Sifflet makes data quality monitoring scalable with features like auto-coverage, which automatically generates monitors across your datasets. Whether you're working with Snowflake, BigQuery, or other platforms, you can quickly reach high monitoring coverage and get real-time alerts via Slack, email, or MS Teams to ensure data reliability.

Real-time metrics are essential for proactive data pipeline monitoring. Sifflet’s observability tools provide real-time alerts and anomaly detection, helping teams quickly identify and resolve issues before they impact downstream systems or violate SLA compliance.

Great question! AI doesn't usually fail because of bad models, but because of unreliable data. Without strong data observability in place, it's hard to detect data issues like schema changes, stale tables, or broken pipelines. These problems undermine trust, and without trust in your data, even the best models can't deliver value.

Data monitoring focuses on detecting issues like failed jobs or freshness violations, often after the fact. Data observability, on the other hand, provides real-time metrics, proactive alerts, and end-to-end visibility into your data pipelines. With Sifflet’s observability platform, you don’t just monitor—you understand, troubleshoot, and continuously improve your data operations.

When selecting a data observability tool, consider your data stack, team size, and specific needs like anomaly detection, metrics collection, or schema registry integration. Whether you're looking for open source observability options or a full-featured commercial platform, make sure it supports your ecosystem and scales with your data operations.

Datadog is a strong choice for infrastructure and system observability, but it may not be the best fit for teams focused on data reliability and data governance. While it offers some data quality monitoring through Metaplane, it lacks the business context and advanced data lineage tracking needed to ensure trust in your analytics. For those priorities, a dedicated data observability platform like Sifflet is better equipped.

At Sifflet, we're integrating advanced AI models into our observability platform to enhance data quality monitoring and anomaly detection. Marie, our Machine Learning Engineer, has been instrumental in building intelligent systems that automatically detect issues across data pipelines, making it easier to maintain data reliability in real time.