No, Sifflet does not store your data. We designed our platform to discard any data previews immediately after display, and we only retain metadata like table and column names. This approach supports GDPR compliance and strengthens your overall data governance strategy.

Sifflet is built with data governance in mind. Our platform offers robust data lineage tracking, audit logging, and anomaly detection features that help enforce data contracts and monitor for compliance issues like GDPR violations. By providing full transparency into your data pipelines, Sifflet helps you maintain trust and accountability across your data ecosystem.

Absolutely! Our new dbt tab surfaces custom metadata defined in your dbt models, which you can leverage for better data governance and data profiling. It’s all about giving you the flexibility to manage your data assets exactly the way you need.

Popular ingestion tools like Fivetran, Stitch, and Apache Kafka integrate well with cloud data observability platforms. They offer strong support for telemetry instrumentation, real-time ingestion, and schema registry integration. Pairing them with observability tools ensures your data stays reliable and actionable across your entire stack.

Sifflet is designed to monitor subtle changes in data distributions, which is key for data drift detection. This helps teams catch shifts in data that could negatively impact AI model performance. By continuously analyzing incoming data and comparing it to historical patterns, Sifflet ensures your models stay aligned with the most relevant and reliable inputs.

A data observability platform continuously monitors inventory data using real-time metrics and anomaly detection. It compares RFID scans with POS transactions, flags inconsistencies, and tracks key inventory KPIs. This helps retailers maintain more accurate stock levels and reduce shrinkage or overstocking.

Great question! Data observability plays a crucial role in AI governance by helping teams continuously monitor model behavior, detect data drift or concept drift, and ensure outputs remain fair and explainable. With tools like data lineage tracking and real-time metrics, observability helps verify that AI systems operate within approved policies, making governance not just a policy but a practice.

Adaptavist used Sifflet’s observability platform to map the blast radius of changes, alert users before issues occurred, and validate results pre-production. This proactive approach to data pipeline monitoring helped them eliminate downtime during a major refactor and shift from 'merge and pray' to a risk-aware, observability-first workflow.