Analytics & CDP

Apache Spark

Apache Spark is an open-source, distributed computing system designed for large-scale data processing. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance, supporting in-memory processing for faster analytics. Spark includes libraries for SQL, streaming, machine learning, and graph processing, making it a unified analytics engine for big data workloads.

Also known as: Spark, Apache Spark, Spark Framework, Spark Platform, Spark Core

🧊Why learn Apache Spark?

Developers should learn Apache Spark when working with big data applications that require fast, scalable processing of large datasets, such as real-time analytics, ETL pipelines, or machine learning tasks. It is particularly useful in scenarios where Hadoop MapReduce is too slow, as Spark's in-memory computing can be up to 100 times faster for iterative algorithms. Use cases include fraud detection, recommendation systems, and log analysis in industries like finance, e-commerce, and telecommunications.

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Compare Apache Spark

Apache Spark vs Apache Hadoop→Apache Spark vs Apache Flink→Apache Spark vs Apache Storm→

Learning Resources

Apache Spark Official Documentation

Databricks Spark Tutorial

Coursera: Big Data Analysis with Scala and Spark

YouTube: Apache Spark Tutorial for Beginners

Book: Learning Spark, 2nd Edition

Related Tools

Hadoop Scala Python Apache Kafka Apache Flink

Alternatives to Apache Spark

Apache Hadoop Apache Flink Apache Storm

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