online
• Limited buffer pool commands

For more on IBM: IBM: Hybrid Cloud Portfolio Review

Azure Synapse Analytics: Best For Code-Free Offerings

Azure Synapse Analytics, previously known as Azure SQL Data Warehouse, is well suited for organizations of any size looking for an easy on-ramp into cloud-based data warehouse technology, thanks to its integration with Microsoft SQL Server.

Pricing

Azure Synapse Analytics’s pricing page allows customers to request a quote or explore pricing options. For tier one, Azure offers 5,000 units for $4,700; tier two offers 10,000 units for $9,200. For other tier options, refer to the pricing page.

Features

• Dynamic Data Masking (DDM): Azure Synapse Analytics provides a granular level of security control, enabling sensitive data to be hidden on the fly as queries are made.
• Azure Integration: Existing Microsoft users will likely find the most benefit from Azure SQL Data Warehouse, with multiple integrations across the Microsoft Azure public cloud and more importantly, SQL Server for a database.
• Parallel Processing: In contrast to simply running SQL Server on-premises, Microsoft has built on a massively parallel processing architecture that can enable users to run over a hundred concurrent queries.

See more Azure Synapse Analytics features at https://learn.microsoft.com/en-us/azure/synapse-analytics/whats-new.

Pros

• Easy integration
• Some code-free offerings
• Strong data distribution

Cons

• Difficult logging metrics
• Limited diagramming tools
• Limited documentation

For more on Microsoft Azure: Microsoft Azure: Cloud Portfolio Review

Oracle Autonomous Data Warehouse: Best For Integration

For existing users of the Oracle database, the Oracle Autonomous Data Warehouse might be the easiest choice, offering a connected onramp into the cloud including the benefits of data marts, data warehouses, data lakes, and data lakehouses.

Pricing

Oracle’s Autonomous Data Warehouse’s main page offers pricing information as well as a cost estimator for users. The bottom price for Oracle Autonomous Data Warehouse shared and dedicated infrastructures is $0.25 per unit.

Features

• Works With Cloud And Hardware: A key differentiator for Oracle is that it runs the Autonomous Data Warehouse in an optimized cloud service with Oracle’s Exadata hardware systems, which has been purpose-built for the Oracle database.
• Easy Collaboration: The service integrates a web-based notebook and reporting services to share data analysis and enable easy collaboration.
• Strong Integration: While Oracle’s namesake database is supported, users can also migrate data from other databases and clouds, including Amazon Redshift, as well as on-premises object data stores.

See more features at https://www.oracle.com/autonomous-database/autonomous-data-warehouse/.

Pros

• Migration support for other database services
• Purpose-built hardware
• Fast query performance

Cons

• No on-premises solutions
• Limited data connection
• Complicated setup

For more on Oracle: Oracle Data Portfolio Review

SAP Datasphere: Best For Templates

Thanks to the pre-built templates it offers, SAP’s Datasphere might be a good fit for organizations looking for more of a turnkey approach to getting the full benefit of a data warehouse. SAP Datasphere allows data professionals to deliver scalable access to business data.

Pricing

SAP Datasphere’s pricing page lists a free tier and range of flexible pricing options based on needs. The price for capacity datasphere units is $1.06 per unit.

Features

• SAP’s HANA (High-performance Analytic Appliance): The cloud services and database are at the core of Data Warehouse Cloud, supplemented by best practices for data governance and integrated with a SQL query engine.
• Pre-Built Business Templates: Templates can help solve common data warehouse and analytics use cases for specific industries and lines of business.
• Integration with SAP Applications: SAP Datasphere integration means easier access to on-premises as well as cloud data sets.

See more features including a product demo at https://www.sap.com/products/technology-platform/datasphere.html.

Pros

• Inventory controls
• Extract data from multiple sources
• Strategic solutions

Cons

• Difficult for beginners
• Difficult integration
• Limited visual analytics

For more on SAP: SAP Data Portfolio Review

Snowflake: Best For Data Warehouse In The Cloud

Snowflake is a great option for organizations in any industry that want a choice of different public cloud providers for data warehouse capabilities. Snowflake aims to bring development to data, help companies govern data for users, and work globally and cross-cloud.

Pricing

Snowflake’s pricing page links to a quote page and offers a 30-day free trial with $400 of free usage.

Features

• Database Engine: Snowflake’s columnar database engine capability can handle both structured and semi-structured data, such as JSON and XML.
• Cloud Provider Of Choice: Snowflake architecture allows for compute and storage to scale separately, with data storage provided on the user’s cloud provider of choice.
• Virtual Data Warehouse: The system creates what Snowflake refers to as a virtual data warehouse, where different workloads share the same data but can run independently.

See more features at https://www.snowflake.com/en/.

Pros

• SQL-based queries for analytics
• Support for JSON and XML
• Integration with AWS, Azure, and GCP

Cons

• Limited data visualization
• Unable to create dynamic SQL
• Difficult documentation

For more information on Snowflake: Snowflake and the Enterprise Data Platform

Key Features of Data Warehouse Providers and Solutions

Cloud data warehouses typically include a database or pointers to a collection of databases where the production data is collected. Many modern cloud data warehouses also include some form of integrated query engine that enables users to search and analyze the data and assist with data mining.

Other key features to look for in a cloud data warehouse setup:

• Integration or API Libraries
• Data Quality and Compliance Tools
• ETL Tools
• Data Access Tools/Database Searchability
• SQL and NoSQL Data Capabilities

For more features and benefits: Top 10 Benefits of Data Warehousing: Is It Right for You?

How To Choose Which Data Warehouse Provider is Best for You

When looking to choose a cloud data warehouse service, there are several criteria to consider.

Existing Cloud Deployments. Each of the major public cloud providers has its data warehouse that provides integration with existing resources, which could make deployment and usage easier for cloud data warehouse users.

Ability to Migrate Data. Consider the different types of data the organization has and where it is stored. The ability to migrate data effectively into a new data warehouse is critically important.

Storage Options. While data warehouse solutions can be used to store data, having the ability to access commodity cloud storage services can provide lower-cost options.

Bottom Line: Data Warehousing Providers and Solutions

When considering providers and solutions of data warehousing, it’s important to weigh features and cost against your company’s primary goals, including deployment and analytic needs and cloud services.

While each provider and solution offers a variety of features, identifying a company’s own use case can help better evaluate them against a company’s needs.

For more information: 15 Best Data Warehouse Software & Tools

The need for enterprise-grade data storage has propelled the global public cloud market skyward. It is expected to almost double from $445 billion to $988 billion between 2022 and 2027. The richness and diversity of the market can make it daunting for organizations looking to upscale and upgrade their services.

