One of our clients is a fast-growing design and manufacturing solutions provider. To meet their big data needs and constantly deliver SLAs, the company turned to Apache Spark. This approach worked…but over time, it became harder. As the company grew, their software data processing requirements did, too. Spark was great, but they needed a way to improve the performance of their Spark applications and ultimately, ensure their products and services met SLAs.
While Spark allowed the software company to work with large sets of big data and turn them into insights, their approach to Spark was unoptimized. This resulted in significant performance issues, lags, and downtime.
Their Spark problems intensified when the software vendor’s data processing requirements increased 10x over the course of 2020. Their compute consumption ballooned to significant levels, which adversely affected their budget. Each cluster was consuming two or three times the planned capacity.
The software provider turned to Pepperdata and gained superior visibility into their Spark framework and powerful optimization tools. The Pepperdata solution significantly improved their Spark performance and reduced their compute consumption.
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