Principle:DataTalksClub Data engineering zoomcamp Dlt Pipeline Execution
| Page Metadata | |
|---|---|
| Knowledge Sources | dlt docs: dlt Documentation, EtLT pattern: How dlt Works |
| Domains | Data_Engineering, Data_Ingestion |
| Last Updated | 2026-02-09 14:00 GMT |
Overview
Managed pipeline execution is the principle of configuring and running a data pipeline through a framework that automatically handles schema evolution, state tracking, normalization, and destination-specific loading.
Description
A data pipeline is a configured sequence of operations that extracts data from one or more sources, transforms it as needed, and loads it into a destination. In a managed pipeline execution model, the developer provides high-level configuration (pipeline name, destination, dataset name) and a data resource, and the framework orchestrates the entire load process.
The managed execution model contrasts with manual ETL scripting, where the developer must explicitly handle every aspect of the load: creating destination tables, mapping source types to destination types, managing batching, handling errors, and tracking which data has already been loaded.
Key responsibilities managed by the framework include:
- Schema inference and evolution -- The framework inspects the incoming data, infers the destination schema, and automatically handles schema changes (new columns, type widening) across successive pipeline runs.
- State management -- The pipeline maintains state between runs, tracking which data has been loaded, what schema version is current, and whether the previous run completed successfully. This enables incremental loading and crash recovery.
- Normalization -- Nested data structures (e.g., JSON with nested objects and arrays) are automatically normalized into flat relational tables suitable for the destination.
- Destination abstraction -- The same pipeline code can target different destinations (BigQuery, PostgreSQL, DuckDB, etc.) by changing a single configuration parameter. The framework handles destination-specific SQL dialect differences, authentication, and bulk loading optimizations.
- Load packaging -- Data is organized into load packages that can be atomically committed to the destination. If a load fails partway through, the framework can retry or roll back the partial load.
Usage
Use managed pipeline execution when:
- The pipeline must reliably load data into a data warehouse or database
- Schema evolution must be handled automatically without manual DDL changes
- The same pipeline should be deployable to different destinations without code changes
- State tracking and incremental loading are required across successive runs
- The developer wants to focus on extraction logic rather than destination-specific loading mechanics
Theoretical Basis
The managed pipeline execution pattern follows this conceptual flow:
FUNCTION configure_pipeline(name, destination, dataset):
pipeline = new Pipeline()
pipeline.name = name
pipeline.destination = resolve_destination(destination)
pipeline.dataset = dataset
pipeline.state = load_previous_state(name)
RETURN pipeline
FUNCTION execute_pipeline(pipeline, resource):
-- Phase 1: Extract
extracted_data = consume_generator(resource)
-- Phase 2: Normalize
schema = infer_or_evolve_schema(extracted_data, pipeline.state.schema)
normalized_data = normalize_to_relational(extracted_data, schema)
-- Phase 3: Load
load_package = create_load_package(normalized_data, schema)
result = pipeline.destination.load(load_package)
-- Phase 4: State update
pipeline.state.schema = schema
pipeline.state.last_load = result.metadata
persist_state(pipeline.state)
RETURN result
-- Usage:
pipeline = configure_pipeline("my_pipeline", "warehouse", "my_dataset")
info = execute_pipeline(pipeline, data_resource)
The pipeline operates in four distinct phases. Extraction pulls data from the resource generator. Normalization converts the extracted data into a relational form that matches the destination's requirements. Loading sends the normalized data to the destination in an atomic package. State update persists the result so the next run can build on the current state.
The pipeline.run() abstraction encapsulates all four phases behind a single method call. The return value (a load info object) provides metadata about what was loaded, including record counts, table names, and any schema changes that were applied. This information is essential for monitoring, alerting, and debugging.