Stop Fighting Your Data Stack
Stop Fighting Your Data Stack. Start Shipping Value.
The Modern Data Stack promised agility but delivered complexity. You're left wrestling with fragmented tools, opaque data lineage, and vendor lock-in. It's time to evolve.
Starlake is the Declarative Data Stack—a foundational rethinking of data engineering that delivers production-grade data faster, simpler, and more securely than ever before.
Declarative Data Stack
Unified, simple, and powerful data engineering
Is Your Modern Stack Holding You Back?
If you're tired of these common frustrations, you're not alone.
The Modern Data Stack promised agility but delivered complexity. These are the real problems teams face every day.
Garbage In, Garbage Out
Bad data silently poisons your pipelines, causing errors that are a nightmare to debug downstream.
Template Lock-In
Your SQL is tangled in a web of Jinja or Python, making it impossible to run, test, or audit outside of a specific tool.
Expensive, Slow Cycles
Every small change requires a costly, minutes-long "compile and test" cycle on your production data warehouse.
Tool Sprawl
You're stitching together separate tools for ingestion, quality, transformation, or have to use proprietary orchestration tools, creating a fragile and complex system.
Sound Familiar?
These aren't isolated issues—they're systemic problems with the Modern Data Stack approach. It's time for a better way.
The Starlake Way: Simple, Pure & Powerful
Starlake isn't just another layer. It's a unified workflow built on a few core principles.
Each principle addresses a fundamental problem with the Modern Data Stack, delivering solutions that are both powerful and simple.
Quality-First Ingestion
Stop firefighting bad data. Starlake validates every record against your schemas and business rules before it enters your pipeline.
Instead of a separate quality tool, validation is built into the very first step, guaranteeing only trusted data flows downstream.
SQL, Unchained
Write pure, portable SQL. Stop wrestling with complex templating languages. With Starlake, your transformation logic is just SQL.
Copy it from your favorite editor (like DBeaver or Snowsight) and it simply works. We automatically derive the table and column lineage for you.
Develop Locally, Deploy Globally
Achieve lightspeed development cycles. Starlake lets you develop and debug your entire pipeline on your laptop using DuckDB, for free.
Our transparent transpilation automatically converts your Snowflake SQL to run locally. When you're ready, deploy the original, pure SQL to production.
Git-Style Data Branching
Experiment on production data, safely. Starlake uses "lazy snapshots" to give you a Git-like branch of your entire production dataset.
Explore, test, and develop on a perfect, read-only replica of your live environment without any risk.
Orchestration Agnostic
Use the orchestrator you already love. Starlake automatically generates the execution graph (DAG) from your pure SQL.
Feeds it into your orchestrator of choice—whether it's Snowflake Tasks, Airflow or Dagster.
A Truly Agnostic Semantic Layer
Define business logic once, use it everywhere. Write your semantic model once, and Starlake automatically transpiles it.
To the native format for your database (Snowflake Cortex Analyst semantic model) or BI tools, including PowerBI (TMDL) and Looker (LookML). Ensure consistency across your organization.
Built for the Entire Data Team
Unify your workflow with tools for everyone. From powerful CLI for engineers to intuitive GUI for analysts.
For Engineers: A powerful CLI for scripting, automation, and complex pipeline management. For Analysts: An intuitive GUI to configure ingestion, view lineage, and monitor data health.
The Old Way vs. The Starlake Way
See the dramatic difference in approach, complexity, and results
Compare the fragmented Modern Data Stack approach with Starlake's unified Declarative Data Stack solution.
The Old Way
Modern Data Stack complexity
Fragmented Quality
Separate tools for ingestion, quality, and transformation create gaps where bad data slips through.
Template Lock-In
SQL tangled in Jinja/Python templates that only work in specific tools.
Expensive Development
Every change requires expensive warehouse compute cycles for testing.
Tool Sprawl
Multiple separate tools for each function, creating integration complexity.
The Problem
The Modern Data Stack approach creates complexity, vendor lock-in, and expensive development cycles that slow down your team and increase costs.
Ready to Build the Future of Data? 🚀
Leave the complexity of the Modern Data Stack behind. Embrace a simpler, faster, and more reliable way to deliver production-grade data.
Join the teams already building with Starlake's Declarative Data Stack approach.
Start Your Journey Today
Experience the power of the Declarative Data Stack. See how Starlake can transform your data engineering workflow in just 30 minutes.
Free to get started. Open source, no vendor lock-in, and works with your existing infrastructure.