Here’s a brief guide to some of the leading providers of public cloud solutions and how to choose the right provider for specific business needs.

Best Public Cloud Providers:

Amazon Web Services (AWS)

Amazon subsidiary Amazon Web Service (AWS) emerged in 2006, revolutionizing how organizations access cloud computing technology and remote resources. It offers a vast array of resources, allowing it to design and execute new solutions at a rapid pace to keep up with the global market’s evolution.

AWS’s services range from Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) to the simplified and easy-to-access and use, Software as a Service (SaaS) cloud models. Key offerings include:

Amazon EC2

Amazon Elastic Compute Cloud (EC2) is a web service that delivers secure and scalable computing capacity based in the cloud designed to facilitate web-centric computing for developers. This allows them to obtain and configure capacity with minimal friction with the infrastructure.

The services are available in a wide selection of instance types, from public to private and hybrid, that can be optimized to fit different use cases.

Amazon S3

Amazon Simple Storage Service (S3) is an object-based storage service known for its industry-leading scalability, security, performance and reliable data availability. Organizations of various sizes and industries can use it to store and retrieve any amount of data at any time, providing easy-to-use management features in order to organize data and configure it finely-tuned access control.

Amazon RDS

Amazon Relational Database Service (RDS) simplifies the setup and operations of relational databases in the cloud. AWS is responsible for automating all the redundant and time-consuming administrative tasks, such as hardware provisioning, database setup and data backup and recovery. This is best used to free up developers’ time, allowing them to focus on more pressing tasks like application development and design.

Use Cases and Industries

As a multinational corporation, AWS is able to cater to a wide variety of industries at different stages of development, from startups to established enterprises, as well as the public sector.

Use cases include:

• Application hosting
• Data processing
• Data warehousing
• Backup and restoration

This makes AWS’s service particularly useful for data-intensive industries such as healthcare, telecommunications, financial services, retail, and manufacturing.

Microsoft Azure

Microsoft launched Azure in 2010 as a comprehensive suite of cloud-based services designed to help businesses and organizations navigate the challenges that come with digital adoption. Azure was built on Microsoft’s decades-long specialty—software design—allowing its public cloud solutions to integrate seamlessly with other Microsoft products.

Azure also includes a multitude of services that range from computing and database management to storage and machine learning, including the following:

Azure Blob Storage

Azure Blob Storage is an object-based and scalable storage platform used for data lakes, warehouses and analytics as well as backup and recovery. It’s optimized for massive amounts of unstructured data, like text or binary values.

Azure Cosmos DB

Azure Cosmos DB is a database management service that’s multi-modeled, globally distributed and highly scalable, ensuring low latency that supports various APIs to facilitate access. It supports data models including SQL, MongoDB, Tables, Gremlin and Cassandra.

Azure Virtual Machines

Azure’s Virtual Machines are on-demand, scalable resources that provide users the flexibility of virtualization without the need to invest in or maintain the infrastructure that runs it. They also run on several Microsoft software platforms, supporting numerous Linux distributions for a more versatile experience.

Use Cases and Industries

When combined with Microsoft’s software and enterprise-focused approach to the public cloud, Microsoft Azure’s comprehensive services make it the ideal solution for numerous use cases, such as:

• Big data and analytics
• Application hosting
• Disaster and backup recovery
• IoT applications

Azure’s services are used by businesses and organizations in a number of industries such as e-commerce, healthcare, insurance and financial institutions.

Google Cloud Platform (GCP)

First launched in 2011 as a cloud-based subsidiary of Google, Google Cloud Platform (GCP) is a suite of cloud computing services that uses the same infrastructure as Google’s software products. Its industry-leading creations from TensorFlow and Kubernetes are some of the greatest examples of Google’s sophisticated solutions, and include the following:

Google Cloud Engine

Also known as Google Kubernetes Engine (GKE), Cloud Engine is a fully managed, user-ready environment used to deploy containerized applications and web services. Based on the open-source Kubernetes system, it’s developed by Google for managing workloads, enabling developers to flexibly and efficiently develop apps and deploy applications.

Google Cloud Storage

Google Cloud Storage is a fully managed and scalable object-oriented storage service. It includes many services ranging from serving website content to storing data for archival purposes and disaster recovery.

Google Compute Engine

Google Compute Engine is a cloud-based virtual machine solution that’s scalable and flexible. It allows users to tailor their computing environment, meeting specific requirements, and offering flexible pricing and cost savings.

Use Cases and Industries

GCP is used by organizations and businesses in IT, healthcare and retail, as well as the financial industry. Use cases include:

• Data analytics and machine learning
• Application development
• Storage and database management

IBM Cloud

IBM launched IBM Cloud in 2011 as a collection of cloud-based computing services. It leverages IBM’s vast experience, offering a robust approach to enterprise-grade public cloud platforms with an emphasis on open-source technologies and supporting a diverse set of computing models, including the following:

IBM Cloud Functions

IBM Cloud Functions is IBM’s Function as a Service (FaaS) solution built on Apache OpenWhisk. It enables developers to execute code in response to events as well as direct HTTP calls without having to manage their own hardware infrastructure.

IBM Cloud Virtual Servers

These flexible and scalable cloud computing solutions support both public and dedicated virtual servers. They’re the right balance of computing power to cost, allowing companies to deploy the servers globally and reach their customers.

IBM Cloud Databases

IBM Cloud Databases is a family of managed, public databases that support a wide variety of data models that include relational, key-value, document, and time-series applications.

Use Cases and Industries

IBM Cloud services a wide range of industries with its diverse offerings, such as IT and technology companies, healthcare organizations, financial institutions and retail providers, as well as the public sector. Use cases include:

• Public and hybrid cloud implementation
• Blockchain development
• Data analytics and management
• AI and machine learning

Oracle Cloud Infrastructure

The Oracle Cloud Infrastructure is a part of Oracle’s comprehensive cloud offering, first launched in 2012. The public cloud solution leverages Oracle’s long history in enterprise computing and data processing, enabling the company to provide robust, scalable and secure services, including the following:

Oracle Cloud Storage

Oracle Cloud Storage is a high-performance, scalable and reliable object storage service. It’s capable of storing an unlimited amount of data of any content type, including analytic data and rich content like images and video.

Oracle Cloud Compute

Oracle Cloud Compute encompasses a variety of cloud computing options set to meet the needs of small-scale applications to enterprise-grade workloads. It’s available as both bare metal and virtual machine instances, giving users a flexible, scalable environment for running applications.

