Apache Spark is best used when you need high-speed data processing and need to perform complex data transformations or machine learning jobs. It excels in scenarios where you benefit from its in-memory processing capabilities, such as iterative algorithms used in machine learning, where you repeatedly access the same data. For example, if you're building a real-time recommendation system that processes large datasets and requires quick computations, Spark's architecture allows you to handle this efficiently.
Apache Beam is ideal when you need to build a unified batch and streaming pipeline that can run on multiple platforms. For example, if you're developing a data pipeline that requires both batch processing of historical data and real-time processing of streaming data (like user activity tracking), using Beam allows you to write a single codebase that can run effectively in different environments.