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


You may also be interested in…

 

blog

Low-code Application Development Can Drive Higher Apache Spark Adoption in the Enterprise

Apache Spark adoption is growing but the complexities remain Apache Spark has moved beyond the early-adopter phase and is now…

blog

Move away from batch ETL with next-gen Change Data Capture

As data volumes continue to grow, enterprises are constantly looking for ways to reduce processing time and expedite insight extraction….

Case Study

Real-Time Driver Profiling & Risk Assessment for Usage-based Insurance with StreamAnalytix

To keep up with the new digital consumer and remain competitive, the auto insurance industry is increasingly investing in connected…

Case Study

Hyper-Scale Data Processing and Storage Using StreamAnalytix

Enterprises generally need to compromise with running and maintaining multiple batch processes on the accumulated data due to throughput and…

White Paper

Integration of Cloudera Navigator Enables Data Governance with StreamAnalytix

Ensuring that the data is well managed, secure, and accessible are some of the critical requirements for organizations relying on…

webinar

Private: Simplify Spark-based ETL workflows on the cloud

Learn how you can visually design and manage Spark-based workflows by using StreamAnalytix on popular cloud platforms like AWS, Azure, and Databricks.

Start your free trial

of StreamAnalytix

TRY NOW

Download

StreamAnalytix Lite NOW

DOWNLOAD FOR FREE
Schedule a Demo