Great question! Investing in data observability helps organizations proactively monitor the health of their data, reduce the risk of bad data incidents, and ensure data quality across pipelines. It also supports better decision-making, improves SLA compliance, and helps maintain trust in analytics. Ultimately, it’s a strategic move that protects your business from costly mistakes and missed opportunities.

Great question! Sifflet acts as a unified observability platform that consolidates metadata from tools like Snowflake and BigQuery into one centralized Data Catalog. By surfacing tags, labels, and schema details, it makes data discovery and governance much easier for all stakeholders.

With data stacks growing rapidly and teams staying the same size or shrinking, proactive monitoring is more important than ever. These AI agents bring memory, reasoning, and automation into the observability platform, helping teams scale their efforts with confidence and clarity.

Retailers can track a wide range of metrics such as inventory health, stock obsolescence risks, carrying costs, and dynamic safety stock levels. These observability dashboards offer time-series analysis and predictive insights that support better decision-making and improve overall data reliability.

Yes it is! You can deploy Sifflet’s observability platform within your own private Google Cloud environment using VPC deployment, giving you full control over data governance and security.

We're proud to work with amazing organizations like St-Gobain, Penguin Random House, and Euronext. These enterprises rely on Sifflet for cloud data observability, data lineage tracking, and proactive monitoring to ensure their data is always AI-ready and analytics-friendly.

Adaptavist built a platform health dashboard that scores each asset based on data freshness, quality, and reliability. This kind of data profiling helps them prioritize fixes, improve root cause analysis, and ensure continued trust in their analytics pipeline observability.

Sifflet integrates directly with your CI pipelines on platforms like GitHub and GitLab to proactively surface issues before code is merged. By analyzing the impact of dbt model changes and running data quality monitors in testing environments, Sifflet ensures data reliability and minimizes production disruptions.