databricks-pipelines

Lakeflow Spark Declarative Pipelines Development

FIRST: Use the parent databricks-core skill for CLI basics, authentication, profile selection, and data discovery commands.

Decision Tree

Use this tree to determine which dataset type and features to use. Multiple features can apply to the same dataset — e.g., a Streaming Table can use Auto Loader for ingestion, Append Flows for fan-in, and Expectations for data quality. Choose the dataset type first, then layer on applicable features.

User request → What kind of output?
├── Intermediate/reusable logic (not persisted) → Temporary View
│   ├── Preprocessing/filtering before Auto CDC → Temporary View feeding CDC flow
│   ├── Shared intermediate streaming logic reused by multiple downstream tables
│   ├── Pipeline-private helper logic (not published to catalog)
│   └── Published to UC for external queries → Persistent View (SQL only)
├── Persisted dataset
│   ├── Source is streaming/incremental/continuously growing → Streaming Table
│   │   ├── File ingestion (cloud storage, Volumes) → Auto Loader
│   │   ├── Message bus (Kafka, Kinesis, Pub/Sub, Pulsar, Event Hubs) → streaming source read
│   │   ├── Existing streaming/Delta table → streaming read from table
│   │   ├── CDC / upserts / track changes / keep latest per key / SCD Type 1 or 2 → Auto CDC
│   │   ├── Multiple sources into one table → Append Flows (NOT union)
│   │   ├── Historical backfill + live stream → one-time Append Flow + regular flow
│   │   └── Windowed aggregation with watermark → stateful streaming
│   └── Source is batch/historical/full scan → Materialized View
│       ├── Aggregation/join across full dataset (GROUP BY, SUM, COUNT, etc.)
│       ├── Gold layer aggregation from streaming table → MV with batch read (spark.read / no STREAM)
│       ├── JDBC/Federation/external batch sources
│       └── Small static file load (reference data, no streaming read)
├── Output to external system (Python only) → Sink
│   ├── Existing external table not managed by this pipeline → Sink with format="delta"
│   │   (prefer fully-qualified dataset names if the pipeline should own the table — see Publishing Modes)
│   ├── Kafka / Event Hubs → Sink with format="kafka" + @dp.append_flow(target="sink_name")
│   ├── Custom destination not natively supported → Sink with custom format
│   ├── Custom merge/upsert logic per batch → ForEachBatch Sink (Public Preview)
│   └── Multiple destinations per batch → ForEachBatch Sink (Public Preview)
└── Data quality constraints → Expectations (on any dataset type)