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Key Features
AI Agents for Data ObservabilityData Catalog with intelligent metadataData Quality Monitoring (out-of-the-box and custom)Automated Monitoring Coverage with AI recommendationsAnomaly Detection (freshness, volume, schema, distribution issues)Data Lineage (upstream, downstream, across data layers)Built-in SecurityLineage-based triage and diagnosis
Sifflet is an AI-augmented data observability platform designed to help data teams ensure the reliability and quality of their data, while also empowering business users with trustworthy insights. It addresses common pain points like data engineers spending significant time on reliability tasks and analysts vetting data quality, by automating monitoring, detection, and resolution of data issues.
The platform integrates a data catalog, data quality monitoring, and data lineage capabilities, all augmented by AI. This allows users to explore, understand, and trust their data instantly by providing full context on every asset, including lineage, quality signals, schema changes, and usage. Sifflet aims to supercharge productivity, uplevel data reliability, and empower data ownership, ultimately removing obstacles to superior insights and value from data.
Sifflet is an AI-augmented data observability platform that helps data teams monitor, detect, and resolve data quality and reliability issues. It combines a data catalog, data quality monitoring, and data lineage to provide a comprehensive view of data health and ensure data trustworthiness.
How much does Sifflet cost?
Pricing information is not publicly available on the website. Users are encouraged to contact Sifflet for a demo or to get in touch for pricing details.
Is Sifflet free?
Based on the available information, Sifflet does not appear to offer a free tier. It is a paid enterprise solution, and interested parties are directed to contact them for product tours and demos.
Who is Sifflet for?
Sifflet is designed for data leaders, data engineers, and data platform teams who need to ensure data reliability, improve data quality, and enable data governance. It also benefits data users and analysts by providing trustworthy data for their insights and products.