Build a business entity graph (ontology) from annotated sources and taxonomy. Use after annotate-sources to design the entity model before CDM generation.
Write dlthub transformation functions that map source tables to CDM entities. Use after generate-cdm to produce the transformation Python script.
Generate a Canonical Data Model (CDM) in DBML using Kimball dimensional modeling. Use after create-ontology to produce the implementation-ready CDM schema.
Add incremental loading to a dlt filesystem pipeline — filter files by modification date, optionally filter records by a timestamp column, and switch to merge with a primary key to deduplicate updated records, so each run only reads new or modified files. Use…
Create a dlt filesystem pipeline that reads files (CSV, Parquet, JSONL, or custom) from local disk, S3, GCS, Azure, or SFTP into a destination. Use for the filesystem core source. Not for REST APIs (rest_api) or databases (sql_database).
Speed up a dlt filesystem pipeline. Use when reading files (CSV, Parquet, JSONL) from local disk, S3, GCS, Azure, or SFTP is slow or memory-heavy and the user wants to optimize it — choose a faster reader, read files in parallel, narrow the glob, or stream in…
Make a dlt pipeline faster or lighter on memory. Use when the user says a pipeline is slow, takes too long, runs out of memory, uses too much RAM, or wants to optimize, speed up, parallelize, or increase throughput. Covers source-agnostic levers (parallelism,…
Adjust a working dlt pipeline for production — remove dev limits, verify pagination (including stuck or looping paginators), configure incremental loading, expand date ranges, and handle rate-limit/429 errors with retries, backoff, and request timeouts. Use…