{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/2"}],"enrichment":{"capability":"dlt automates extraction, schema inference, and loading of data from REST APIs, SQL databases, files, and DataFrames into 20+ destinations (DuckDB, Snowflake, BigQuery, Postgres, etc.) with declarative resource definitions and zero-copy Arrow support.","skillfed_tags":["etl-pipeline","data-integration","schema-inference"],"use_cases":["Load REST API data into DuckDB or Snowflake with automatic pagination, filtering, and schema inference.","Replicate tables from a MySQL or PostgreSQL database into a data warehouse with incremental updates.","Ingest CSV or Parquet files from S3, GCS, or Azure into a destination, handling schema drift automatically.","Build an Airflow DAG that extracts from multiple sources, normalizes nested data, and upserts into BigQuery.","Merge pandas or Polars DataFrames into a warehouse with zero-copy Arrow support and type safety.","Enforce data quality at the gate using schema contracts that reject or adapt unexpected columns and types."],"what_it_does":"dlt is a Python library that handles the repetitive parts of data pipelines: extracting from REST APIs, SQL databases, cloud storage, or DataFrames; inferring and normalizing schemas automatically; and loading into any of 20+ destinations. You define sources declaratively using decorators or configuration objects, then point a pipeline at a destination\u2014dlt manages credentials, DDL, type mapping, staging, and schema drift for you. It's a library, not a platform: you pip-install it into your existing code and keep your workflow intact.\n\nThe package supports incremental loading (load only new or changed rows), merge strategies (upsert on primary key), schema contracts (freeze, evolve, or discard unexpected data), and secrets injection from environment variables or config files. It works anywhere Python runs\u2014Colab notebooks, AWS Lambda, Airflow DAGs, local scripts, or AI coding agents. The Dataset API lets you reconnect to a pipeline by name and read tables back in the format your tool needs (DataFrame, SQL query, etc.).","worth_installing":"Yes. dlt is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It solves a real problem\u2014automating tedious data loading\u2014with a clean, Pythonic API and support for many sources and destinations. The declarative resource model and schema inference reduce boilerplate significantly. Start with a simple REST API or SQL database extraction to evaluate fit."},"id":"dlt","links":{"html":"https://skillfed.io/packages/dlt","md":"https://skillfed.io/packages/dlt.md","pypi":"https://pypi.org/project/dlt/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"dlt","python_support":"supports_current","summary":"dlt is an open-source python-first scalable data loading library that does not require any backend to run."},"popularity":{"monthly_downloads":8210929,"position":1652,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.30.0"}