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Tame %%your%% Snowflake stack.
The Offer (valid through Jan 31st, 2026) $30,000 / 12 months (normally $85K - a 65% discount)
Purchase via Snowflake Marketplace or AWS PPA (no PO, no onboarding delays).
Includes:
- Up to 500 assets + unlimited users
- Enterprise Edition features at Entry price, including: SSO, Snowflake Data Sharing, Pipeline Monitoring, AI Agents
Pre-configured 12-month SKU for 1-click private offer acceptance.
Unlockable via unused Snowflake credits.
First 20 customers only.
Activate it now by filling your details below













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Frequently asked questions
Why is data observability so important for AI-powered organizations in 2025?
Great question! As AI continues to evolve, the quality and reliability of the data feeding those models becomes even more critical. Data observability ensures that your AI systems are powered by clean, accurate, and up-to-date data. With platforms like Sifflet, organizations can detect issues like data drift, monitor real-time metrics, and maintain data governance, all of which help AI models stay accurate and trustworthy.
What can I expect from Sifflet at Big Data Paris 2024?
We're so excited to welcome you at Booth #D15 on October 15 and 16! You’ll get to experience live demos of our latest data observability features, hear real client stories like Saint-Gobain’s, and explore how Sifflet helps improve data reliability and streamline data pipeline monitoring.
Can I learn about real-world results from Sifflet customers at the event?
Yes, definitely! Companies like Saint-Gobain will be sharing how they’ve used Sifflet for data observability, data lineage tracking, and SLA compliance. It’s a great chance to hear how others are solving real data challenges with our platform.
What role did data observability play in improving Meero's data reliability?
Data observability was key to Meero's success in maintaining reliable data pipelines. By using Sifflet’s observability platform, they could monitor data freshness, schema changes, and volume anomalies, ensuring their data remained trustworthy and accurate for business decision-making.
Which industries or use cases benefit most from Sifflet's observability tools?
Our observability tools are designed to support a wide range of industries, from retail and finance to tech and logistics. Whether you're monitoring streaming data in real time or ensuring data freshness in batch pipelines, Sifflet helps teams maintain high data quality and meet SLA compliance goals.
What role does data lineage tracking play in volume monitoring?
Data lineage tracking is essential for root cause analysis when volume anomalies occur. It helps you trace where data came from and how it's been transformed, so if a volume drop happens, you can quickly identify whether it was caused by a failed API, upstream filter, or schema change. This context is key for effective data pipeline monitoring.
Can non-technical users benefit from Sifflet’s data observability platform?
Absolutely. Sifflet is designed to be accessible to everyone. With an intuitive UI and our AI Assistant, even non-technical users can set up data quality monitors, track real-time metrics, and contribute to data governance without writing a line of code.
How did Sifflet help reduce onboarding time for new data team members at jobvalley?
Sifflet’s data catalog provided a clear and organized view of jobvalley’s data assets, making it much easier for new team members to understand the data landscape. This significantly cut down onboarding time and helped new hires become productive faster.






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