Apache Spark is an open-source distributed computing system designed for big data processing and analytics. It was developed at the University of California, Berkeley's AMPLab in 2009 and later handed over to the [[Apache Software Foundation]], where it became an Apache top-level project in 2014. Spark provides a unified analytics engine that supports various data processing tasks such as batch processing, real-time streaming, machine learning, and graph processing. It is built on the concept of resilient distributed datasets (RDDs), which are fault-tolerant collections of objects that can be processed in parallel across a cluster of machines. One of the key features of Spark is its ability to cache data in memory, enabling faster iterative processing and interactive queries. This makes it much faster than traditional disk-based systems like Hadoop MapReduce. Spark also offers a rich set of libraries for various tasks, including Spark SQL for SQL queries, MLlib for machine learning, GraphX for graph processing, and Streaming for real-time data streaming. Spark can be used with different programming languages such as Scala, Java, Python, and R. It provides high-level APIs that make it easy to develop distributed applications without dealing with the complexities of distributed systems. Overall, Apache Spark has gained significant popularity in the big data community due to its speed, scalability, ease of use, and rich ecosystem of tools and libraries. It has been adopted by many organizations to process large-scale datasets and build advanced analytics applications.