Summary
Batch Processing is a technique used to handle and process large datasets efficiently. It works by breaking the data into smaller chunks and processing them together in a single batch.
Apache Spark is the leading technology for batch processing, offering scalable and distributed data processing. It can handle unmanageable data sizes by using parallelism and Distributed Computing
A key concept in batch processing is MapReduce:
- Map: Splits the data into smaller, manageable pieces for parallel processing.
- Reduce: Aggregates the processed data results from the individual tasks.
- Order: The order of Map and Reduce steps is flexible; the primary focus is on splitting and then aggregating data.
Batch processing is widely supported by cloud infrastructures like Amazon EMR and Databricks, which provide scalable environments for running batch jobs.