Data transformation · Y42 (Datos Intelligence GmbH)

Y42

Managed DataOps platform combining ingestion, dbt-core/SQL/Python transformation, orchestration, and governance in one workspace.

Visit Y42 Alternatives

Y42 is a fully managed data platform built around BigQuery and Snowflake that bundles ingestion, transformation, and orchestration rather than being a transformation tool alone. Its transformation layer runs dbt Core natively alongside SQL and Python assets in a single declarative DAG, so teams get dbt-compatible modeling without operating dbt themselves. Y42 adds a browser-based UI and code IDE, native Git integration with pull-request-based review, and a built-in scheduler that removes the need for a separate orchestrator like Airflow for most pipelines. Governance features include lineage, data contracts, and cost visibility across the warehouse. It targets teams that want the modeling discipline of dbt plus ingestion and scheduling in one managed product, trading some flexibility for less operational overhead than assembling dbt, an EL tool, and an orchestrator separately.

At a glance

Vendor Y42 (Datos Intelligence GmbH)
Pricing model Subscription
Free tier Yes
Deployment Cloud
Open source No
Best for Small-to-mid data teams who want ingestion, dbt-style transformation, and orchestration in one managed product.

Pricing

A free tier is available; paid plans are flat monthly fees with an additional usage-based add-on for row-based data ingestion.

Plan Price Notes
Free $0/month For building on your own data warehouse at no cost
Business $500/month 2 spaces, 3 users, orchestrator, SQL/dbt Core and Python assets
Enterprise Custom pricing Unlimited spaces, SSO, audit logging, SLA
SQL-Based Ingestion Add-on From $15 per million rows ingested Pre-built connectors via cData or custom Python ingestion

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Native dbt Core execution alongside SQL and Python assets
  • Declarative orchestrator and scheduler built in
  • Browser-based UI plus a code IDE
  • Git-native development with pull-request review
  • Built-in data ingestion connectors
  • Lineage and data contracts for governance
  • Warehouse cost visibility

Integrations

Profile last reviewed September 21, 2026

Alternatives

dbt Transforms data already in the warehouse with SQL SELECT statements, adding version control, testing, documentation and lineage. SQLMesh Open-source SQL transformation framework with column-level lineage and virtual data environments, built as a drop-in dbt alternative. Dataform Google Cloud's built-in SQL transformation tool for BigQuery, offering dbt-like modeling without a separate platform to run.

Y42 in the index now

Terms to know

Data modeling The practice of organizing data into entities, relationships and schemas that make it accurate and efficient to query. dbt (data build tool) An open-source tool from dbt Labs that lets analytics teams write data transformations as version-controlled, tested SQL. ELT (extract, load, transform) A data integration pattern that loads raw data first and transforms it afterward using the warehouse's own compute. ETL (extract, transform, load) A data integration pattern that transforms data before loading it, unlike ELT which transforms after loading. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Data ingestion Moving data from its source systems into a storage or processing system where it can be transformed and analyzed.

Related guides

How to choose a data transformation tool Choosing between dbt and its rivals comes down to editor vs code, warehouse lock-in, rebuild cost and who is checking the SQL before it ships.