Oracle Cloud Functions

Oracle’s Function as a Service (FaaS) offering lets developers write and deploy code without worrying about underlying infrastructure. It’s based on the open-source Fn Project and allows developers to build, run, and scale applications in a fully managed serverless environment.

Use Cases and Industries

With its versatile offerings, Oracle Cloud Infrastructure is able to serve a wide range of industries such as application development, insurance, healthcare and e-commerce in both the private and public sectors. Use cases include:

• High-performance computing (HPC)
• Enterprise resource planning (ERP)
• Data backup and recovery
• Data analytics

Alibaba Cloud

Launched in 2009, Alibaba Cloud is the cloud computing faction of the Alibaba Group. As the leading cloud provider in China and among the top global providers, Alibaba Cloud capitalizes on Alibaba’s massive scale and experience with e-commerce and data processing. Services include the following:

ApsaraDB

ApsaraDB is a suite of managed database services that cover a wide range of database types including relational, NoSQL and in-memory databases. These services handle database administration tasks, allowing developers to focus on their applications rather than database management.

Alibaba Object Storage Service

Alibaba Object Storage Service (OSS) is an easy-to-use service that enables users to store, backup and archive large amounts of data in the cloud. It is highly scalable, secure, and designed to store exabytes of data, making it ideal for big data scenarios.

Alibaba Elastic Compute Service

Alibaba Elastic Compute Service (ECS) provides fast memory and flexible cloud servers, allowing users to build reliable and efficient applications with ease. ECS instances come in a variety of types, each optimized for certain workloads, making them versatile for different application scenarios.

Use Cases and Industries

In essence, Alibaba Cloud’s extensive services, coupled with its strong presence in Asia, make it a compelling choice in the public cloud market. It also serves a multitude of data-heavy industries such as technology companies, media and entertainment, financial services and education. Use cases include:

• E-commerce platforms
• Big data analytics and processing
• AI and machine learning models

Emerging Public Cloud Providers

The booming market and demand for public cloud have opened the doors for numerous technology companies to start offering their own cloud computing and storage solutions. The focus of emerging cloud providers tends to be on providing straightforward, scalable, and affordable cloud services to small and midsize businesses, and key players in addition to the ones covered in this article include DigitalOcean, Linode and Vultr. All offer developer-friendly features at affordable rates alongside high-quality customer service and support.

Factors to Consider When Choosing a Public Cloud Provider

When choosing a provider of public cloud solutions, there are several factors to consider.

Scalability and performance

The cloud service provider must be able to handle workloads and be able to accommodate growth and changes as business grows.

Security

Providers must be compliant with local and federal data security and privacy regulations. Additionally, they should be able to protect data against attacks, leaks and breaches.

Pricing flexibility

Cloud services are most known for their flexible, pay-as-you-go pricing models. Multiple tiers at varying costs allow businesses to access only the resources they need.

Integration and customer service

A public cloud solution should be compatible with existing and legacy systems, ensuring seamless integration, and should include reliable customer support and service to ensure access to solutions and assistance.

Bottom Line: Public Cloud Providers

The public cloud market offers a diverse range of options, each with its own strengths and trade-offs. AWS, Microsoft Azure, GCP, IBM Cloud, Oracle Cloud Infrastructure and Alibaba Cloud are major players, each serving a multitude of industries with a broad array of services. Simultaneously, emerging providers offer compelling alternatives, especially for certain use cases or customer profiles.

When choosing a provider, considerations over scalability, performance, security, cost, integration and support are key. By understanding these factors, businesses can make informed decisions and choose the public cloud provider that best meets their specific needs.

As businesses discover new use cases and develop the infrastructure to support more IoT applications, the entire Internet of Things continues to evolve. Let’s look at some of the current trends in that evolution.

Table Of Contents

• Top 5 IoT Trends
  1. IoT Cybersecurity Concerns Grow
  2. IoT Advancements In Health Care
  3. 5G Enables More IoT Opportunities
  4. Demand For Specialized IoT Data Management
  5. Bundled IoT For The Enterprise Buyer
• The Future Of IoT
• Bottom Line: IoT Trends

Top 5 IoT Trends

IoT devices can help companies use their data in many ways, including generating, sharing and collecting data throughout their infrastructure. While some companies are leaping into IoT technology, others are more cautious, observing from the sidelines to learn from the experiences of those pioneering IoT.

When looking through these five key trends, keep in mind how IoT devices affect and interact with company infrastructure to solve problems.

1. IoT Cybersecurity Concerns Grow

As new IoT solutions develop quickly, are users being protected from cyber threats and their connected devices? Gabriel Aguiar Noury, robotics product manager at Canonical, which publishes the Ubuntu operating system, believes that as more people gain access to IoT devices and the attack surface grows, IoT companies themselves will need to take responsibility for cybersecurity efforts upfront.

“The IoT market is in a defining stage,” Noury said. “People have adopted more and more IoT devices and connected them to the internet.” At the same time they’re downloading mobile apps to control them while providing passwords and sensitive data without a clear understanding of where they will be stored and how they will be protected—and, in many cases, without even reading the terms and conditions.

“And even more importantly, they’re using devices without checking if they are getting security updates…,” Noury said. “People are not thinking enough about security risks, so it is up to the IoT companies themselves to take control of the situation.”

Ben Goodman, SVP of global business and corporate development at ForgeRock, an access management and identity cloud provider, thinks it’s important that we start thinking of IoT devices as citizens and hold them accountable for the same security and authorization requirements as humans.

“The evolution of IoT security is an increasingly important area to watch,” Goodman said. “Security can no longer be an afterthought prioritized somewhere after connectivity and analytics in the Internet of Things. Organizations need to start treating the ‘things’ in the Internet of Things as first-class citizens.”

Goodman said such a measure would mean that non-human entities are required to register and authenticate and have access granted and revoked, just like humans, helping to ensure oversight and control.

“Doing this for a thing is a unique challenge, because it can’t enter a username or password, answer timely questions, or think for itself,” he said. “However, it represents an incredible opportunity to build a secure network of non-human entities working together securely.”

For more information on IoT and security: Internet of Things (IoT) Security Trends

2. IoT Advancements In Healthcare

The healthcare industry has benefited directly from IoT advancements. Whether it’s support for at-home patient care, medical transportation, or pharmaceutical access, IoT solutions are assisting healthcare professionals with more direct care in situations where they cannot provide affordable or safe hands-on care.

Leon Godwin, principal cloud evangelist for EMEA at Sungard AS, a digital transformation and recovery company, explained that IoT not only makes healthcare more affordable—it also makes care and treatment more accessible and patient-oriented.

