ESG Validation

Mike Leone

Mike Leone

ESG Senior Analyst Mike Leone covers converged and hyperconverged infrastructure, data platforms, data management, data analytics, and the emerging blockchain segment. Mike joined ESG as part of the validation team, where he provided third-party validation of claims made by vendors. In his analyst role, Mike draws upon his enthusiasm for bleeding edge technology as well as his engineering and marketing backgrounds to help IT vendors improve everything from product development and marketing to go-to-market strategies and roadmap adjustments.

Recent Posts by Mike Leone:

ESG Lab Review: Conveniently and Securely Archive to Microsoft Azure with HubStor


Cloud usage is becoming a mandate within organizations looking for ways to manage data growth and improve operational efficiency, while reducing costs. The challenge for organizations is understanding how to consume cloud storage because, by itself, the cloud is often viewed as just infrastructure or a platform-as-a-service. A software layer or software-as-a-service to make cloud storage consumable and relevant to an organization’s needs and ongoing strategy is missing. The market wants an easy way to not only consume cloud storage, but also to put data in or pull it out without being locked in.

Topics: Storage Data Platforms, Analytics, & AI Cloud Services & Orchestration

ESG Technical Review: Analyzing the Performance of MapR-DB, a NoSQL Database in the MapR Converged Data Platform


This ESG Technical Review documents and analyzes MapR-DB performance test results. Testing evaluated the performance and scalability of MapR-DB running in the cloud, and we compare results with other leading NoSQL database offerings.

Topics: Data Platforms, Analytics, & AI Cloud Services & Orchestration

ESG Lab Review: Creating a Consolidated, Highly Performing, Cost-efficient Infrastructure with ClearDB’s Aventra IRON Technology


This ESG Lab Report documents the validation of ClearDB’s Aventra IRON technology. ESG Lab leveraged a combination of guided demos, audited performance results, and ClearDB self-experienced quantitative cost savings to highlight the ease of deployment, as well as potential performance and cost efficiencies gained using Aventra IRON software.

Topics: Storage Cybersecurity Networking Converged Infrastructure Cloud Services & Orchestration

ESG Lab Review: Dell EMC Digital Platform for Enterprise Assets and Internet of Things


This ESG Lab Review looks at how businesses drive outcomes with the use of Dell Technologies and Dell EMC assets. The Dell EMC assets include a turnkey platform consisting of Native Hybrid Cloud and Analytic Insights Module as part of Dell EMC Converged platforms and solutions. ESG Lab observed how Dell Technologies can empower organizations to:

  • Define their approach to augmenting digital transformation with IoT (Internet of Things) and surveillance technologies with persona-driven use cases.
  • Blend video-based, visual verification with IoT and enterprise data to move beyond traditional surveillance and security investigation and enhance customer experiences, identify overhead, respond to incidents, and collect evidence.
  • Understand how to evaluate and extend an infrastructure for enterprise IoT—core, edge, and cloud—with data transparency and right-time intelligence.
  • Accelerate maturity progression from proof of value to production, including multi-data center, mission-critical deployment.
Topics: Storage Networking Data Platforms, Analytics, & AI

ESG Lab Review: Application-aware Management, Visibility, and Analytics in Virtualized Environments with Uila


This ESG Lab Report validates Uila’s full stack management, visibility, and analytic capabilities. Testing focused on fast root cause analysis through application visibility, complete infrastructure monitoring with application context, and end-user experience monitoring for improved application interactions.

Topics: Storage Networking Converged Infrastructure Cloud Services & Orchestration

ESG Lab Validation: Performance and Cost Efficiency of Intel and Microsoft Hyperconverged Infrastructure


This ESG Lab Validation documents our audit of synthetic and real-world testing of hyperconverged infrastructure (HCI) reference architectures (RAs) delivered by Intel and Microsoft. The solutions include Intel-based servers and flash drives with Windows Server 2016 and Storage Spaces Direct. Testing focused on performance, scalability, and cost effectiveness.

Topics: Converged Infrastructure

ESG Lab Review: Robin Systems: Container-based Virtualization for Databases and Data-centric applications


This ESG Lab Report highlights the recent testing of Robin Container-Based Virtualization Platform. Using a combination of guided demos and audited performance results, ESG Lab validated the ease of use, performance, scalability, and efficiency of Robin Systems’ container-based architecture.

Topics: Data Platforms, Analytics, & AI Cloud Services & Orchestration

ESG Lab Review: MapR Converged Data Platform with MapR Streams

The Challenges

Most organizations have an existing BI/analytics strategy. Many of these strategies consist of a mix of overlapping BI/analytics tools with a focus on batch processing—analyzing piles of data that have already been collected, stored, transformed, merged, cleansed, etc. Now that the Internet of Things is more than a buzz word, the early-adopters are starting to re-evaluate their BI/data analytics approaches. Their existing solutions are already overwhelmed with massive quantities of data and IoT will only add to the complexity. Whether it’s web applications tracking user clicks, sensors collecting weather data, or simply machine log data from within a single IT infrastructure, massive amounts of data are being generated every second and organizations are looking for any way possible to harness that data as soon as it’s collected.  As such, ESG research shows that 45% of organizations are planning to deploy a new BI/analytics solution over the next 24 months. And what is the top requirement for driving the evaluation process? The move to a more real-time analytics approach (Figure 1).[1] This same research also supports the idea that current BI/analytics solutions do not meet existing requirements and needs (26%) because new applications are generating new data types that need different analytical tools (27%).

Topics: Data Platforms, Analytics, & AI