databricks-pipelines

name: databricks-pipelines description: Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation. compatibility: Requires databricks CLI (>= v1.0.0) metadata: version: "0.3.0" parent: databricks-core

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.
• When reading or defining datasets within the pipeline, use the dataset name only — do NOT use fully-qualified names unless the pipeline already does so or the user explicitly requests a different target catalog/schema.

API Reference

Before writing pipeline code for any feature, read the linked reference file. Each table below maps the feature to the exact API and to the detail file for that (feature, language).

Some features sit on top of others — read both:

• Auto Loader / Auto CDC / Sinks target a streaming table → also read streaming-table-python.md / streaming-table-sql.md.
• Expectations attach to a dataset → also read the dataset definition file (streaming-table / materialized-view / temporary-view).

Dataset Definition APIs

Flow and Sink APIs

CDC APIs

For querying SCD Type 2 history tables (__START_AT / __END_AT, point-in-time, joining facts with historical dimensions), see scd-2-querying.md.

Data Quality APIs

Reading Data APIs

Table/Schema Feature APIs

Legacy DLT Syntax — always migrate

The tables above show only the modern API. If you see any of the following in user code, it is the legacy DLT syntax — always migrate to the modern form, do not extend it. Read references/dlt-migration.md before suggesting changes so the conversion is correct (especially around apply_changes → create_auto_cdc_flow semantics and partition_cols → cluster_by).

Language Selection (Python vs SQL)

Decide before scaffolding — the choice picks template files (.py vs .sql) and which reference docs apply. Both can coexist, but pick a primary. When unsure, default to SQL for simplicity.

If ambiguous, ask. Stick with the chosen language unless the user explicitly switches.

Choose Your Workflow

Three project shapes exist — pick before scaffolding. Default to A for production-bound work and C for exploration / demo scaffolding.

• A: Standalone new pipeline project (DAB) — pipeline IS the project, no existing databricks.yml. Scaffold with databricks pipelines init --output-dir . --config-file init-config.json. → 1-project-initialization-with-dab.md
• B: Pipeline in an existing bundle (DAB) — databricks.yml already exists. Add a resources/<name>.pipeline.yml pointing at src/. → 1-project-initialization-with-dab.md#workflow-b-pipeline-in-existing-bundle
• C: Rapid CLI iteration (no bundle) — prototyping. databricks pipelines create / start-update / list-pipeline-events; formalise into a bundle later if the work goes to production. → 2-rapid-iteration-with-cli.md

Pipeline Structure

• Follow the medallion pattern (Bronze → Silver → Gold) unless the user says otherwise. Keep it simple by default — just a few tables.
• One dataset per file, named after the dataset. Transformation files live in src/ or transformations/.
• Gold layer: preserve key business dimensions. When aggregating into Gold, keep the dimensions analysts will filter / slice by (location, department, product line, customer segment, time period). Over-aggregating loses information that can't be recovered downstream. If a dashboard is mentioned, every filter on it needs to be a column in the Gold table. Easier to aggregate further in queries than to recover lost dimensions.

Running a Pipeline

Picking the right run command depends on the workflow chosen above.

• Workflow A / B (DAB) — Code changes only take effect after databricks bundle deploy. Always deploy before any run, dry run, or selective refresh.

  databricks bundle validate --profile <profile>
  databricks bundle deploy -t dev --profile <profile>
  databricks bundle run <pipeline_name> -t dev --profile <profile>
  databricks pipelines get <pipeline_id> --profile <profile>      # status
  

  → Full DAB run + iteration details: references/1-project-initialization-with-dab.md#running-a-pipeline-workflow-a--b

• Workflow C (CLI, no bundle) — Upload files to the workspace, then drive the pipeline directly. Re-upload after every code change.

  databricks workspace import-dir ./my_pipeline /Workspace/Users/<user>/my_pipeline --overwrite
  databricks pipelines start-update <pipeline_id>
  

  → Full CLI run + polling pattern: references/2-rapid-iteration-with-cli.md

Refresh modes (both workflows):

• Selective refresh is preferred when you only need to run one table. Dependencies must already be materialized.
• Full refresh is the most expensive and dangerous option and can lead to data loss (it reprocesses streaming sources from scratch, destroying streaming state). Use only when really necessary. Always suggest it as a follow-up the user must explicitly approve.

Always poll the update, not top-level pipeline state — see the polling rationale in 2-rapid-iteration-with-cli.md#step-4-start-an-update-and-poll-that-update. Same rule applies to bundle runs.

Reference Index

Project & lifecycle:

• 1-project-initialization-with-dab.md — Workflows A and B.
• 2-rapid-iteration-with-cli.md — Workflow C; start-update + polling + error-extraction.
• pipeline-configuration.md — Full create/update JSON reference + variant snippets + multi-schema + platform constraints.
• performance.md — Liquid Clustering, state management, joins, pre-aggregation, monitoring.
• dlt-migration.md — DLT → SDP conversions.

Cross-cutting patterns:

• streaming-patterns.md — Dedup, windowed aggregations, late data, rescue-data quarantine, anomaly detection, lag monitoring.
• scd-2-querying.md — Current-state, point-in-time, joining facts with historical dims.
• kafka.md — Kafka / Event Hubs ingestion.

Auto Loader format-specific options: JSON · CSV · XML · Parquet · Avro · Text · ORC.

Dataset, flow, CDC, expectation, Auto Loader, and sink references are listed per (feature, language) in the API Reference tables above.