Y42 provides a turnkey data orchestration platform that unifies the building, monitoring, and maintenance of data flows within a single environment. The platform offers native compatibility across all pipeline stages, eliminating the need for third-party adapters and simplifying infrastructure management. It features asset-based orchestration, built-in dependency monitoring, and Git integration for version control of both code and data transformations.
Funding
$33.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Data teams often struggle with fragmented data flows across multiple tools, leading to inefficient maintenance, unpredictable errors, and wasted resources in cloud data warehouses. Version control workflows can be cumbersome, requiring redundant table rebuilds and making it difficult to track the impact of data changes.
Solution
Y42 offers a turnkey data orchestration platform that provides a unified space to build, monitor, and maintain robust data pipelines. The platform eliminates the need for third-party adapters by offering native compatibility across all pipeline stages, from ingestion to transformation and testing. Built-in monitoring capabilities provide end-to-end visibility, enabling users to quickly troubleshoot data issues. Y42 versions both code and data, allowing teams to reuse tables created in other branches and avoid redundant computations.
Target Audience
Y42 is designed for data practitioners and data teams who want to streamline their data workflows and reduce the overhead of managing complex data pipelines.
Features
- Asset-based orchestrator with a standard configuration schema across all pipeline steps
- Native compatibility with Airbyte, Fivetran, dbt core, and Python
- Data dependency linkages to visualize the impact of data changes and troubleshoot issues
- Git-based version control for code and data, enabling collaboration and reuse of tables
- Built-in monitoring capabilities for end-to-end visibility of data pipelines
- Virtual data builds for testing in isolated environments before merging changes
- Integration with Google BigQuery and Snowflake data warehouses