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

How can I monitor the health of my ingestion pipelines?

To keep your ingestion pipelines healthy, it's best to use observability tools that offer features like pipeline health dashboards, data quality monitoring, and anomaly detection. These tools provide visibility into data flow, alert you to schema drift, and help with root cause analysis when issues arise.

Can Sifflet support SLA compliance and data governance goals?

Absolutely! Sifflet supports SLA compliance through proactive data quality monitoring and real-time metrics. Its deep metadata integrations and lineage tracking also help organizations enforce data governance policies and maintain trust across the entire data ecosystem.

Is this feature scalable for large datasets and multiple data assets?

Yes, it is! With Sifflet’s auto-coverage and observability tools, you can monitor distribution deviation at scale with just a few clicks. Whether you're working with batch data observability or streaming data monitoring, Sifflet has you covered with automated, scalable insights.

Why is metadata observability so important in an Open Data Stack?

In an Open Data Stack, metadata acts as the new control plane, guiding how different engines interpret and interact with your data. Without active metadata observability, you're at risk of schema drift, catalog mismatches, and invisible data errors. Sifflet helps you stay ahead by continuously monitoring metadata changes and ensuring data reliability across your stack.

How does Sifflet support data lineage tracking and context enrichment?

Sifflet enhances your data catalog with lineage tracking and context by incorporating dbt model descriptions, input-output dataset views, and AI-powered recommendations. This enrichment helps users quickly understand where data comes from and how it's used, making it easier to trust and leverage data confidently.

How does Sifflet help with end-to-end data observability?

Sifflet enhances end-to-end data observability by allowing you to declare any asset in your data stack, including custom applications and scripts. This ensures full visibility into your data pipelines and supports comprehensive data lineage tracking and root cause analysis.

Can data observability improve collaboration across data teams?

Absolutely! With shared visibility into data flows and transformations, observability platforms foster better communication between data engineers, analysts, and business users. Everyone can see what's happening in the pipeline, which encourages ownership and teamwork around data reliability.

How does Sifflet’s dbt Impact Analysis improve data pipeline monitoring?

By surfacing impacted tables, dashboards, and other assets directly in GitHub or GitLab, Sifflet’s dbt Impact Analysis gives teams real-time visibility into how changes affect the broader data pipeline. This supports better data pipeline monitoring and helps maintain data reliability.

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