The MCP (Model Context Protocol) Server is a new interface that lets you interact with Sifflet directly from your development environment. It's designed to make data observability more seamless by allowing you to query assets, review incidents, and trace data lineage without leaving your IDE or notebook. This helps streamline your workflow and gives you real-time visibility into pipeline health and data quality.

Business teams can access data quality insights directly within their BI dashboards, reducing their reliance on data engineers. This democratizes data observability and empowers teams to make confident, data-driven decisions with full transparency into data lineage and reliability.

When you're selling data, trust is everything. Data observability ensures your data is accurate, fresh, and traceable, which builds client confidence. Carrefour, for example, used observability to monitor over 800 assets and enforce data quality across 8 countries, making their data products reliable and revenue-generating at scale.

For Meero, having an observability layer like Sifflet was crucial to ensure end-to-end visibility of their data pipelines. It allowed them to proactively monitor data quality, reduce downtime, and maintain SLA compliance, making it an indispensable part of their modern data stack.

Sifflet collects and monitors real-time metrics like data freshness, schema changes, and volume anomalies. With dynamic thresholding and real-time alerts via Slack or email, teams can respond quickly and keep their analytics platform running smoothly.

Great question! With our new ServiceNow integration, Sifflet can automatically create incidents from any data alert, helping your team respond faster and stay on top of critical issues. It's a big win for incident response automation and keeps your data observability workflows smooth and efficient.

Great question! With our latest dbt integration update, we’ve unified dbt models and the datasets they generate into a single asset. This means you get richer context and better visibility across your data pipelines, making it easier to track data lineage, monitor data quality, and ensure SLA compliance all from one place.

Data observability helps organizations ensure data quality, monitor pipelines in real time, and build trust in their data. At Big Data LDN, we’ll share how companies like Penguin Random House use observability tools to improve data governance and drive better decisions.