Speedata, whose first-of-its-kind Analytics Processing Unit (APU) accelerates big data analytic workloads across industries, announced the launch of its ‘Workload Analyzer.’ The browser-based performance predictor tool analyzes Spark log files to help data engineers learn how to maximize workload performance, both in the cloud and on-prem.
In an age of continuous exponential data growth without Moore’s law to keep infrastructure costs from skyrocketing, data engineers and analysts are increasingly bound to budgetary constraints that limit their ability to extract the full business value out of their data. Enterprises are utilizing various optimization tools to minimize costs associated with computing and improve efficiency via software optimizations, including file formats and engine rewrites. Nevertheless, the core issue remains unresolved.
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Speedata‘s Workload Analyzer tool analyzes log files, providing data engineers and platform administrators with valuable insights into the performance of their Spark queries. The tool demonstrates how an enterprise’s workload would perform in different environments, assisting engineers in determining the impact of certain infrastructure decisions such as deploying a faster network or adding more servers.
“Our team is committed to providing enterprises with the tools needed to accelerate their big data analytics workloads,” said Jonathan Friedmann, Co-Founder & CEO of Speedata. “The Workload Analyzer is one of those tools, helping businesses focus on what’s working and how to improve what’s not. It’s designed to help data engineers optimize their analytics with available infrastructure, set realistic goals, maximize their data, and maintain their competitive edge.”
The Workload Analyzer also demonstrates the performance improvement of an enterprise’s data analytics workload when running on Speedata‘s APU. The game-changing APU alleviates the main bottlenecks of data analytics, significantly improving workloads’ speed and performance, dramatically reducing costs, and increasing efficiency. Early prototypes of the APU are currently being sampled by multiple customers across different industries, including pharmaceuticals, finance, and more.
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