The Data Pipeline Stack is 6 MCPs (Postgres, ClickHouse, DuckDB, AWS/S3, GitHub, Grafana) that cover OLTP, OLAP, local prototyping, lake, code, and monitoring. Stakeholder questions go from half-day to 5 minutes; pipeline debugging from hours to minutes. Free to mid-scale — you only pay for the underlying services.
ETL, warehouse, lake, and analytics — built for modern data teams
$ mcpizy install postgres clickhouse duckdb aws github grafanaOne command installs and configures all 6 MCPs for Claude Code, Cursor, Windsurf, or any MCP-compatible client.
Modern data stacks run on a predictable shape: Postgres as source-of-truth OLTP, ClickHouse (or Snowflake/BigQuery) as the warehouse, DuckDB for local prototyping, S3 as the lake, GitHub for dbt/Airflow/Dagster code, Grafana for monitoring. This stack is those six as MCPs. Stakeholder questions ('how many active users last month?') that used to take a half-day now take 5 minutes.
The win isn't replacing your BI tool — it's making the 'ad-hoc analysis' path 10x faster. When a PM asks for a breakdown, you don't open Looker or write a Jupyter notebook; you ask Claude, which introspects the warehouse schema, writes the SQL, runs it, and returns the answer with the code for review.
Replaces ~$600/mo of tooling (Mode, Hex seats, Looker admins, BigQuery admin console) for a 3-person data team. Biggest win: stakeholder response time drops 10x, so the team is no longer a bottleneck.
Both have community MCPs (Snowflake MCP, BigQuery MCP). Swap them for ClickHouse MCP if that's your warehouse. Same workflow, different connector.
Yes — for prototyping. DuckDB queries parquet/CSV files directly from S3 without ingestion. 10x faster iteration when you're sketching a transformation. Lift to dbt once the logic is stable.
Yes — it reads your existing model style, introspects the schema, generates the SQL, runs `dbt test`, and opens a PR. Most data engineers cut dbt authoring time by 50%.
Use a read-only replica for most queries. Postgres MCP supports multiple connections — point at the replica for exploration, at primary only for controlled writes behind confirmation.
Their code lives in GitHub (MCP covers it). Execution layer is separate — but the community Dagster MCP and Airflow MCP expose DAG state to Claude, letting you debug failed runs in-chat.