Google BigQuery
Integrate Sifflet with BigQuery to monitor all table types, access field-level lineage, enrich metadata, and gain actionable insights for an optimized data observability strategy.




Metadata-based monitors and optimized queries
Sifflet leverages BigQuery's metadata APIs and relies on optimized queries, ensuring minimal costs and efficient monitor runs.


Usage and BigQuery metadata
Get detailed statistics about the usage of your BigQuery assets, in addition to various metadata (like tags, descriptions, and table sizes) retrieved directly from BigQuery.
Field-level lineage
Have a complete understanding of how data flows through your platform via field-level end-to-end lineage for BigQuery.


External table support
Sifflet can monitor external BigQuery tables to ensure the quality of data in other systems like Google Cloud BigTable and Google Cloud Storage


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Frequently asked questions
How did Carrefour improve data reliability across its global operations?
Carrefour enhanced data reliability by adopting Sifflet's AI-augmented data observability platform. This allowed them to implement over 3,000 automated data quality checks and monitor more than 1,000 core business tables, ensuring consistent and trustworthy data across teams.
What role do tools like Apache Spark and dbt play in data transformation?
Apache Spark and dbt are powerful tools for managing different aspects of data transformation. Spark is great for large-scale, distributed processing, especially when working with complex transformations and high data volumes. dbt, on the other hand, brings software engineering best practices to SQL-based transformations, making it ideal for analytics engineering. Both tools benefit from integration with observability platforms to ensure transformation pipelines run smoothly and reliably.
How does Sifflet’s Freshness Monitor scale across large data environments?
Sifflet’s Freshness Monitor is designed to scale effortlessly. Thanks to our dynamic monitoring mode and continuous scan feature, you can monitor thousands of data assets without manually setting schedules. It’s a smart way to implement data pipeline monitoring across distributed systems and ensure SLA compliance at scale.
How can organizations improve data governance with modern observability tools?
Modern observability tools offer powerful features like data lineage tracking, audit logging, and schema registry integration. These capabilities help organizations improve data governance by providing transparency, enforcing data contracts, and ensuring compliance with evolving regulations like GDPR.
How does reverse ETL improve data reliability and reduce manual data requests?
Reverse ETL automates the syncing of data from your warehouse to business apps, helping reduce the number of manual data requests across teams. This improves data reliability by ensuring consistent, up-to-date information is available where it’s needed most, while also supporting SLA compliance and data automation efforts.
How does Sifflet support data lineage tracking and governance?
Sifflet’s unified data catalog and observability features bring context-rich insights into your data workflows. This integration enhances data lineage tracking and supports stronger data governance by giving teams a holistic view of how data flows and transforms across your systems.
Who benefits from implementing a data observability platform like Sifflet?
Honestly, anyone who relies on data to make decisions—so pretty much everyone. Data engineers, BI teams, data scientists, RevOps, finance, and even executives all benefit. With Sifflet, teams get proactive alerts, root cause analysis, and cross-functional visibility. That means fewer surprises, faster resolutions, and more trust in the data that powers your business.
How does Sifflet's ServiceNow integration help with incident response automation?
Great question! With our new ServiceNow integration, Sifflet can automatically create incidents from any data alert, helping your team respond faster and stay on top of critical issues. It's a big win for incident response automation and keeps your data observability workflows smooth and efficient.
























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