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

Is Sifflet Insights easy to set up with my existing tools?

Yes, onboarding is seamless. You can quickly integrate Sifflet Insights with your existing BI tools and start receiving real-time metrics and alerts. It’s designed to enhance efficiency and support incident response automation without disrupting your current workflows.

How can data observability help prevent missed SLAs and unreliable dashboards?

Data observability plays a key role in SLA compliance by detecting issues like ingestion latency, schema changes, or data drift before they impact downstream users. With proper data quality monitoring and real-time metrics, you can catch problems early and keep your dashboards and reports reliable.

Why is metadata so important for modern data monitoring?

Great question! Metadata adds the context that traditional monitoring lacks. It helps you understand not just what failed, but also where, why, and who owns it. By layering in technical, operational, and business metadata, your data monitoring becomes smarter and more actionable—making it easier to maintain data quality and reliability across your stack.

How can data observability help improve the happiness of my data team?

Great question! A strong data observability platform helps reduce uncertainty in your data pipelines by providing transparency, real-time metrics, and proactive anomaly detection. When your team can trust the data and quickly identify issues, they feel more confident, empowered, and less stressed, which directly boosts team morale and satisfaction.

How does Sifflet's integration with dbt Core improve data observability?

Great question! By integrating with dbt Core, Sifflet enhances data observability across your entire data stack. It helps you monitor dbt test coverage, map tests to downstream dependencies using data lineage tracking, and consolidate metadata like tags and descriptions, all in one place.

How do Sifflet's AI agents like Sentinel and Forge improve data pipeline monitoring?

Sentinel recommends monitoring strategies based on metadata, making it easy for non-technical users to set up robust data quality monitoring. Forge goes a step further by suggesting contextual fixes grounded in historical patterns. Together, they enhance data pipeline monitoring by enabling proactive issue detection and resolution.

How does data quality monitoring help improve data reliability?

Data quality monitoring is essential for maintaining trust in your data. A strong observability platform should offer features like anomaly detection, data profiling, and data validation rules. These tools help identify issues early, so you can fix them before they impact downstream analytics. It’s all about making sure your data is accurate, timely, and reliable.

Why is data observability so important for AI and analytics initiatives?

Great question! Data observability ensures that the data fueling AI and analytics is reliable, accurate, and fresh. At Sifflet, we see data observability as both a technical and business challenge, which is why our platform focuses on data quality monitoring, anomaly detection, and real-time metrics to help enterprises make confident, data-driven decisions.

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