“IoT in healthcare will become more prevalent as healthcare providers look to reduce costs and drive better customer experience and engagement,” Godwin said. “This might include advanced sensors that can use light to measure blood pressure, which could be incorporated in watches, smartphones, or standalone devices or apps that can measure caloric intake from smartphone cameras.”

Godwin said that AI is also being used to analyze patient data, genetic information, and blood samples to create new drugs, and after the first experiment using drones to deliver organ transplants across cities happened successfully, rollout is expected more widely.

Jahangir Mohammed, founder and CEO of Twin Health, a digital twin company, thinks that one of the most significant breakthroughs for healthcare and IoT is the ability to constantly monitor health metrics outside of appointments and traditional medical tests.

“Recent innovations in IoT technology are enabling revolutionary advancements in healthcare,” Mohammed said. “Until now, individual health data has been mostly captured at points in time, such as during occasional physician visits or blood labs. As an industry, we lacked the ability to track continuous health data at the individual level at scale.

“Advancements in IoT are shifting this paradigm. Innovations in sensors now make it possible for valuable health information to be continuously collected from individuals.

Mohammed said advancements in AI and Machine Learning, such as digital twin technology and recurrent neural networks, make it possible to conduct real-time analysis and see cause-and-effect relationships within incredibly complex systems.

Neal Shah, CEO of CareYaya, an elder care tech startup, cited a more specific use case for IoT as it relates to supporting elders living at home—a group that suffered from isolation and lack of support during the pandemic.

“I see a lot of trends emerging in IoT innovation for the elderly to live longer at home and avoid institutionalization into a nursing home or assisted living facility,” Shah said. Through research partnerships with university biomedical engineering programs, CareYaya is field testing IoT sensors and devices that help with everything from fall prevention to medication reminders, biometric monitoring of heart rate and blood pressure—even mental health and depression early warning systems through observing trends in wake-up times.

Shah said such IoT innovations will improve safety and monitoring and make it possible for more of the vulnerable elderly population to remain in their own homes instead of moving into assisted living.

For more information on health care in IoT: The Internet of Things (IoT) in Health Care

3. 5G Enables More IoT Opportunities

5G connectivity will make more widespread IoT access possible. Currently, cellular companies and other enterprises are working to make 5G technology available in more areas to support further IoT development.

Bjorn Andersson, senior director of global IoT marketing at Hitachi Vantara, a top-performing IoT and  IT service management company, explained why the next wave of wider 5G access will make all the difference for new IoT use cases and efficiencies.

“With commercial 5G networks already live worldwide, the next wave of 5G expansion will allow organizations to digitize with more mobility, flexibility, reliability, and security,” Andersson said. “Manufacturing plants today must often hardwire all their machines, as Wi-Fi lacks the necessary reliability, bandwidth, or security.”

But 5G delivers the best of two worlds, he said—the flexibility of wireless with the reliability, performance, and security of wired networks. 5G provides enough bandwidth and low latency to have a more flexible impact than a wired network, enabling a whole new set of use cases.

Andersson said 5G will increase the feasibility of distributing massive numbers of small devices that in the aggregate provide enormous value with each bit of data.

“This capacity to rapidly support new apps is happening so early in the deployment cycle that new technologies and infrastructure deployment can happen almost immediately, rather than after decades of soaking it in,” he said. “With its widespread applicability, it will be feasible to deliver 5G even to rural areas and remote facilities far more quickly than with previous Gs.”

For more: Internet of Things (IoT) Software Trends

4. Demand For Specialized IoT Data Management

With its real-time collection of thousands of data points, the IoT solutions strategy focuses heavily on managing metadata about products and services. But the overwhelming amount of data involved means not all IoT developers and users have begun to fully optimize the data they can now access.

Sam Dillard, senior product manager of IoT and edge at InfluxData, a data platform provider for IoT and in-depth analytics use cases, believes that as connected IoT devices expand globally, tech companies will need to find smarter ways to store, manage and analyze the data produced by the Internet of Things.

“All IoT devices generate time-stamped (or time series) data,” Dillard said. “The explosion of this type of data, fueled by the need for more analytics, has accelerated the demand for specialized IoT platforms.”

By 2025, around 60 billion connected devices are projected to be deployed worldwide—the vast majority of which will be connected to IoT platforms, he said. Organizations will have to figure out ways to store the data and make it all sync together seamlessly as IoT deployments continue to scale at a rapid pace.

5. Bundled IoT For The Enterprise Buyer

While the average enterprise buyer might be interested in investing in IoT technology, the initial learning curve can be challenging as IoT developers work to perfect new use cases for users.

Andrew De La Torre, group VP of technology for Oracle Communications at cloud and data management company Oracle, believes that the next big wave of IoT adoption will be in bundled IoT or off-the-shelf IoT solutions that offer user-friendly operational functions and embedded analytics.

Results of a survey of 800 respondents revealed an evolution of priorities in IoT adoption across industries, De La Torre said—most notably, that enterprises are investing in off-the-shelf IoT solutions with a strong desire for connectivity and analytics capabilities built-in.

Because of specific capabilities, commercial off-the-shelf products can extend IoT into other industries thanks to its availability in public marketplaces. When off-the-shelf IoT aligns with industrial needs, it can replace certain components and systems used for general-use practices.

While off-the-shelf IoT is helpful to many companies, there are still risks as it develops—security risks include solution integration, remote accessibility and widespread deployments and usage. Companies using off-the-shelf products should improve security by ensuring that systems are properly integrated, running security assessments, and implementing policies and procedures for acquisitions.

The Future Of IoT

Customer demand changes constantly. IoT services need to develop at the same pace.

Here’s what experts expect the future of Iot development to look like:

Sustainability and IoT

Companies must embrace IoT and its insights so they can pivot to more sustainable practices, using resources responsibly and organizing processes to reduce waste.

There are multiple ways a company can contribute to sustainability in IoT:

• Smart energy management: Using granular IoT sensor data to allow equipment control can eliminate office HVAC system waste and benefit companies financially and with better sustainability practices.
• Extent use style: Using predictive maintenance with IoT can extend the lifespan of a company’s model of manufacturing. IoT will track what needs to be adjusted instead of creating a new model.
• Reusing company assets: Improved IoT information will help a company determine whether it needs a new product by looking at the condition of the assets and use history.

IoT and AI

The combination of Artificial Intelligence (AI) and IoT can cause industries, businesses and economies to function in different ways than either IoT or AI function on their own. The combination of AI and IoT creates machines that have smart behaviors and supports strong decision-making processes.

