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Solutions by platform

Datalake and warehouse support

Platforms

Snowflake

Sifflet supports Snowflake-only features such as stages, time travel, and streams, giving more data lineage insights and improving data quality monitoring.

How it Works

Stages
  • Stages often serve as an intermediate storage area that allows you to efficiently move data between Snowflake and external systems. Sifflet resurfaces the stages and external tables in the lineage, providing more insights on the origin of the data.
Time travel
  • This feature enables users to access historical data from a defined period. By analyzing and capturing the changes in the data over time, Sifflet can leverage more data points to train its machine learning models for monitoring purposes. As a result, this enhances and accelerates Sifflet's monitoring capabilities, leading to improved performance and effectiveness.
Platforms

Google BigQuery

Sifflet leverages specific Google Big Query features to deepen observability of the data within Google Big Query.

How it Works

BigQuery metadata
  • Metadata enrichment with automated tagging and description, lineage computation with upstream and downstream systems, and actionable data assets monitoring
BigQuery optimization capabilities
  • Nested and repeated fields or data partitions, to deliver the data observability while ensuring data model performance.
External table support
  • This includes Google Cloud BigTable, Google Cloud Storage and Google Drive for end-to-end lineage and actionable troubleshooting.
Platforms

Databricks

Sifflet leverages specific Databricks features to deepen observability of the data within Databricks.

How it Works

Check back for more information on platforms with Sifflet soon.

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CUSTOMER STORIES

Sifflet ticked all of our boxes

In addition to monitoring the quality and accuracy of the data assets, we were also looking to ensure the reliability of the pipelines, observe schema changes and other metadata-related metrics.

Laurent Tachet des Combes
Head of Data, Meero
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With Sifflet we know what to do once the rule fails

We chose Sifflet for its wide offering. We’ve checked out other vendors from the space, and they have all data quality rules, but what we need is the next step. Being able to know what to do once that rule fails.

Logo NextBite
Ross Serven
Director of Data Engineering, Nextbite
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Sifflet allows you to find and trust your data

It is very convenient to have the data quality feature together with the data catalog. I think it’s a great combination: the data catalog allows you to find all the data you need and the data quality feature monitors our data flows, ensuring that the data we use is reliable at all times.

Marco Kleine-Böhme
VP Data & Analytics, jobvalley
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