What's changed: Created Professional Data Engineer Chapter 2 (Domain 2 "Ingest and process": planning/building = sources/sinks, transforms, batch/streaming/windowing/late data, Dataflow/Beam/Dataproc/Spark/Hadoop/Cloud Data Fusion/BigQuery/Pub/Sub/Kafka; deploy/operationalize = Cloud Composer (DAG)/Workflows, cleansing/AI enrichment, CI/CD).
2.2Deploying and operationalizing pipelines
Understand job automation and orchestration (Cloud Composer, Workflows), data cleansing and AI data enrichment, integrating new data sources, and continuous delivery of pipelines via CI/CD.
Operationalizing means running designed pipelines automatically, repeatably, and safely. Orchestration and CI/CD put the data platform into operation.
2.2.1Automation and orchestration
Orchestrate multi-step jobs with Cloud Composer (managed Airflow), expressing dependencies as a DAG. Use Workflows for lightweight cross-service chaining. Clean data for quality and, as needed, add value with AI data enrichment (classification, extraction, summarization). Integrate new data sources by fitting schema/format into ingestion logic. Map "multi-step dependencies = Cloud Composer (DAG)" and "lightweight chaining = Workflows."
2.2.2Continuous delivery via CI/CD
Validate and deploy pipeline code (transform logic, DAGs, IaC) via CI/CD (e.g., Cloud Build). This ensures changes are reviewed, tested, and applied consistently across environments (dev→prod). Avoid manual deploys that cause drift and errors. Map "apply pipeline changes safely = CI/CD."
Common: requirement → means. E.g., "schedule dependent jobs as a DAG" = Cloud Composer; "lightweight cross-service chaining" = Workflows; "deploy transforms/data as code safely" = CI/CD (Cloud Build); "add classification/extraction to data" = AI data enrichment.
Watch the mix-ups: (1) Do not confuse Cloud Composer (heavy dependency orchestration) and Workflows (lightweight chaining). (2) Manual deploys cause drift—use CI/CD for consistency. (3) Simple recurring runs may suffice with BigQuery scheduled queries.
2.2.3Section summary
- Multi-step dependencies = Cloud Composer (DAG); lightweight chaining = Workflows
- Cleansing and AI data enrichment add quality and value
- Validate and consistently deploy pipeline changes via CI/CD (Cloud Build)
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Quick check
(just a quick review)Q1. To schedule/orchestrate multi-step pipelines with dependencies as a DAG, which is best?
Q2. For lightweight cross-service step chaining (a simple workflow), which is best?
Q3. To review/test pipeline transform logic and DAGs and deploy consistently, which best fits?
Q4. Which correctly contrasts Cloud Composer and Workflows?
Q5. What is the main problem with manual pipeline deploys?
Q6. To add value like classification, extraction, or summarization to data, which best fits?