Actions Reference¶
Concept¶
What is an Action¶
An Action is the atomic unit of work in a Lakehouse Plumber (LHP) FlowGroup. A Pipeline contains one or more FlowGroups, and a FlowGroup contains an ordered list of Actions. Each Action declares one step — read a source, transform a view, persist a table, or assert a quality rule — and LHP compiles it into the corresponding Lakeflow Declarative Pipelines Python construct.
Action flow¶
graph LR
A[Load] --> B{0..N Transform}
B --> C[Write]
Every FlowGroup starts with one Load, applies zero or more Transforms, and ends with one Write. Test actions attach data-quality expectations to any view along the way.
Minimum FlowGroup¶
This FlowGroup ingests a Delta source, renames a column, and writes the result to a streaming table:
pipeline: bronze_ingestion
flowgroup: customer
actions:
- name: load_customer
type: load
readMode: stream
source:
type: delta
database: samples.tpch
table: customer_sample
target: v_customer_raw
- name: rename_customer_key
type: transform
transform_type: sql
source: v_customer_raw
target: v_customer
sql: "SELECT c_custkey AS customer_id, c_name AS name FROM stream(v_customer_raw)"
- name: write_customer_bronze
type: write
source: v_customer
write_target:
type: streaming_table
database: "${catalog}.${bronze_schema}"
table: customer
Action types reference¶
Actions come in four top-level types:
Type |
Purpose |
|---|---|
Load
|
Bring data into a temporary view (e.g. CloudFiles,
Delta, JDBC, SQL, Python).
|
Transform
|
Manipulate data in one or more steps (SQL, Python,
schema adjustments, data-quality checks, temp tables…).
|
Write
|
Persist the final dataset to a streaming_table or
materialized_view.
|
Test
|
Validate data quality using DLT expectations in
temporary tables (uniqueness, referential integrity…).
|
Where to start¶
Quickstart — build your first pipeline end-to-end in about ten minutes, including the load and write Actions used above.
Decisions — decision matrices for picking a load source, write target, streaming vs. batch mode, and write mode.
Further Reading¶
Reference templates (Github Repo) fully documented YAML files covering every option.
Databricks Expectations: DLT expectations