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)

Common Traps

• Names → SDP = LDP = Lakeflow Declarative Pipelines = (formerly) DLT. All interchangeable when the user mentions them.
• "Create a table" without specifying type → ask whether the source is streaming or batch. Streaming source → Streaming Table; batch source → Materialized View. Mismatched pairs error at validation.
• Aggregation over a streaming source → use a Materialized View with a batch read (spark.read.table / SELECT FROM without STREAM). STs are append-only and don't recompute aggregates when source rows change; MVs do.
• Intermediate logic → default to a Temporary View. Even for shared logic reused by multiple downstream tables. Use a Private MV/ST (private=True / CREATE PRIVATE ...) only when materializing once saves significant reprocessing. For preprocessing before Auto CDC, the temp view is required — the CDC flow reads from STREAM(view_name) (SQL) or spark.readStream.table("view_name") (Python).
• Union of streams → use multiple Append Flows. UNION across streaming sources is an anti-pattern.
• Changing dataset type → cannot change ST→MV or MV→ST in place. Full refresh does NOT help. Drop the existing table manually or rename the new dataset.
• `CREATE OR REFRESH` vs `CREATE` → both parse for SQL datasets, but CREATE OR REFRESH is the idiomatic convention. For PRIVATE datasets: CREATE OR REFRESH PRIVATE STREAMING TABLE / ... MATERIALIZED VIEW.
• Kafka/Event Hubs sink serialization → the value column is mandatory; serialize the row with to_json(struct(*)) AS value. See sink-python.md.
• Multi-column Auto CDC sequencing → SQL: SEQUENCE BY STRUCT(col1, col2). Python: sequence_by=struct("col1", "col2"). See the auto-cdc references.
• Auto CDC TRUNCATE (SCD Type 1 only) → SQL: APPLY AS TRUNCATE WHEN condition. Python: apply_as_truncates=expr("condition"). Do NOT claim truncate is unsupported.
• Python-only features → Sinks, ForEachBatch Sinks, CDC from snapshots, and custom data sources are Python-only. When the user is working in SQL, clarify this and suggest switching to Python.
• Recommend ONE clear approach → present a single recommended path. Don't list anti-patterns or inferior alternatives — they confuse. Only mention alternatives when they genuinely offer different trade-offs.

Common Issues

Error → cause/fix mappings agents hit constantly. For DAB-bundle vs CLI-iteration deploy issues, see the workflow-specific reference files.

Publishing Modes

Pipelines use a default catalog and schema configured in the pipeline settings. All datasets are published there unless overridden.

• Fully-qualified names: Use catalog.schema.table in the dataset name to write to a different catalog/schema than the pipeline default. The pipeline creates the dataset there directly — no Sink needed.
• USE CATALOG / USE SCHEMA: SQL commands that change the current catalog/schema for all subsequent definitions in the same file.
• LIVE prefix: Deprecated. Ignored in the default publishing mode.