Data Engineering

Explore your warehouse, inspect tables, and sample rows without leaving the editor. The extension connects to Snowflake, BigQuery, Redshift, and Postgres instances, pulls schemas, reads column statistics, and runs profiling queries so you can spot skews, nulls, and cardinality issues. It generates ready-to-run SQL snippets, maps schema changes to downstream models, and records table lineage as you edit transformations.

This plugin exposes over 30 focused skills and an MCP server that talks to Apache Airflow's full REST API. You can create and edit DAGs, push DAG files, trigger runs, check task status, read task logs, clear failed tasks, and watch scheduler health. It edits dbt models, runs dbt commands, writes tests, and commits changes with clear git messages.

The tool also helps write pipeline code: it scaffolds Python or SQL transformation functions, generates parametrized operators, and creates sensible defaults for retries, timeouts, and task dependencies. It tracks schema drift by comparing current table metadata to expected models and flags breaking changes so you can add tests or migrations.

Imagine fixing a failing Airflow task: you inspect the table samples, write a quick SQL patch, update the DAG, push the change, and trigger a backfill — all from the same interface. That avoids context-switching between console, SQL editor, git, and Airflow UI, saving hours when debugging production pipelines or shipping hotfixes.