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Kitsuna Data

About The Project

This is a concept for what a Rails-inspired small data platform for startups and SMEs could look like. After using a variety of end-to-end solutions like DOMO, Keboola, Mozart Data and others, I keep wishing there was something that would do the 80% of ELT + BI out-of-the-box, without the price surprises.

This project is an attempt to stitch together a set of solid and reliable open-source tools that combine into a lean platform where one data engineer can own the entire lifecycle. From ELT, to data modelling, to deploying and scaling in production.

Kitsuna overview diagram

Main Features

  1. 🧪 From laptop to production in minutes - Develop locally with DuckDB, deploy with the same code. No more "it works on my machine" problems.

  2. ⚡ Lightning-fast analytics on any data size - DuckDB's column-oriented design handles gigabytes of data on modest hardware. Query billions of rows in seconds.

  3. 📊 Beautiful dashboards - Drag-and-drop dataviz with Metabase. Perfect for everyone - tech and non-tech alike.

  4. 💸 Scale without breaking the bank - Enterprise-grade data stack for as little as $30/month. DuckDB + SQLMesh's efficiency means less compute costs than Snowflake or BigQuery.

  5. 🔄 30+ ready-to-use integrations - Instant integrations with dlt for Stripe, GitHub, Salesforce, and more. Connect your SaaS tools with minimal code.

  6. 🤖 Just ask your DB - Ask questions in plain English with DuckDB's MCP. Get immediate answers without writing complex queries.

  7. 🔍 End-to-end data lineage - SQLMesh tracks transformations from raw to gold data. Understand exactly where metrics come from and debug easily.

Warning

The project is very much in the pre-alpha stage. This is more of an experiment and is not meant for produciton workloads.

Goals

  • Local-first development for the entire stack.
  • Support companies that can't afford heavy, expensive data tools or large teams.
  • No "SSO tax" - all tools should be either fully free, or affordable once deployed in serious prod use case.
  • No k8s, so a small data team can be self-sufficient .
  • Cheap path to production and scaling.

Tech Stack

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