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 batch processing, real-time streaming, machine learning, and graph processing. Spark is known for its in-memory computing capabilities, which significantly speed up data processing compared to disk-based systems like Hadoop MapReduce.

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 analytics, ETL (Extract, Transform, Load) pipelines, or real-time data streaming applications, as it offers high performance and scalability for processing terabytes to petabytes of data. It is particularly useful in industries like finance, e-commerce, and healthcare for tasks such as fraud detection, recommendation systems, and log analysis, where fast data processing is critical.

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Apache Spark vs Apache Flink→Apache Spark vs Apache Hadoop→Apache Spark vs Apache Beam→

Learning Resources

Apache Spark Official Documentation

Databricks Academy: Spark Fundamentals

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 Machine Learning

Alternatives to Apache Spark

Apache Flink Apache Hadoop Apache Beam

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