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 does Sifflet help with SLA compliance for business metrics?

By combining real-time metrics monitoring with proactive alerts and incident management workflows, Sifflet helps teams stay on top of SLA compliance. Users can track metrics freshness, detect anomalies, and take action before SLA breaches occur.

Can I use Sifflet to detect issues in my dbt models before they impact downstream dashboards?

Absolutely! Sifflet's real-time anomaly detection and full data lineage tracking make it easy to catch issues in your dbt models early. This proactive approach helps prevent broken dashboards and ensures data reliability across your analytics pipeline.

How does Sifflet support collaboration across data teams?

Sifflet promotes un-siloed data quality by offering a unified platform where data engineers, analysts, and business users can collaborate. Features like pipeline health dashboards, data lineage tracking, and automated incident reports help teams stay aligned and respond quickly to issues.

What are some signs that our organization might need better data observability?

If your team struggles with delayed dashboards, inconsistent metrics, or unclear data lineage, it's likely time to invest in a data observability solution. At Sifflet, we even created a simple diagnostic to help you assess your data temperature. Whether you're in a 'slow burn' or a 'five alarm fire' state, we can help you improve data reliability and pipeline health.

Why is aligning data initiatives with business objectives important for Etam?

At Etam, every data project begins with the question, 'How does this help us reach our OKRs?' This alignment ensures that data initiatives are directly tied to business impact, improving sponsorship and fostering collaboration across departments. It's a great example of business-aligned data strategy in action.

What does Sifflet's recent $12.8M Series A funding mean for the future of data observability?

Great question! This funding round, led by EQT Ventures, allows us to double down on our mission to make data more reliable and trustworthy. With this investment, we're expanding our data observability platform, enhancing real-time monitoring capabilities, and growing our presence in EMEA and the US.

How does aligning data observability with business objectives improve outcomes?

Aligning data observability with business goals transforms data from a technical asset into a strategic one. By setting clear KPIs and linking data quality monitoring to business impact, teams can make smarter decisions, improve SLA compliance, and drive real value from their data investments.

Why is data lineage a pillar of Full Data Stack Observability?

At Sifflet, we consider data lineage a core part of Full Data Stack Observability because it connects data quality monitoring with data discovery. By mapping data dependencies, teams can detect anomalies faster, perform accurate root cause analysis, and maintain trust in their data pipelines.

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