While IoT deals with devices interacting through the internet, AI works with Machine Learning (ML) to help devices learn from their data.

AI IoT succeeds in the following implementations:

• Managing, analyzing, and obtaining helpful insights from customer data
• Offering quick and accurate analysis
• Adding personalization with data privacy
• Providing assistance to use security against cyber attacks

More Use of IoT in Industries

Healthcare is cited as one of the top IoT industries, but many others are discovering how IoT can benefit their companies.

Agriculture

IoT can be used by farmers to help make informed decisions using agriculture drones to map, image, and survey their farms along with greenhouse automation, monitoring of climate conditions, and cattle monitoring.

IoT enables agriculture companies to have more control over their internal processes while lowering production risks and costs. This will reduce food waste and improve product distribution.

Energy

IoT in the energy sector can improve business performance and customer satisfaction. There are many IoT benefits for energy industry, especially in the following areas:

• Remote monitoring and managing
• Process optimization
• Workload forecasting
• Grid balancing
• Better decision-making

Finance

Banks and customers have become familiar with managing transactions through many connected devices. Because the amount of data transferred and collected is extensive, financial businesses now have the ability to measure risk accurately using IoT.

Banks will start using sensors and data analytics to collect information about customers and offer personalized services based on their activity patterns. Banks will then better understand how their customers handle their money.

Manufacturing

Manufacturing organizations gather data at most stages of the manufacturing process, from product and process assistance through planning, assembly and maintenance.

The IoT applications in the manufacturing industry include:

• Production monitoring: With IoT services’ ability to monitor data patterns, IoT monitoring provides optimization, waste reduction and less mundane work in process inventory.
• Remote equipment management: Remote work has grown in popularity, and IoT services allow tracking and maintaining of equipment’s performance.
• Maintenance notifications: IoT services help optimize machine availability by receiving maintenance notifications when necessary.
• Supply chains: IoT solutions can help manufacturing companies track vehicles and assets, improving manufacturing and supply chain efficiency.

For more industries using IoT: IoT in Smart Cities

Bottom Line: IoT Trends

IoT technology reflects current trends and reaches many areas including AI, security, healthcare, and other industries to improve their processes.

Acknowledging IoT in a business can help a company improve a company structure, and IoT will benefit a company’s infrastructure and applications.

For IoT devices: 85 Top IoT Devices

The cloud networking market has made it easier for companies to use intent-based networking, business intelligence (BI), configuration management, and services such as software-defined, cloud, edge, and networking solutions.

For more network trends: Top Network Segmentation Trends

Top 5 Cloud Networking Trends

1. Enterprise Network Strategy In The User’s Home

Changing workforce expectations have led many companies to a more globally distributed remote workforce – a trend that also rises with the cloud. 

As a result, enterprise networking infrastructure now has to support users in their homes.

Drit Suljoti, co-founder and CTO of Catchpoint, a digital experience monitoring platform provider, explained that consumer-grade networking technology does not always offer the levels of support and visibility necessary for remote work, which is increasingly becoming a problem.

“Organizations across the board have experienced the frustrations and performance volatility that can result from consumer-grade WiFi, VPN clients, and increased dependence on the internet from the employee’s wider household,” Suljoti said. “At the ground level, how can IT support desks ensure they have the necessary visibility into the daily digital life of their remote employees? 

“These mission-critical teams need the ability to understand the digital performance of an individual’s device, network, and applications, and the third-party providers they rely on. This is even more essential when employees are working remotely, without on-site support to troubleshoot performance issues.”

Bob Friday, VP and CTO of Mist, Juniper’s artificial intelligence (AI)-driven enterprise business, believes that many companies are starting to respond to this remote work shift by increasing networking security and monitoring their employees’ remote work environments.

“[A] major shift is in how enterprise-level networking trends are becoming increasingly important for personal users as well,” Friday said. “Whether you’re an executive at a company or you work in a profession that puts you into contact with sensitive information, the continued normalization of remote and hybrid work environments means that enterprise-grade networking and security will move into the home networking space.

“To ensure end-to-end network visibility, reliability, and security, we can expect enterprise-grade networking solutions to begin permeating remote and hybrid workforces, as enterprise IT teams take an even sharper look at their network edge.”

2. Networking With Remote AI Support

Users and enterprise devices often need technical support that was normally provided in the office. As remote work – again, supported by the cloud – continues to become a standard approach, many companies are adopting AI solutions to assist with customer experience (CX) and support requirements of the network.

“More help is needed in managing this critical infrastructure, which is why AI has become a necessity for network management,” said Friday. “Enterprises and technology providers have already adopted AI assistants in their networking support teams. Cloud AI has enabled a new tech support model, one that has created the volume and quality of data necessary to train AI technologies. 

“This AIOps model has led to incredible progress. At present, AI can answer up to 70% of support tickets with the same effectiveness as a domain expert. Eventually, this AIOps technology will move all the way to the end-user. 

“And like the average human employee, AI has the ability to learn and improve over time, thus providing a better customer experience consistently and proactively. But unlike the average human employee, that skill and expertise is not lost when they retire or quit. The more that AI is used as part of the IT help desk, the more the technology can improve its answers and, ultimately, the end-user experience.”

3. The Growth Of Intent-Based Networking (IBN)

Networking technology continues to grow more sophisticated. Particularly with the more widespread use of software-defined networking (SDN), intent-based networking is being used more in enterprise networks that want additional business intelligence (BI), configuration management, and other features embedded in their networks. All of these feature are part of the growing sophistication of cloud technology. 

Eric McGee, senior network engineer at TRG Datacenters, a data center vendor, explained why IBN is helpful to network administrators who want to better understand and manage their networks.

“One important networking technology trend that network engineers need to take note of is the emergence of intent-based networking,” McGee said. “The main role of IBN is to capture business intent and apply these insights across the network, ensuring that network administration is aligned with business intent. In other words, the IBN framework will receive an intent from the business and translate it, or encode it into the configuration of the network, resulting in the desired changes. Now, the network infrastructure is aligned with the business’s current needs.

“IBN also enables the automation of network administrative tasks involved, such as the configuration of networks, mitigation of risks, as well as the reporting and solving of network issues. Implementing IBN as a form of network administration makes the process of creating, managing, implementing, and monitoring network policies easier, simpler, and less labor-intensive. A lot of the manual effort put into traditional configuration management is made redundant when IBN is implemented.”

4. Holistic Networking Offerings

Traditional networking solutions typically need a variety of hardware and software components to work properly. 

