Traditional data catalogs focus mainly on metadata management, but they don't actively assess data quality or track changes in real time. As data environments grow more complex, teams need more than just an inventory. They need data observability tools that provide real-time metrics, anomaly detection, and data quality monitoring to ensure reliable decision-making.

An end-to-end data platform includes layers for ingestion, storage, transformation, orchestration, governance, observability, and analytics. Each part plays a role in making data reliable and actionable. For example, data lineage tracking and real-time metrics collection help ensure transparency and performance across the pipeline.

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.

Sifflet's dbt runs tab offers deep insights into model execution, cost, and runtime, making it easy to spot inefficiencies. You can also use historical performance data to set up custom dashboards and proactive monitors. This helps with capacity planning and ensures your data pipelines stay optimized and cost-effective.

When a data catalog integrates seamlessly with your databases, cloud storage, and data lakes, it enhances your ability to monitor data pipelines in real time. This connectivity supports better ingestion latency tracking and helps maintain a reliable observability platform.

Our Head of Product, Martin Zerbib, will walk you through how Sifflet enables data lineage tracking, real-time metrics, and data profiling at scale. You’ll get a sneak peek at our roadmap and see how we’re making data more accessible and reliable for teams of all sizes.

Shippeo uses Sifflet’s data observability platform to monitor every stage of their data pipelines. By implementing raw data monitoring, intermediate layer checks, and front-facing metric validation, they catch issues early and maintain trust in their real-time supply chain visibility tools.

A metadata control plane brings together technical metrics and business context by leveraging metadata across your stack. This enables better decision-making, reduces alert fatigue, and supports SLA compliance by giving teams a single source of truth for pipeline health and data reliability.