Version control for data and ETL pipelines. Rollback mistakes in minutes, not weeks.
| date | total_revenue | customers |
|---|---|---|
| 2025-01-01 | $45,230 | 128 |
| 2025-01-02 | $52,180 | 142 |
| 2025-01-03 | $58,920 | 156 |
| 2025-01-04 | $61,450 | 163 |
| 2025-01-05 | $47,890 | 131 |
Engineers waste up to 40% of their time monitoring, investigating, and fixing data. Even then, you don't trust the accuracy or freshness of your dashboards.
When mistakes happen, data teams face 2-3 week backfill campaigns costing $50K-200K per incident. One bad query can corrupt your entire pipeline.
You want AI to answer data questions, but it can't show you proof or where data came from. Without lineage, AI-generated insights are just guesses.
Tables depend on other tables, but tracking relationships manually is error-prone. One delayed upstream job breaks everything downstream.
Zero visibility into data spending. Companies waste 60-80% of budgets on redundant datasets, inefficient queries, and over-provisioned infrastructure.
Bring Git-like superpowers to your data pipelines. Branch, version, and rollback with confidence.
95% faster incident response. One-click rollback with automatic downstream DAG cascade. Select any point in time, click rollback, and the entire dependency tree cascades automatically.
ROLLBACK TO VERSION 122
Complete audit trail. Every query, transformation, and schema change is automatically versioned. Git-like branching for data and schema with 60%+ storage reduction.
SELECT * FROM sales VERSION AS OF 123
Lineage captured at authoring time. Automatically track table dependencies at the partition level. Jobs run in the right order with change impact assessment.
DEPENDS ON sales.date = YESTERDAY
Hours to seconds. AI agents understand schemas and lineage. Ask questions in natural language and get instant SQL generation and query execution.
"Show me sales by region last month"
One platform, every data persona
That query you just wrote? Click "Save as Table" and it becomes a production-ready data pipeline with scheduling, dependency tracking, error handling, and automatic rollback propagation. No Airflow configs, no YAML files.
Point to any S3 location - we auto-detect the schema and create a queryable table instantly. Import wizard handles partitions, data types, everything. Start running Python/PySpark queries immediately.
Develop and test data transformations on isolated feature branches without touching production. Enable Preview mode to let stakeholders validate changes. Merge when ready.
Get paged about a late data pipeline? Open the table, see its upstream dependencies with completion status at a glance. Click the incomplete one. Repeat. Find root cause in seconds.
Bad data got through? Rollback the source table to any previous snapshot. DataVersion automatically marks all downstream tables as invalid and triggers cascade reprocessing.
No idle clusters burning money. EMR Serverless spins up only when jobs run, then shuts down automatically. Pay only for actual compute seconds.
A complete data platform in one deployment
Ask questions in plain English. Get executable SQL instantly. One-click to run, one-click to save.
Develop and test on isolated branches. Preview for stakeholders. Merge to production when ready.
Complete lineage from source to report, snapshot by snapshot. Full audit trail for compliance.
Point to S3, paste from Excel, or connect external tables. Auto-detect schema and partitions.
Enterprise SSO with AWS Cognito. Secure authentication, audit logs, and cross-account sharing.
Industry-standard table format with time travel, schema evolution, and ACID transactions built-in.
Have questions? We'd love to hear from you.