A great distinction! Storage platforms hold your actual data, while a data catalog helps you understand what that data means. Sifflet connects both, so when we detect an anomaly, the catalog tells you what business process is affected and who should be notified. It’s how we turn raw telemetry into actionable insights for better incident response automation and SLA compliance.

Reverse ETL automates the syncing of data from your warehouse to business apps, helping reduce the number of manual data requests across teams. This improves data reliability by ensuring consistent, up-to-date information is available where it’s needed most, while also supporting SLA compliance and data automation efforts.

At Sifflet, we believe data quality isn't one-size-fits-all. Our observability platform blends technical robustness with business context, offering customized data quality monitoring that adapts to your specific use cases. This means you get both reliable pipelines and meaningful metrics that align with your business goals.

Full Data Stack Observability gives you end-to-end visibility into your data pipeline, from ingestion to consumption. It enables real-time anomaly detection, root cause analysis, and proactive alerts, helping you catch and resolve issues before they affect your dashboards or reports. It's a game-changer for organizations looking to scale data quality efforts efficiently.

Data observability integrates across your existing data stack, from ingestion tools like Airflow and AWS Glue to storage solutions like Snowflake and Redshift. It acts as a monitoring layer that provides real-time insights and alerts across each stage, helping teams maintain pipeline health and ensure data freshness checks are always in place.

Data lineage is essential for GDPR because it helps you trace personal data from source to destination. This means you can see where PII is stored, how it flows through your data pipelines, and which reports or applications use it. With this visibility, you can manage deletion requests, audit data usage, and ensure data governance policies are enforced consistently.

When evaluating observability tools, consider factors like ease of setup, support for real-time metrics, data freshness checks, and integration with your existing stack. Look for platforms that offer strong data pipeline monitoring, business context in alerts, and cost transparency. Tools like Sifflet also provide fast time-to-value and support for both batch and streaming data observability.