Snowflake
Integrate Sifflet with Snowflake to unlock field-level lineage, enrich metadata, and gain actionable insights for a more effective data observability strategy.
Snowflake-specific assets
Sifflet supports multiple Snowflake-specific objects, like streams and stages, for exhaustive coverage.
Usage and Snowflake metadata
Get detailed statistics about the usage of your Snowflake assets, in addition to various metadata (like tags, descriptions, and table sizes) retrieved directly from Snowflake.
Field-level lineage
Have a detailed understanding of how data flows through your platform via field-level end-to-end lineage for Snowflake.
Go Further
Data Observability for your data stack with Snowflake
Discover the Core Capabilities That Ensure Data Quality in Snowflake
Do You need Data Observability?
Every data-driven team dreams of creating "fireworks" with their insights. But without control, the "fire" of the modern data stack can spark chaos.Discover if you're fighting fires or igniting fireworks:
Frequently asked questions
Data-quality-as-code (DQaC) allows you to programmatically define and enforce data quality rules using code. This ensures consistency, scalability, and better integration with CI/CD pipelines. Read more here to find out how to leverage it within Sifflet
Yes, Sifflet leverages AI to enhance data observability with features like anomaly detection and predictive insights. This ensures your data systems remain resilient and can support advanced analytics and AI-driven initiatives. Have a look at how Sifflet is leveraging AI for better data observability here
AI enhances data observability with advanced anomaly detection, predictive analytics, and automated root cause analysis. This helps teams identify and resolve issues faster while reducing manual effort. Have a look at how Sifflet is leveraging AI for better data observability here
Data observability ensures data governance policies are adhered to by tracking data usage, quality, and lineage. It provides the transparency needed for accountability and compliance. Read more here.
Yes! While smaller organizations may have fewer data pipelines, ensuring data quality and reliability is equally important for making accurate decisions and scaling effectively. What really matters is the data stack maturity and volume of data. Take our test here to find out if you really need data observability.
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