Pipelines¶

Pipelines connect typed transformations into complete, executable data workflows.

If Data Contracts define what data looks like and Transformations define how data changes, then Pipelines define how those transformations are connected.

ETLantic models pipelines using the Data Pipeline Contract Standard (DPCS) while remaining independent of any execution engine.

What This Section Covers¶

This section explains how to:

Define pipelines with Python classes
Connect transformations using typed inputs and outputs
Declare sources and sinks
Configure execution profiles
Validate pipeline graphs
Generate DPCS artifacts
Plan execution
Produce lineage and documentation

The Authoring Model¶

A pipeline is declared using ordinary Python.

from etlantic import Pipeline, Sink, Source


class CustomerPipeline(Pipeline):
    raw: Extract[RawCustomer] = Extract(asset="customer_source")

    normalized = NormalizeCustomers.step(
        customers=raw,
        minimum_age=18,
    )

    warehouse: Load[Customer] = Load(
        input=normalized.result,
        asset="customer_sink",
    )

The declaration focuses on logical data flow. Profiles bind customer_source and customer_sink to files, tables, APIs, or other environment-specific implementations.

Relationship to DPCS¶

Every pipeline has a portable representation.

Python Pipeline
       │
       ▼
ETLantic
       │
       ▼
DPCS Pipeline Contract

Python is the preferred authoring experience.

DPCS is the portable artifact.

Planning vs. Execution¶

ETLantic separates planning from execution.

Planning determines:

Graph topology
Contract compatibility
Implementation selection
Validation policy
Execution profile
Runtime bindings

Execution plugins perform the actual work.

Sources and Sinks¶

Pipelines begin with typed sources and end with typed sinks.

Source
   │
   ▼
Transformation
   │
   ▼
Transformation
   │
   ▼
Sink

Every connection is validated through data contracts.

Validation¶

Before execution, ETLantic validates:

Graph structure
Contract compatibility
Required bindings
Transformation implementations
Execution profile
Plugin capabilities

Planning should fail before execution whenever possible.

Generated Artifacts¶

A pipeline can generate:

DPCS contracts
Documentation
Mermaid diagrams
Graphviz diagrams
Lineage graphs
Execution plans

Generated artifacts are deterministic and suitable for version control.

Documentation Roadmap¶

Read this section in the following order:

Key Principles¶

Pipelines connect transformations.
Data contracts validate every connection.
Planning precedes execution.
Execution belongs to plugins.
DPCS is the canonical portable representation.
Python remains the preferred authoring experience.

Next Step¶

Continue with Pipeline to learn how to define typed pipeline classes and compose transformations into complete workflows.