Co-Founder/Managing Partner
Matia
Matia provides a unified DataOps platform that integrates data ingestion, reverse ETL, observability, and cataloging to streamline data management processes. This solution addresses the challenges of siloed data systems, enabling teams to enhance data accuracy and accelerate insights while reducing operational costs by up to 40%.
- Artificial Intelligence
- Data & Analytics
- Enterprise Software
- Software Only
Updated 23 months ago
Funding
Raised to date
$10.5MRaised 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.
Funding rounds are not available yet.
Founders
Product
Problem
Data teams often struggle with siloed data systems and the complexity of managing multiple tools for data ingestion, transformation, observability, and activation. This fragmented approach leads to inefficiencies, increased operational costs, and difficulties in ensuring data accuracy and reliability.
Solution
Matia offers a unified DataOps platform that integrates data ingestion, reverse ETL, data cataloging, and observability into a single solution. By consolidating these functions, Matia eliminates data silos and streamlines data management processes, enabling data teams to move faster and more reliably. The platform provides a single source of truth for data, enhancing data accuracy and governance while reducing the need for multiple disparate tools. Matia's unified approach allows teams to focus on deriving actionable insights and accelerating innovation, rather than spending time fixing broken and inconsistent data pipelines.
Target Audience
Matia is designed for data, AI, and engineering teams seeking to streamline their data operations, improve data accuracy, and accelerate insights.
Features
- Unified platform integrating data ingestion, reverse ETL, data catalog, and observability
- Support for 100+ integrations to connect various data sources and destinations
- Automated data quality checks at ingestion and reverse ETL stages for early error detection
- End-to-end data lineage tracking at the column and table level
- Operational lineage with run history and error logs
- dbt cataloging with models, sources, tests, and ownership
- Data certification and ownership features to mark trusted data assets
- Real-time engineering and data support