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Fireworks, not fires: A Guide to Assessing your Need for Data Observability –
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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.
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.
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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 empowers our teams through Centralized Data Visibility"

"Having the visibility of our DBT transformations combined with full end-to-end data lineage in one central place in Sifflet is so powerful for giving our data teams confidence in our data, helping to diagnose data quality issues and unlocking an effective data mesh for us at BBC Studios"

Ross Gaskell
Software engineering manager, BBC Studios

"Sifflet allows us to find and trust our data"

"Sifflet has transformed our data observability management at Carrefour Links. Thanks to Sifflet's proactive monitoring, we can identify and resolve potential issues before they impact our operations. Additionally, the simplified access to data enables our teams to collaborate more effectively."

Mehdi Labassi
CTO, Carrefour Links

"A core component of our data strategy and transformation"

"Using Sifflet has helped us move much more quickly because we no longer experience the pain of constantly going back and fixing issues two, three, or four times."

Sami Rahman
Director of Data, Hypebeast
Read the customer story