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

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 can data lineage tracking help with root cause analysis?

Data lineage tracking shows how data flows through your systems and how different assets depend on each other. This is incredibly helpful for root cause analysis because it lets you trace issues back to their source quickly. With Sifflet’s lineage capabilities, you can understand both upstream and downstream impacts of a data incident, making it easier to resolve problems and prevent future ones.

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 table-level lineage important for data observability?

Table-level lineage helps teams perform impact analysis, debug broken pipelines, and meet compliance standards by clearly showing how data flows between systems. It's foundational for data quality monitoring and root cause analysis in modern observability platforms.

Why is data observability so important for AI-powered organizations in 2025?

Great question! As AI continues to evolve, the quality and reliability of the data feeding those models becomes even more critical. Data observability ensures that your AI systems are powered by clean, accurate, and up-to-date data. With platforms like Sifflet, organizations can detect issues like data drift, monitor real-time metrics, and maintain data governance, all of which help AI models stay accurate and trustworthy.

How does the Sifflet and Firebolt integration improve data observability?

Great question! By integrating with Firebolt, Sifflet enhances your data observability by offering real-time metrics, end-to-end lineage, and automated anomaly detection. This means you can monitor your Firebolt data warehouse with precision and catch data quality issues before they impact the business.

Why did Shippeo decide to invest in a data observability solution like Sifflet?

As Shippeo scaled, they faced silent data leaks, inconsistent metrics, and data quality issues that impacted billing and reporting. By adopting Sifflet, they gained visibility into their data pipelines and could proactively detect and fix problems before they reached end users.

How does data observability differ from traditional data quality monitoring?

Great question! Traditional data quality monitoring focuses on pre-defined rules and tests, but it often falls short when unexpected issues arise. Data observability, on the other hand, provides end-to-end visibility using telemetry instrumentation like metrics, metadata, and lineage. This makes it possible to detect anomalies in real time and troubleshoot issues faster, even in complex data environments.

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