Solution by Platform

BigQuery

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.

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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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Data Observability for your data stack with Google BigQuery

Optimize Data Quality in BigQuery with Actionable Insights for Trust and Efficiency

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Sifflet’s AI Helps Us Focus on What Moves the Business

What impressed us most about Sifflet’s AI-native approach is how seamlessly it adapts to our data landscape — without needing constant tuning. The system learns patterns across our workflows and flags what matters, not just what’s noisy. It’s made our team faster and more focused, especially as we scale analytics across the business.

Simoh-Mohamed Labdoui

Head of Data

"Enabler of Cross Platform Data Storytelling"

"Sifflet has been a game-changer for our organization, providing full visibility of data lineage across multiple repositories and platforms. The ability to connect to various data sources ensures observability regardless of the platform, and the clean, intuitive UI makes setup effortless, even when uploading dbt manifest files via the API. Their documentation is concise and easy to follow, and their team's communication has been outstanding—quickly addressing issues, keeping us informed, and incorporating feedback. "

Callum O'Connor

Senior Analytics Engineer, The Adaptavist

"Building Harmony Between Data and Business With Sifflet"

"Sifflet serves as our key enabler in fostering a harmonious relationship with business teams. By proactively identifying and addressing potential issues before they escalate, we can shift the focus of our interactions from troubleshooting to driving meaningful value. This approach not only enhances collaboration but also ensures that our efforts are aligned with creating impactful outcomes for the organization."

Sophie Gallay

Data & Analytics Director, Etam

" 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

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Frequently asked questions

Who are some of the companies using Sifflet’s observability tools?

We're proud to work with amazing organizations like St-Gobain, Penguin Random House, and Euronext. These enterprises rely on Sifflet for cloud data observability, data lineage tracking, and proactive monitoring to ensure their data is always AI-ready and analytics-friendly.

How often is the data refreshed in Sifflet's Data Sharing pipeline?

The data shared through Sifflet's optimized pipeline is refreshed every four hours. This ensures you always have timely and accurate insights for data quality monitoring, anomaly detection, and root cause analysis within your own platform.

How can organizations create a culture that supports data observability?

Fostering a data-driven culture starts with education and collaboration. Salma recommends training programs that boost data literacy and initiatives that involve all data stakeholders. This shared responsibility approach ensures better data governance and more effective data quality monitoring.

How does data observability improve data contract enforcement?

Data observability adds critical context that static contracts lack, such as data lineage tracking, real-time usage patterns, and anomaly detection. With observability tools, teams can proactively monitor contract compliance, detect schema drift early, and ensure SLA compliance before issues impact downstream systems. It transforms contracts from documentation into enforceable, living agreements.

What role does data quality monitoring play in a data catalog?

Data quality monitoring ensures your data is accurate, complete, and consistent. A good data catalog should include profiling and validation tools that help teams assess data quality, which is crucial for maintaining SLA compliance and enabling proactive monitoring.

Why is a data catalog essential for modern data teams?

A data catalog is critical because it helps teams find, understand, and trust their data. It centralizes metadata, making data assets searchable and understandable, which reduces duplication, speeds up analytics, and supports data governance. When paired with data observability tools, it becomes a powerful foundation for proactive data management.

Can the Sifflet AI Assistant help non-technical users with data quality monitoring?

Absolutely! One of our goals is to democratize data observability. The Sifflet AI Assistant is designed to be accessible to both technical and non-technical users, offering natural language interfaces and actionable insights that simplify data quality monitoring across the organization.

How do I ensure SLA compliance during a cloud migration?

Ensuring SLA compliance means keeping a close eye on metrics like throughput, resource utilization, and error rates. A robust observability platform can help you track these metrics in real time, so you stay within your service level objectives and keep stakeholders confident.

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