Lightweight Python library for building data pipelines as code
https://dlthub.comdlt is an open-source Python library that simplifies the creation of data loading pipelines. It handles schema inference, incremental loading, data type mapping, and destination management โ all from a few lines of Python code. Unlike UI-heavy integration platforms, dlt is designed for engineers who want full control over their pipelines while avoiding boilerplate.
We use dlt for custom ingestion scenarios where flexibility and code-first workflows matter more than a visual interface. It’s our go-to tool when we need to extract data from proprietary APIs, build custom incremental loading logic, or integrate data loading into existing Python applications and Airflow DAGs.
We build dlt pipelines for API extraction, database replication, and file ingestion with declarative resource definitions.
We implement cursor-based pagination, last-modified timestamps, and merge keys for efficient incremental extraction.
We leverage dlt's automatic schema evolution: new columns detected, data types inferred, and schema changes versioned.
We embed dlt pipelines inside Airflow DAGs as native Python tasks.
We configure dlt for Snowflake, BigQuery, ClickHouse, PostgreSQL, DuckDB, and filesystem destinations.
Extracting data from proprietary or undocumented APIs where pre-built connectors don’t exist.
Data pipelines as pure Python code โ version-controlled, tested, and reviewed.
Scenarios where deploying a full integration platform is overkill.
Quick prototyping of new data sources before committing to a full platform.
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