Great question! By integrating with dbt Core, Sifflet enhances data observability across your entire data stack. It helps you monitor dbt test coverage, map tests to downstream dependencies using data lineage tracking, and consolidate metadata like tags and descriptions, all in one place.

We're excited because MCP allows us to build intelligent, context-aware agents that go beyond alerts. With MCP, our observability tools can now support real-time metrics analysis, dynamic thresholding, and even automated remediation. It’s a huge step forward in delivering reliable and scalable data observability.

Sifflet consolidates metadata from your entire data stack into a centralized Data Catalog, making it easier for data stakeholders to discover, understand, and trust data. With features like enriched metadata, Snowflake tags, and BigQuery labels, data discovery becomes faster and more intuitive, reducing time spent searching for the right assets.

A data observability platform is a system that continuously monitors the health and reliability of your data pipelines. It helps you detect issues like schema changes, volume drops, or stale data before they impact business decisions. By combining technical telemetry with business context, platforms like Sifflet ensure data trust across the entire organization.

In sectors like financial services, insurance, and logistics, data reliability isn't just a technical concern, it's a compliance and operational necessity. A single data incident can lead to regulatory risks or business disruption. That's why data observability platforms like Sifflet are being adopted to ensure data quality, monitor pipelines in real time, and maintain SLA compliance.

Great question! This funding round, led by EQT Ventures, allows us to double down on our mission to make data more reliable and trustworthy. With this investment, we're expanding our data observability platform, enhancing real-time monitoring capabilities, and growing our presence in EMEA and the US.

Acceldata integrates cost observability into its platform, giving you predictive insights into cloud spend, storage usage, and compute performance. This proactive monitoring helps teams plan capacity better and avoid unexpected overages, making it a great choice for enterprises juggling both data reliability and budget constraints.

Data governance ensures that data is collected, managed, and shared responsibly, which is especially important when data is treated as a product. It helps maintain compliance with regulations and supports data quality monitoring. With proper governance in place, businesses can confidently deliver reliable and secure data products.