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GCP Dataflow Pipelines

⬢ ਸਤਰ 2ਤਕਨੀਕੀ
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ਉੱਚ
ਤਨਖਾਹ ਪ੍ਰਭਾਵ
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3 ਮਹੀਨੇ
ਸਿੱਖਣ ਸਮਾਂ
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ਇੱਕ ਨਜ਼ਰ ਵਿੱਚ

Dataflow is Google's fully managed service for Apache Beam pipelines. Process terabytes of data (batch or streaming). Automatic scaling, fault tolerance, monitoring. Used for ETL, real-time analytics, data transformations. Mastery takes 4-6 weeks. Senior practitioners earn 20-30% premium. Skill adjacent to BigQuery, Pub/Sub, and Cloud Storage.

GCP Dataflow Pipelines ਕੀ ਹੈ

Dataflow is Google's fully managed service for Apache Beam pipelines. Define a data pipeline (extract → transform → load), Dataflow executes it at scale. Handles distributed computing, fault tolerance, autoscaling, monitoring. Process gigabytes to petabytes efficiently. Use cases: ETL (extract from Firestore, transform, load to BigQuery), real-time analytics (Pub/Sub → aggregate → BigQuery), batch export (Cloud Storage files → process → BigTable).

🔧 ਯੰਤਰ ਅਤੇ ਈਕੋਸਿਸਟਮ
Apache Beam SDK (Python/Java)Google Cloud DataflowBigQuery (data sink)Cloud Storage (data source/sink)Pub/Sub (streaming source)Cloud LoggingPipeline templatesFlexible Resource Scheduling

📋 ਸ਼ੁਰੂ ਕਰਨ ਤੋਂ ਪਹਿਲਾਂ

Python

💰 ਖੇਤਰ ਅਨੁਸਾਰ ਤਨਖਾਹ

ਖੇਤਰਨਵਾਂਮੱਧਸੀਨੀਅਰ
🇺🇸USA$85k$140k$220k
🇬🇧UK£60k£105k£165k
🇪🇺EU€65k€115k€180k
🇨🇦CANADAC$90kC$145kC$230k

🎓 ਸਰਟੀਫਿਕੇਸ਼ਨ

Google Cloud Professional Data Engineer→Dataflow Documentation→Apache Beam Official Guide→

⚖ ਨਾਲ ਤੁਲਨਾ ਕਰੋ

Apache Spark

❓ ਅਕਸਰ ਪੁੱਛੇ ਸਵਾਲ

▶What's the difference between batch and streaming pipelines?
Batch: process finite data (file, database query), end-to-end latency hours/days. Streaming: continuous data (Pub/Sub), low latency (seconds). Same Beam SDK, different execution model. Choose based on data source and latency needs.
▶How does Dataflow handle stragglers and slow workers?
Dataflow distributes work across workers. If one worker is slow, others pick up extra load (dynamic work rebalancing). Slower worker doesn't hold up the pipeline. Result: predictable latency.
▶Can I use Dataflow for real-time analytics?
Yes. Stream data from Pub/Sub, aggregate in Dataflow, write results to BigQuery. Latency: 30-60 seconds. For sub-second latency, use BigQuery Streaming Inserts or BigQuery BI Engine.
▶How much does Dataflow cost?
Pay for worker hours. Example: 10 workers × 1 hour = 10 worker-hours = ~$5 (at standard pricing). Storage and data processed don't cost extra (unlike BigQuery). Scale is unlimited.
▶What's a SideInput and when do I use it?
SideInput: small reference data (lookup table, configuration). Join main pipeline data with SideInput for enrichment. Example: stream of user events, SideInput = user profiles, enrich events with user info.
▶Can I reuse Dataflow pipelines as templates?
Yes. Create a template (reusable pipeline definition), launch it multiple times with different parameters. Great for recurring jobs (daily exports, hourly aggregations).
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