BigQuery
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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

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 the new Fivetran integration enhance data observability in Sifflet?
Great question! With our new Fivetran integration, Sifflet now provides visibility into your data's journey even before it reaches your data platform. This means you can track data from its source through Fivetran connectors all the way downstream, offering truly end-to-end data observability.
How does Sifflet support data governance at scale?
Sifflet supports scalable data governance by letting you tag declared assets, assign owners, and classify sensitive data like PII. This ensures compliance with regulations and improves collaboration across teams using a centralized observability platform.
How does Dailymotion foster a strong data culture beyond just using observability tools?
They’ve implemented a full enablement program with starter kits, trainings, and office hours to build data literacy and trust. Observability tools are just one part of the equation; the real focus is on enabling confident, autonomous decision-making across the organization.
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 does Sifflet support data quality monitoring at scale?
Sifflet uses AI-powered dynamic monitors and data validation rules to automate data quality monitoring across your pipelines. It also integrates with tools like Snowflake and dbt to ensure data freshness checks and schema validations are embedded into your workflows without manual overhead.
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.
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 the Model Context Protocol (MCP) improve data observability with LLMs?
Great question! MCP allows large language models to access structured external context like pipeline metadata, logs, and diagnostics tools. At Sifflet, we use MCP to enhance data observability by enabling intelligent agents to monitor, diagnose, and act on issues across complex data pipelines in real time.

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