However, as networks continue to evolve their software-defined, cloud, edge, and solutions, many networking vendors are offering more holistic networking packages to manage every aspect of the network.

Patrick MeLampy, Juniper Fellow at Juniper Networks, a top global networking company, believes that enterprise client-to-cloud connectivity is one of the biggest drivers behind more unified networking packages.

“I’d have to say that there are a few key networking trends that are gaining steam,” MeLampy said. “Enterprise client-to-cloud connectivity service offerings will take off. This means we’ll see Wi-Fi, wired, routing, and security capabilities pulled together, all in one simple offering, making it more efficient and effective for teams to manage ever-expanding networks.”

For more on cloud networking: The Cloud Networking Market

5. Managing Network Data With Different Ops Methodologies

With more software- and cloud-based networking solutions used across the board, several companies are looking into new ways to manage and read their networking data.

Richard Larkin, manager of North America sales engineering at NetBrain, a next-gen network operations company, believes that the knowledge and approach of different ops teams are particularly applicable to new ways of automating network data management. 

“The days of managing networks with SNMP polling and traps as well as Syslog data are almost over,” Larkin said. “Many enterprises still leverage these telemetry sources, but it’s not enough. We need a more comprehensive solution harvesting data, from API, CLI, packet, netflow, and other sources, to get the complete picture as well as visibility into SD-WAN, SDN, cloud, and SaaS offerings.

“A trend that I am seeing is the blending and blurring of lines between NetOps, SecOps, and DevOps. With networks becoming more software-defined and cloud-based, organizations are trying to fill the gap of the traditional network monitoring data (SNMP, Syslog, etc.) with homegrown solutions using Python, Ansible, and other coding. What would be interesting is if there was an easier way to codify the knowledge of the NetOps teams that required minimal coding and can be produced in minutes, not hours, days, and weeks.”

For more on networking management: The Network Management Market

The Future Of Cloud Networking

With the vitality in cloud networking for businesses, these trends above will further develop in the future, offering more opportunities for the growing market. From automation and network efficiency, businesses will see more benefits than ever.

Looking ahead, the future developments in cloud networking may include:

• Networking automation: Using network automation will help a company with a variety of tasks, including configuring, provisioning, managing, and testing network devices.
• Network-as-a-Service (NaaS): NaaS is a cloud model that allows users to control their network and attain the performance they expect from it without having to own, build, or maintain their infrastructure.
• 5G Cellular: 5G, the latest cellular update, allows a new network designed to connect virtually, including machines, devices, and more.
• Wi-Fi 6: Wi-Fi 6 is the new release for Wi-Fi network protocol that can be faster than its predecessors due to more focus on traffic and other technologies.
• Network Efficiency: With improved network scalability in the next couple of years, traffic will be aggregated for IP and Ethernet platforms. 
• Universal Networks: In the future, networking will have the ability to add new protocols and functions for better service. This can include services such as Ethernet services, mobile services, and more.

Along with the listed predictions and processes, more technologies are developing in networking, including AI, ML, the cloud, edge computing, Internet of Things (IoT), and more as they continue to play an increasingly important role in the future of networking. 

Bottom Line: Top Cloud Networking Trends

With remote training becoming a necessity in businesses, networking can help manage workers at home with a network strategy and remote AI support – a trend that leverages cloud networking to a great extent. 

Companies can use tools such as software-defined networking (SDN), intent-based networking, business intelligence (BI), and configuration management through their networking infrastructure.

Networking used to be based on hardware-defined networking, increasingly also offers services such as software-defined, cloud, edge, and networking solutions. 

For more information: Top 10 Enterprise Networking Companies

IoT professionals looking to advance their careers must prove they have the necessary knowledge and abilities and a certificate can help grow a person’s knowledge.

Table of Content:

• Top 8 Internet of Things Certifications
  • CCC Internet Of Things Foundation Certification: Best For Cloud IoT
  • CertNexus Certified Internet Of Things Practitioner: Best For Vendor-Neutral Learning
  • Microsoft Certified Azure IoT Developer: Best For Azure Users
  • Arcitura Certified IoT Architect: Best For Beginners
  • Global Tech Council Certified IoT Expert: Best For Programmers
  • AWS Internet Of Things Foundation Series: Best For Price
  • Stanford Internet Of Things Graduate Certificate: Best For Experts
  • hIOTron’s End-To-End IoT Certification Course: Best For Job Hunting
• Why Should You Get An IoT Certification?
• Bottom Line: Internet of Things Certifications

For more on IoT platforms: Best IoT Platforms & Software

Top 8 Internet of Things Certifications

IoT certifications can provide that proof that a student has the education in IoT for future jobs or improvement with how a company uses IoT.

Here are eight that could help workers impress employers:

1. CCC Internet Of Things Foundation Certification: Best For Cloud IoT

The Cloud Credential Council (CCC) offers one of the most comprehensive, vendor-neutral IoT certifications. The Internet of Things Foundation (IoTF) certification covers six learning modules, including IoT security and governance, architecture, and business use cases. According to the CCC, ideal participants include software engineers, system administrators, and IT architects.

Skills Acquired

The certification can teach many skills based on the path a student decides to use.

This includes:

• Define concepts and terminologies of IoT.
• Examine new devices and interfaces that are driving IoT growth.
• Relate to business perspectives of IoT (advantages of early adoption of IoT technologies).
• Predict the implications of IoT for your business.
• Examine the role of enabling technologies for IoT, such as cloud computing and Big Data.
• Identify security and governance issues with IoT.
• Examine future growth opportunities of IoT in the coming years.

Requirements

This course has no prerequisites, but participants should have a firm grasp of cloud-related concepts and terms.

Duration, Location, And Cost

Length of exam: 60 minutes, 25 questions.
Location: Webcam-proctored online only.
Cost: $349 (Study materials and voucher for exam).

For more on IoT Cloud: Internet of Things (IoT) Cloud Trends

2. CertNexus Certified Internet Of Things Practitioner: Best For Vendor-Neutral Learning

Another comprehensive, vendor-neutral certification is CertNexus’s Certified Internet of Things Practitioner. This course covers six topics, from constructing and programming IoT devices to processing data and identifying real-world use cases. It stands out because it’s accredited under the ANSI/ISO/IEC 17024 standard, a requirement for many government projects.

Skills Acquired

The certification can teach many skills based on the path a student decides to use.

This includes:

• Foundational knowledge.
• Implement IoT systems.
• Design IoT systems.
• Manage an IoT ecosystem.

