Managed ETL Services: Glue, Dataflow, Data Factory

• Describe what Glue/Dataflow/Data Factory each manage for you
• Choose batch vs streaming vs hybrid ingest for a workload
• Write and orchestrate jobs with monitoring, retries and lineage built-in

• You don't provision or patch Spark/Flink clusters — the service autoscales them.
• Orchestration, retries, checkpointing and lineage are built-in rather than assembled.
• Serverless cost fits bursty loads without paying for idle fleet.
• Managed connectors (SaaS, DBs, file systems) ingest and deliver out of the box.

Home cooking gives control but every ingredient, pot and schedule is yours. A delivery service standardizes the kitchen, the menu, and the logistics — quality and speed up, control of the recipe stays with you.

• Glue: Spark ETL jobs (PySpark/Scala/SQL) run on managed Spark; crawlers infer schema into the catalog; Workflows/StepFunctions orchestrate.
• Dataflow: Beam pipelines (Java/Python) run on a managed runner; uniform streaming/batch transforms with exactly-once-ish sinks.
• Data Factory: linked services + pipelines with triggers, Copy activities, and in-line dataflows over Azure blob/SQL.
• All emit monitoring (CloudWatch/Stackdriver/Log Analytics), retry policies and run/version metadata.
• Best practice: transform in SQL where possible (dbt/Spark SQL) so logic is portable out of any one product.

• An e-commerce platforrocks nightly revenue aggregation as a Glue PySpark job orchestrated by StepFunctions with DataBrew-quality checks.
• A payments company sends every event to Dataflow (Beam) for windowed enrichment into BigQuery with exactly-once semantics.
• A manufacturer's daily ERP export moves into Azure blob and FACTORY pipelines copy + lightly normalize it into Synapse.

• Keep transform logic plain SQL/code so it survives vendor changes — services are the delivery, not the recipe.
• Use serverless runners for bursty jobs; shift steady 24/7 streams to Dataflow-class managed streaming.
• Register every job's output into the catalog so lineage populates automatically.
• Expose run metadata (script hash, env, schema) as columns so you can audit which code produced which rows.

• Writing vendor-flavored glue that never ported and locking the warehouse into one runner.
• Ignoring the service's autoscale behavior: a job tuned for 1 spark worker explodes cost at 20.
• No retry/idempotency design, so a network blip inside a managed job double-loads a table.
• Skipping the service's own logs and stitching pre-query hacks to debug batch failures.

• Start with the service's run log + lineage view — Glue/Dataflow/Df write one we can inspect before touching SQL.
• Re-run failures with a narrowed dataset (date range, small partition) to isolate a code vs cluster issue.
• Watch autoscale metrics: high shuffle vs high compute distinction often tells you query-bound vs data-bound.
• Compare run metadata across retries — an identical green run after a red one usually means flaky retry, not fixed code.