Starflow

Declarative data pipelines as YAML.

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.

Starflow is the Declarative Data Stack a foundational rethinking of data engineering that delivers production grade data faster, simpler, and more securely than ever before.

Starflow
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Declarative Data Stack

Unified, simple, and powerful data engineering

✅ Quality-First
Built in validation
⛓️ Pure SQL
No templating
💻 Local Dev
Free & fast

See it in action

Three ways to ship a pipeline

Talk to a Starflow persona, ask Starflow what to do next, or call a CLI skill directly. Every interaction below is the real prompt and the real shape of the response.

Talk to a Starflow persona

Five expert AI personas cover the data lifecycle. Winston the Data Architect lays out two or three options with trade-offs before naming a recommendation, and names the failure mode of every choice.

Meet the personas
~/projects/acme-analytics - claude code

Ask Starflow what to do next

Starflow reads a manifest of skill dependencies and scans the artifacts you've already produced. It recommends the next required step based on real state, not a fixed checklist, so it works whether you started at discovery or jumped in mid-stream.

See the manifest model
~/projects/acme-analytics - claude code

Or call a CLI skill directly

For quick, targeted tasks, skip the methodology. Each of the 49 CLI skills knows the exact flags, YAML schema, and write strategies for its Starflow command. Ask in plain English; receive correct configuration on the first try.

Browse the catalog
~/projects/acme-analytics - claude code

Developer Tools

AI-Powered Data Engineering

Starflow meets you where you work. An intelligent VSCode extension and 48 AI skills for Claude Code give your team superpowers.

VSCode Extension

Your entire data platform, inside your editor

Build, test, and deploy data pipelines without leaving VSCode. Auto-infer schemas, preview SQL, visualize lineage, and deploy DAGs with a single click.

Schema Inference

Auto-generate YAML configs from data sources

SQL Preview & Dry Run

Validate transformations before execution

Visual Lineage & ER Diagrams

Understand data flow at a glance

One-Click DAG Deploy

Generate and deploy Airflow/Dagster workflows

Results Viewer

Inspect query results inside the editor

ACL Management

Review and manage access control visually

BigQuerySnowflakeRedshiftDatabricksDuckDB

AI Skills

48 skills that make Claude Code a Starflow expert

Install the Starflow Skills plugin and Claude Code instantly knows every CLI command, YAML pattern, write strategy, and best practice. It generates correct configurations on the first try.

8 Ingestion Skills

autoload, load, ingest, stage, kafkaload and more

7 Transform & Extract

transform, extract, extract-data, extract-schema

5 Lineage & Quality

lineage, col-lineage, expectations, table-deps

10 Ops & Orchestration

dag-generate, dag-deploy, serve, metrics, freshness

6 Schema & Security

bootstrap, infer-schema, secure, iam-policies

12 Utilities & Config

config, connection, compare, test, validate

Claude CodeDuckDBBigQuerySnowflakeRedshiftDatabricks

The Old Way vs. The Starflow Way

See the dramatic difference in approach, complexity, and results

Compare the fragmented Modern Data Stack approach with Starflow'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.

Quality checks as afterthought
Bad data reaches production
Downstream debugging nightmares
Multiple tool dependencies

Template Lock in

SQL tangled in Jinja/Python templates that only work in specific tools.

Vendor specific templating
Impossible to test locally
No portability between tools
Complex debugging required

Expensive Development

Every change requires expensive warehouse compute cycles for testing.

Expensive warehouse compute
Slow feedback loops
Wasted development time
High operational costs

Tool Sprawl

Multiple separate tools for each function, creating integration complexity.

Multiple vendor dependencies
Complex integration points
Fragile system architecture
Maintenance overhead

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 Starflow's Declarative Data Stack approach.

Faster development cycles
Lower operational costs
Simplified architecture
Better data quality
Team productivity
Vendor independence

Start Your Journey Today

Experience the power of the Declarative Data Stack. See how Starflow 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.

Trusted by Data Teams Worldwide

🏢 Enterprise Ready
🔒 Production Grade
⚡ High Performance
🌍 Open Source