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

Why is combining dbt Core with a data observability platform like Sifflet a smart move?

Combining dbt Core with a data observability platform like Sifflet helps data teams go beyond transformation and into full-stack monitoring. It enables better root cause analysis, reduces time to resolution, and ensures your data products are trustworthy and resilient.

How does data observability support MLOps and AI initiatives at Hypebeast?

Data observability plays a key role in Hypebeast’s MLOps strategy by monitoring data quality from ML models before it reaches dashboards or decision systems. This ensures that AI-driven insights are trustworthy and aligned with business goals.

What does 'observability culture' mean at Adaptavist?

For Adaptavist, observability culture means going beyond tools. It's about clear ownership of alerts, integrating data quality monitoring into sprints, and giving stakeholders ways to provide feedback directly in dashboards. They even track observability metrics to continuously improve their own observability practices.

What makes Sifflet stand out when it comes to data reliability and trust?

Sifflet shines in data reliability by offering real-time metrics and intelligent anomaly detection. During the webinar, we saw how even non-technical users can set up custom monitors, making it easy for teams to catch issues early and maintain SLA compliance with confidence.

How does automated data lineage improve data reliability?

Automated data lineage boosts data reliability by giving teams a clear, real-time view of data flows and dependencies. This visibility supports faster troubleshooting, better data governance, and improved SLA compliance, especially when combined with other observability tools in your stack.

What is Flow Stopper and how does it help with data pipeline monitoring?

Flow Stopper is a powerful feature in Sifflet's observability platform that allows you to pause vulnerable pipelines at the orchestration layer before issues reach production. It helps with proactive data pipeline monitoring by catching anomalies early and preventing downstream damage to your data systems.

What’s the difference between AI governance and data governance?

AI governance and data governance are both essential, but they serve different purposes. Data governance focuses on the quality, security, and availability of data inputs, while AI governance oversees the behavior and outcomes of models using that data. Together, they ensure reliable, transparent, and compliant AI systems across the data lifecycle.

What features should we look for in scalable data observability tools?

When evaluating observability tools, scalability is key. Look for features like real-time metrics, automated anomaly detection, incident response automation, and support for both batch data observability and streaming data monitoring. These capabilities help teams stay efficient as data volumes grow.

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