Apache Beam Yaml: LOW-CODE DATA PIPELINES FOR BATCH AND STREAMING: Build ETL Workflows Without Programming Using Declarative Configuration, Runners, a
暫譯: Apache Beam YAML：批次與串流的低程式碼資料管線：使用宣告式設定與 Runners，無需程式設計即可建置 ETL 工作流程

Build real batch and streaming data pipelines using Apache Beam YAML, with declarative configuration you can run across runners and environments.

Many teams want reliable ETL workflows without turning every change into a programming project, but they still need correctness, portability, testing, and operational discipline. Beam YAML gives you a low-code way to define pipeline graphs, transforms, and runner options in a format that fits reviewable config and repeatable delivery.

This book shows you how to go from a first working YAML pipeline to production-ready patterns, including schemas as contracts, ingestion and transformation building blocks, joins and aggregations that stay correct, and the streaming fundamentals that protect you from late data surprises and runaway state.

• write your first batch pipeline in yaml, run it locally, and verify outputs
• use everyday yaml patterns for mapping filtering branching merging and parameterization
• treat schemas as contracts, control inference, declare output schemas, handle drift and nulls
• apply ingestion patterns for files events databases and warehouses, normalize inputs into a canonical schema
• build practical transformation graphs, enrichment lookups deduplication and quality gates
• design joins with strong key strategy, use sql in yaml where it is the right boundary, prevent skew driven failures
• create correct aggregations, avoid accidental fanout, validate results with reconciliation checks
• master streaming basics, event time watermarks windows triggers and allowed lateness
• implement reliability patterns, error outputs dead letter handling and safe reprocessing workflows
• test yaml pipelines with deterministic fixtures, contract tests, and ci gates to prevent regressions
• tune for performance and cost, diagnose fusion reshuffles skew and hot keys, manage state growth
• run on different runners, package dependencies for repeatable builds, validate portability with a runner matrix
• extend beam yaml with providers, catalogs, composite transforms, and cross language expansion services
• deliver to production with configuration separation orchestration observability and safe release practices
• use end to end reference pipelines for batch streaming and hybrid designs you can adapt

This guide includes working yaml pipeline examples and runner command patterns so you can move from configuration to running jobs, not just diagrams.

Grab your copy today and start shipping low-code Beam pipelines you can trust.