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Apache Beam - Unified Batch and Streaming Data Processing
A unified programming model for defining and executing data processing pipelines, supporting both batch and streaming modes across various execution engines like Apache Flink, Apache Spark, and Google Cloud Dataflow.
Java
Added on 2025年7月6日8,192
Stars4,366
ForksJava
LanguageProject Introduction
Summary
Apache Beam is an open-source project that provides a unified model for defining and executing data processing pipelines. It simplifies large-scale data processing by allowing developers to create pipelines that can run on any Beam-supported runner, abstracting away the complexities of the underlying execution engine.
Problem Solved
Developers often need to manage different codebases and APIs for batch versus streaming data processing, and face challenges in porting pipelines across different distributed execution systems. Apache Beam provides a single programming model to overcome these complexities.
Core Features
Unified API for Batch & Streaming
Design a single pipeline that can process data in both bounded (batch) and unbounded (streaming) modes.
Portability Across Execution Engines
Run your pipeline code on multiple distributed processing backends (runners) without modifying the code.
Advanced Windowing and Watermarks
Powerful abstractions for windowing event-time data and managing processing progress with watermarks.
Tech Stack
Java
Python
Go
Scala (via API)
Various Runner Dependencies (Flink, Spark, Dataflow, etc.)
Use Cases
Apache Beam's unified model makes it suitable for a wide variety of data processing scenarios, including:
Real-time Streaming Data Processing
Details
Process high-volume, low-latency data streams from sources like IoT devices, application logs, or financial feeds for real-time monitoring and analytics.
User Value
Enables immediate insights and responsive actions based on continuously arriving data.
Large-scale ETL and Data Integration
Details
Build complex ETL (Extract, Transform, Load) jobs to migrate, clean, and transform large datasets for data warehousing or business intelligence.
User Value
Provides a scalable and maintainable way to prepare data for analysis and reporting.
Vendor-agnostic Data Processing
Details
Develop data processing logic once and deploy it on different cloud providers (e.g., Google Cloud Dataflow, AWS EMR with Flink/Spark) or on-premise clusters based on cost, performance, or existing infrastructure.
User Value
Reduces vendor lock-in and increases flexibility in infrastructure choices.
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