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's in-memory computing capabilities make it significantly faster than traditional disk-based systems like Hadoop MapReduce for many workloads.
Developers should learn Apache Spark when working with big data analytics, ETL (Extract, Transform, Load) pipelines, or real-time data processing, as it excels at handling petabytes of data across distributed clusters efficiently. It is particularly useful for applications requiring iterative algorithms (e.g., machine learning), interactive queries, or stream processing, such as in financial analytics, IoT data analysis, or recommendation systems.