Shared Understanding. Ultimate Confidence. At Scale.
When everyone knows your data is systematically validated for quality, understands where it comes from and how it's transformed, and is aligned on freshness and SLAs, what’s not to trust?


Always Fresh. Always Validated.
No more explaining data discrepancies to the C-suite. Thanks to automatic and systematic validation, Sifflet ensures your data is always fresh and meets your quality requirements. Stakeholders know when data might be stale or interrupted, so they can make decisions with timely, accurate data.
- Automatically detect schema changes, null values, duplicates, or unexpected patterns that could comprise analysis.
- Set and monitor service-level agreements (SLAs) for critical data assets.
- Track when data was last updated and whether it meets freshness requirements

Understand Your Data, Inside and Out
Give data analysts and business users ultimate clarity. Sifflet helps teams understand their data across its whole lifecycle, and gives full context like business definitions, known limitations, and update frequencies, so everyone works from the same assumptions.
- Create transparency by helping users understand data pipelines, so they always know where data comes from and how it’s transformed.
- Develop shared understanding in data that prevents misinterpretation and builds confidence in analytics outputs.
- Quickly assess which downstream reports and dashboards are affected


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Frequently asked questions
What role does data lineage play in incident management and alerting?
Data lineage provides visibility into data dependencies, which helps teams assign, prioritize, and resolve alerts more effectively. In an observability platform like Sifflet, this means faster incident response, better alert correlation, and improved on-call management workflows.
Why is embedding observability tools at the orchestration level important?
Embedding observability tools like Flow Stopper at the orchestration level gives teams visibility into pipeline health before data hits production. This kind of proactive monitoring is key for maintaining data reliability and reducing downtime due to broken pipelines.
How does the Model Context Protocol (MCP) improve data observability with LLMs?
Great question! MCP allows large language models to access structured external context like pipeline metadata, logs, and diagnostics tools. At Sifflet, we use MCP to enhance data observability by enabling intelligent agents to monitor, diagnose, and act on issues across complex data pipelines in real time.
Can I define data quality monitors as code using Sifflet?
Absolutely! With Sifflet's Data-Quality-as-Code (DQaC) v2 framework, you can define and manage thousands of monitors in YAML right from your IDE. This Everything-as-Code approach boosts automation and makes data quality monitoring scalable and developer-friendly.
What kind of alerts can I expect from Sifflet when using it with Firebolt?
With Sifflet, you’ll receive real-time alerts for any data quality issues detected in your Firebolt warehouse. These alerts are powered by advanced anomaly detection and data freshness checks, helping you stay ahead of potential problems.
How does data transformation impact SLA compliance and data reliability?
Data transformation directly influences SLA compliance and data reliability by ensuring that the data delivered to business users is accurate, timely, and consistent. With proper data quality monitoring in place, organizations can meet service level agreements and maintain trust in their analytics outputs. Observability tools help track these metrics in real time and alert teams when issues arise.
How does data observability help improve data reliability?
Data observability gives you end-to-end visibility into your data pipelines, helping you catch issues like schema changes, data drift, or ingestion failures before they impact downstream systems. By continuously monitoring real-time metrics and enabling root cause analysis, observability platforms like Sifflet ensure your data stays accurate, complete, and up-to-date, which directly supports stronger data reliability.
How does Sifflet support traceability across diverse data stacks?
Traceability is a key pillar of Sifflet’s observability platform. We’ve expanded support for tools like Synapse, MicroStrategy, and Fivetran, and introduced our Universal Connector to bring in any asset, even from AI models. This makes root cause analysis and data lineage tracking more comprehensive and actionable.












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