Patterns for Big Data Processing

Big-data architecture choices are mostly about batch versus streaming, storage versus query flexibility, and how much operational duplication a team can carry. Java often sits in the middle as the implementation layer for ingestion, transformation, and processing jobs.

This section compares MapReduce, Lambda, Kappa, and storage-layer patterns so you can match data volume and latency needs to a workable Java-based design.

In this section

• MapReduce Pattern in Java
  Use MapReduce for large batch transformations when simple distributed stages are enough and lower-latency stream processing is unnecessary.
• Lambda Architecture for Java Big Data Systems
  Understand when a separate batch and streaming design still makes sense for Java data platforms, and when its operational duplication is too costly.
• Kappa Architecture in Java
  Use a streaming-first architecture in Java when one event pipeline is simpler and more reliable than separate batch and speed layers.
• Data Lakes vs. Data Warehouses for Java Data Systems
  Choose between data lakes and warehouses based on schema control, query patterns, governance, and how Java services produce or consume data.