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

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.

What makes Sifflet's approach to data quality unique?

At Sifflet, we believe data quality isn't one-size-fits-all. Our observability platform blends technical robustness with business context, offering customized data quality monitoring that adapts to your specific use cases. This means you get both reliable pipelines and meaningful metrics that align with your business goals.

What are the main challenges of implementing Data as a Product?

Some key challenges include ensuring data privacy and security, maintaining strong data governance, and investing in data optimization. These areas require robust monitoring and compliance tools. Leveraging an observability platform can help address these issues by providing visibility into data lineage, quality, and pipeline performance.

What role does metadata play in a data observability platform?

Metadata provides context about your data, such as who created it, when it was modified, and how it's classified. In a data observability platform, strong metadata management enhances data discovery, supports compliance monitoring, and ensures consistent, high-quality data across systems.

What makes observability scalable across different teams and roles?

Scalable observability works for engineers, analysts, and business stakeholders alike. It supports telemetry instrumentation for developers, intuitive dashboards for analysts, and high-level confidence signals for executives. By adapting to each role without adding friction, observability becomes a shared language across the organization.

How does Flow Stopper support root cause analysis and incident prevention?

Flow Stopper enables early anomaly detection and integrates with your orchestrator to halt execution when issues are found. This makes it easier to perform root cause analysis before problems escalate and helps prevent incidents that could affect business-critical dashboards or KPIs.

What makes Sifflet’s Data Catalog different from built-in catalogs like Snowsight or Unity Catalog?

Unlike tool-specific catalogs, Sifflet serves as a 'Catalog of Catalogs.' It brings together metadata from across your entire data ecosystem, providing a single source of truth for data lineage tracking, asset discovery, and SLA compliance.

Is this integration useful for teams focused on data governance and compliance?

Yes, it really is! With enhanced lineage and metadata tracking from source to destination, the Fivetran integration supports better data governance. It helps ensure transparency, traceability, and SLA compliance across your data ecosystem.

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