Apache Spark is one of the world’s most popular—and powerful—engines for processing big data analytics. But as with any big data platform, its efficiency depends on the infrastructure underneath it. And in fact, 85% of big data projects fail. Using a data lake is part of an effective strategy for mitigating many of the hurdles that impact big data projects (storage, retrieval, consolidation, etc.), but that alone can’t necessarily cover processing and resource issues. When projects call for many parallel Spark jobs, complex operations can compete for resources. The result is crashing jobs or insights that arrive too slowly to be effective.
Thus, getting data into a single-sourced repository like a data lake is only half the big data equation. If tools can’t finish their designated tasks due to resource issues, it defeats the purpose of ingesting all that data. To that end, it’s clear that handling tasks like bursty workloads and dealing with excessive unstructured data needs a platform to manage and support such things while getting the most out of Apache Spark.