Tag: Distributed computing

Over 80% of the Fortune 500 companies use Apache Spark, an open-source, multi-language engine designed to execute data engineering, science, and machine learning on clusters or single-node machines. The core purpose of the Spark is to provide a fast, scalable, and unified platform for processing large-scale data workloads efficiently.

Apache Spark, one of the most active projects managed by the Apache Software Foundation, was developed to be 10 to 100 times faster than its predecessor, Hadoop MapReduce. But what is Spark and what does it do? This distributed processing system is essential for handling large-scale data workloads efficiently.

While Apache Spark is celebrated for its in-memory processing speed, its reliance on massive amounts of RAM can lead to significant cost and performance challenges. Understanding the full spectrum of Spark's limitations is crucial for organizations aiming to select the right big data processing tool for their specific needs.