What are use cases for Spark vs Hadoop?

Apache Spark and Hadoop are both big data processing frameworks, but they have different design centers and use cases: *Hadoop:* 1. _Batch processing_: Hadoop is ideal for large-scale, batch-oriented data processing jobs, such as data warehousing, ETL, and data aggregation. 2. _Data archiving_: Hadoop's distributed file system (HDFS) is suitable for storing and managing large amounts of archival data. 3. _Data integration_: Hadoop can integrate data from various sources, formats, and structures. *Spark:* 1. _Real-time processing_: Spark is designed for real-time data processing, making it suitable for applications like streaming analytics, IoT, and machine learning. 2. _Iterative algorithms_: Spark's in-memory processing is ideal for iterative algorithms like machine learning, graph processing, and data mining. 3. _Interactive queries_: Spark's fast processing enables interactive queries and data exploration. 4. _Data science_: Spark's API and libraries (e.g., MLlib, Spark SQL) make it a popular choice for data science tasks. *Overlap:* 1. _Data processing_: Both Hadoop and Spark can process large datasets, but Spark is generally faster and more suitable for real-time processing. 2. _Data storage_: Both can store data, but Hadoop's HDFS is more scalable and suitable for archival data, while Spark's in-memory storage is better for real-time data. In summary: - Use Hadoop for batch-oriented, large-scale data processing, data archiving, and data integration. - Use Spark for real-time data processing, iterative algorithms, interactive queries, and data science tasks. Keep in mind that Spark can run on top of Hadoop, allowing you to leverage Hadoop's scalability and storage capabilities while using Spark for processing. read less