Why Apache Spark is the Antidote to Multi-Vendor Data Processing

Why Apache Spark is the Antidote to Multi-Vendor Data Processing

By Punit Shah | Aug 29, 2018

The big data open source landscape has evolved.

Organizations today have access to a whole gamut of tools for processing massive amounts of data quickly and efficiently. Among multiple open source technologies that provide unmatched data processing capabilities, there’s one that stands out as the frontrunner − Apache SparkTM.

Apache Spark is gaining acceptance across enterprises due to its speed, iterative computing, and better data access. But for organizations grappling with multiple vendors for their data processing needs, the challenge is bigger. They’re not just looking for a highly capable data processing tool, they’re also looking for an antidote to multi-vendor data processing.

Spark provides several advantages over its competitors that include other leading big data technologies like Hadoop and Storm. Enterprises have successfully tested Apache Spark for its versatility and strengths as a distributed computing framework that can handle end-to-end needs for data processing, analytics, and machine learning workloads.

Let’s find out what makes Apache Spark the enterprise backbone for all types of data processing workloads.

Apache Spark is the Antidote to Multi-Vendor Data Processing

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