Requirements

There are no prerequisites, but participants can take a readiness assessment to see if they have the recommended baseline skills and knowledge.

Duration, Location, And Cost

Length of exam: Two hours, 100 questions.
Location: In person at Pearson VUE test centers or online via Pearson OnVUE.
Cost: Exam $250, self-study $450, in-person classes up to $1,500.

3. Microsoft Certified Azure IoT Developer: Best for Azure Users

IoT professionals looking for vendor-specific options should consider Microsoft’s Certified Azure IoT Developer certification. It equips participants to develop, deploy and manage Azure IoT Edge applications. It focuses mainly on programming and implementation, ideal for workers who lead Azure-specific IoT teams.

Skills Acquired

The certification teaches many skills based on Azure IoT.

This includes:

• Set up the Azure IoT Hub solution infrastructure.
• Provision and manage devices.
• Implement IoT Edge.
• Implement business integration.
• Process and manage data.
• Monitor, troubleshoot, and optimize IoT solutions.
• Implement security.

Requirements

Candidates must be able to program in at least one Azure IoT SDK-supported language and understand device types and services.

Duration, Location, And Cost

Length of exam: ~Two hours.
Location: Proctored online (contact for more details).
Cost: Between $2,000-3,000; exam $165.

4. Arcitura Certified IoT Architect: Best For Beginners

Arcitura’s Certified IoT Architect certification includes three IoT courses, covering skills in IoT architecture, radio protocols, telemetry, and real-world use cases. After learning about these concepts in the first two courses, applicants will apply them in lab exercises in the third. Participants can take the exam without completing the coursework but may be unprepared if they skip it.

Skills Acquired

The certification can teach many skills based on the path a student decides to use.

This includes:

• Introduction of Internet of Things (IoT) concepts.
• Terminology and common models.
• IoT technology architecture and solution design.
• IoT communication protocols.
• Telemetry messaging.
• IoT architecture layers.

Requirements

There are no requirements for the certification.

Duration, Location, And Cost

Length of exam: 110 minutes.
Location: On-site Pearson VUE test centers.
Cost: $249.

5. Global Tech Council Certified IoT Expert: Best for Programmers

IoT professionals seeking a more flexible option may find the Global Tech Council’s Certified IoT Expert course appealing. The entirely self-guided course lasts eight hours in total, and lifetime access means applicants can take it at whatever pace they choose. By the end, participants will learn skills in IoT architecture, protocols, cloud and smart grid applications, Arduino and Raspberry Pi, and more.

Skills Acquired

The certification can teach many skills in IoT from software to key components.

This includes:

• IoT Key Components.
• IoT Layer Architecture.
• IoT Middleware.
• Communication and data link protocol.
• Layer protocols.
• IoT Cloud.
• Fog, Edge, and Grid Computing.
• IoT-aided Smart Grid System.
• Introduction to Arduino.
• Raspberry Pi Models.

Requirements

There are no formal prerequisites, but applicants should have basic programming and app development skills.

Duration, Location, And Cost

Length of exam: N/A.
Location: Online.
Cost: $199.

6. AWS Internet Of Things Foundation Series: Best For Price

Amazon Web Services (AWS) is one of the most popular networking service providers globally, so IoT professionals can gain much from understanding it. Consequently, working through AWS’s Internet of Things Foundation Series is an excellent choice for any IoT worker. Professionals can point toward the course as evidence they have experience in AWS IoT applications.

Skills Acquired

The AWS class can teach many skills in IoT.

This includes:

• Telemetry.
• IoT command and control.
• Fleet management.
• Predictive maintenance.

Requirements

Participants should likely have baseline IoT technical knowledge.

Duration, Location, And Cost

Length of class: 9.5 hours.
Location: On the AWS website.
Cost: Free.

For more on IoT: Internet of Things (IoT) Use Cases

7. Stanford Internet Of Things Graduate Certificate: Best For Experts

Another certification that stands out from the others is Stanford University’s Internet of Things Graduate Certificate. This is a graduate school-level program covering four non-credit online courses, and participants can pick from a list of 15. Applicants can show IoT experience from a leading engineering school after receiving a B or higher in the program. Specific takeaways will vary by course, but participants will generally learn about underlying IoT technologies, circuit design, web applications, security, and emerging tech.

Skills Acquired

The certification can teach many skills based on the path a student decides to use.

This includes:

• IoT technologies.
• Circuit design.
• Web applications.
• IoT security.
• Emerging tech.

Requirements

This certificate requires a bachelor’s degree with a GPA of at least 3.0 and advanced knowledge of programming languages.

Duration, Location, And Cost

Length of exam: Three-year course; exam N/A.
Location: Online.
Cost: $16,800-$21,000.

8. hIOTron’s End-To-End IoT Certification Course: Best For Job Hunting

hIOTron’s End-To-End IoT Certification Course is a certification that allows users to teach monitoring, analyzing, and IoT experience. Users will be certified by the course, ensuring that a user has a complete understanding of core IoT needs. This also includes IoT frameworks and architecture with practice for users.

Skills Acquired

The certification can teach many skills based on the path a student decides to use.

This includes:

• IoT device communication.
• IoT industry uses.
• Learn to build the first End-To-End IOT product using Rasp-berry pi devices.
• Hands-on practicals with IoT Gateway.
• Set up MQTT Broker and Node server.
• End-To-End IoT applications.

Requirements

There are no requirements for the certification.

Duration, Location, And Cost

Length of exam: N/A
Location: Online and classroom.
Cost: Upon request.

For more information on the IoT job market: 5 Trends in the Internet of Things (IoT) Job Market

Why Should You Get An IoT Certification?

IoT certifications can help a user demonstrate their understanding of IoT, such as architecture, management, and security. IoT may have not been included in a university course due to the technology being new for many developers. Understanding IoT helps a company’s employees as well as tech experts looking for a job.

Many jobs require at least baseline knowledge of IoT. Some jobs include:

• Data analyst (IoT).
• IoT developer.
• Chief developer.
• IoT application developer.
• Engineering IoT field application engineer.

Bottom Line: Internet of Things Certifications

IoT is a growing industry that is becoming more relevant in the tech field. Certification can help a user to advance, find a great career, and help with further education.

IoT certifications can seem very difficult, however, finding the best one can be easy as the topic grows and changes.

For more on IoT: The Internet of Things (IoT) Software Market

Particularly with innovations like the cloud, edge computing, Internet of Things (IoT) devices, and streaming, big data has become more prevalent for organizations that want to better understand their customers and operational potential. 

