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Dataflow ETL Pipeline

⬢ 層級 2技術
高
薪資影響
3 個月
學習時長
困難
難度
—
職業
一覽

Google Cloud Dataflow is a managed service for running Apache Beam pipelines at scale. Engineers define data transformations once (Python/Java), Dataflow executes them on GCP infrastructure (auto-scaling, fault tolerance). Beam is powerful: handles batch and streaming, windowing, state management. Senior practitioners earn 15-20% premium because they ship pipelines processing petabytes. Learning: 8-10 weeks (requires understanding of distributed computing, streaming, and GCP).

什麼是 Dataflow ETL Pipeline

Google Cloud Dataflow is Google's managed service for running Apache Beam pipelines at scale. Beam is a unified framework for batch and streaming data processing. Engineers write Python (or Java/Go) transformations once; Beam/Dataflow executes them on distributed infrastructure with auto-scaling, fault tolerance, and monitoring. Example: Events stream from Pub/Sub → Filter invalid events → Enrich with user data → Aggregate per minute (window) → Write to BigQuery. Dataflow scales from 1 event/sec to 1M events/sec automatically.

🔧 工具與生態
Apache BeamGoogle Cloud DataflowPython SDK (or Java/Go)Pub/Sub (streaming source)BigQuery (storage)Cloud Storage (source/sink)Windowing operatorsParDo transformsSide inputs and stateDataflow monitoring

📋 開始之前

Python

💰 各地區薪資

地區初級中級高級
USA$90,000$155,000$235,000
UK£55,000£95,000£145,000
EU€62,000€102,000€157,000
CANADACA$85,000CA$150,000CA$225,000

⚖ 對比

Apache Spark

❓ 常見問題

What's the difference between Beam batch and streaming?
Batch processes bounded data (file, fixed dataset). Streaming processes unbounded data (continuous stream). Beam code is almost identical; runner differs (DirectRunner for dev, Dataflow for prod).
Should I use Dataflow or Dataproc (Spark)?
Dataflow for continuous pipelines, streaming. Dataproc for batch, interactive. Dataflow auto-scales, more serverless. Dataproc more familiar if you know Spark.
What's a window in Beam?
A way to group streaming data into chunks. Fixed window (5-min buckets), sliding window (1-min window every 30s), session window (group until gap). Enables aggregations on infinite streams.
How do I handle state in Beam?
Beam has stateful processing (remember past events). Example: count unique users per minute (state = set of users seen). Use StatefulParDo for complex state.
Can I run Beam locally?
Yes, with DirectRunner (single-threaded, for dev). Good for testing. Production runs on Dataflow (distributed, auto-scaling).

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