Bruin is an online platform that enables data analysts to transform and share data using SQL and Python without the need for custom coding, while ensuring data quality through built-in checks and monitoring. It simplifies the creation of reliable data pipelines by automating data ingestion, transformation, and lineage tracking across various databases.
Funding
$20K 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 analysts often face challenges in building and maintaining reliable data pipelines due to the complexity of integrating various data sources, transforming data using different languages, and ensuring data quality. This process typically requires custom coding and manual effort, leading to increased development time and potential errors. The lack of automated data lineage tracking also makes it difficult to identify the impact of changes and dependencies between teams.
Solution
Bruin is a unified analytics platform that simplifies data engineering by providing built-in operators to ingest, transform, and share data across various databases. The platform parses Git repositories to automatically build data pipelines from SQL and Python files, eliminating the need for extensive custom coding. Bruin incorporates data quality checks, allowing users to define custom SQL checks to ensure data accuracy. The platform also automates data lineage tracking, providing a unified view of data dependencies and enabling users to identify the impact of changes across teams.
Target Audience
Bruin is designed for data analysts and data engineers who need to build and maintain reliable data pipelines without the complexity of custom coding and manual processes.
Features
- Built-in operators for copying data between various sources and destinations.
- Automated data pipeline creation from SQL and Python files stored in Git repositories.
- Support for custom SQL-based data quality checks with alerting.
- Automated data lineage tracking across platforms, from raw data to final reports.
- Cost and usage tracking at the asset level for cloud data warehouses.
- Native Python support for running ML and AI workloads within data pipelines.
- Blocking by default to prevent bad data from being used by downstream assets.