Big Data Trends: Table of Contents

• Real Time Analytics
  
• Stronger Reliance on Cloud Storage
  
• Ethical Customer Data Collection
• AI/ML Powered Automation
• Big Data in Different Industries
• Challenges in Big Data
• Bottom Line: Big Data Trends

Real Time Analytics

Real time big data analytics – data that streams moment by moment – is becoming more popular within businesses to help with large and diverse big data sets. This includes structured, semi-structured, and unstructured data from different sizes of data sets.

With real time big data analytics, a company can have faster decision-making, modeling, and predicting of future outcomes and business intelligence (BI). There are many benefits when it comes to real time analytics in businesses:

• Faster decision-making: Companies can access a large amount of data and analyze a variety of sources of data to receive insights and take needed action – fast.
• Cost reduction: Data processing and storage tools can help companies save costs in storing and analyzing data. 
• Operational efficiency: Quickly finding patterns and insights that help a company identify repeated data patterns more efficiently is a competitive advantage. 
• Improved data-driven market: Analyzing real time data from many devices and platforms empowers a company to be data-driven. Customer needs and potential risks can be discovered so they can create new products and services.

Big data analytics can help any company grow and change the way they do business for customers and employees.

For more on structured and unstructured data: Structured vs. Unstructured Data: Key Differences Explained

Stronger Reliance On Cloud Storage

Big data comes into organizations from many different directions, and with the growth of tech, such as streaming data, observational data, or data unrelated to transactions, big data storage capacity is an issue.

In most businesses, traditional on-premises data storage no longer suffices for the terabytes and petabytes of data flowing into the organization. Cloud and hybrid cloud solutions are increasingly being chosen for their simplified storage infrastructure and scalability.

Popular big data cloud storage tools:

• Amazon Web Services S3
• Microsoft Azure Data Lake
• Google Cloud Storage
• Oracle Cloud
• IBM Cloud
• Alibaba Cloud

With an increased reliance on cloud storage, companies have also started to implement other cloud-based solutions, such as cloud-hosted data warehouses and data lakes. 

For more on data warehousing: 15 Best Data Warehouse Software & Tools

Ethical Customer Data Collection 

Much of the increase in big data over the years has come in the form of consumer data or data that is constantly connected to consumers while they use tech such as streaming devices, IoT devices, and social media. 

Data regulations like GDPR require organizations to handle this personal data with care and compliance, but compliance becomes incredibly complicated when companies don’t know where their data is coming from or what sensitive data is stored in their systems. 

That’s why more companies are relying on software and best practices that emphasize ethical customer data collection.

It’s also important to note that many larger organizations that have historically collected and sold personal data are changing their approach, making consumer data less accessible and more expensive to purchase. 

Many smaller companies are now opting into first-party data sourcing, or collecting their own data, not only to ensure compliance with data laws and maintain data quality but also for cost savings.

AI/ML-Powered Automation

One of the most significant big data trends is using big data analytics to power AI/ML automation, both for consumer-facing needs and internal operations. 

Without the depth and breadth of big data, these automated tools would not have the training data necessary to replace human actions at an enterprise.

AI and ML solutions are exciting on their own, but the automation and workflow shortcuts that they enable are business game-changers. 

With the continued growth of big data input for AI/ML solutions, expect to see more predictive and real-time analytics possibilities in everything from workflow automation to customer service chatbots.

Big Data In Different Industries 

Different industries are picking up on big data and seeing many changes in how big data can help their businesses grow and change. From banking to healthcare, big data can help companies grow, change their technology, and provide for their data.

Banking

Banks must use big data for business and customer accounts to identify any cybersecurity risk that may happen. Big data also can help banks have location intelligence to manage and set goals for branch locations.

As big data develops, big data may become a basis for banks to use money more efficiently.

Agriculture

Agriculture is a large industry, and big data is vital within the industry. However, using the growing big data tools such as big data analytics can predict the weather and when it is best to plant or other agricultural situations for farmers.

Because agriculture is one of the most crucial industries, it’s important that big data support it, and it’s vital to help farmers in their processes. 

Real Estate And Property Management 

Understanding current property markets is necessary for anyone looking, selling, or renting a place to live. With big data, real estate firms can have better property analysis, better trends, and an understanding of customers and markets.

Property management companies are also utilizing their big data collected from their buildings to increase performance, find areas of concern, and help with maintenance processes.

Healthcare

Big data is one of the most important technologies within healthcare. Data needs to be collected from all patients to ensure they are receiving the care they need. This includes data on which medicine a patient should take, their vitals are and how they could change, and what a patient should consume. 

Going forward, data collection through devices will be able to help doctors understand their patients at an even deeper level, which can also help doctors save money and deliver better care.

Challenges in Big Data

With every helpful tool, there will be challenges for companies. While big data grows and changes, there are still challenges to solve.

Here are four challenges and how they can be solved:

Misunderstanding In Big Data

Companies and employees need to know how big data works. This includes storage, processing, key issues, and how a company plans to use the big data tools. Without clarity, properly using big data may not be possible.

Solutions: Big data training and workshops can help companies let their employees learn the ins and outs of how the company is using big data and how it benefits the company.

Data Growth

Storing data properly can be difficult, given how constantly data storehouses grow. This can include unstructured data that cannot be found in all databases. As data grows, it is important to know how to handle the data so the challenge can be fixed as soon as possible.

Solutions: Modern techniques, such as compression, tiering, and deduplication can help a company with large data sets. Using these techniques may help a company with growth and remove duplicate data and unwanted data.

Integrating Company Data

Data integration is necessary for analysis, reporting, and BI. These sources may contain social media pages, ERP applications, customer logs, financial reports, e-mails, presentations, and reports created by employees. This can be difficult to integrate, but it is possible.

Solutions: Integration is based on what tools are used for integration. Companies need to research and find the correct tools.

Lack Of Big Data Professionals

Data tools are growing and changing and often need a professional to handle them, including professionals with titles like data scientists, data analysts, and data engineers. However, some of these workers cannot keep up with the changes happening in the market.

Solutions: Investing money into a worker faced with difficulties in tech changes can fix this problem. Despite the expense, this can solve many problems with companies using big data.

Most challenges with big data can be solved with a company’s care and effort. The trends are growing to be more helpful for companies in need, and challenges will decrease as the technology grows. 

For more big data tools: Top 23 Big Data Companies: Which Are The Best?

Bottom Line: Growing Big Data Trends

Big data is changing continuously to help companies across all industries. Even with the challenges, big data trends will help companies as it grows.

Real time analytics, cloud storage, customer data collection, AI/ML automation, and big data across industries can dramatically help companies improve their big data tools.