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 isn't infrastructure monitoring enough to ensure data reliability?

Great question! Infrastructure tools like Datadog are excellent at monitoring system uptime, server health, and network performance, but they lack visibility into the actual content of your data. That means they can’t catch silent data issues like null values or schema changes that break downstream dashboards. That’s where a data observability platform like Sifflet comes in—it ensures your data is accurate, complete, and trustworthy, not just delivered on time.

What is reverse ETL and why is it important in the modern data stack?

Reverse ETL is the process of moving data from your data warehouse into external systems like CRMs or marketing platforms. It plays a crucial role in the modern data stack by enabling operational analytics, allowing business teams to act on real-time metrics and make data-driven decisions directly within their everyday tools.

What are Sentinel, Sage, and Forge, and how do they enhance data observability?

Sentinel, Sage, and Forge are Sifflet’s new AI agents designed to supercharge your data observability efforts. Sentinel proactively recommends monitoring strategies, Sage accelerates root cause analysis by remembering system history, and Forge guides your team with actionable fixes. Together, they help teams reduce alert fatigue and improve data reliability at scale.

How can data observability help with SLA compliance and incident management?

Data observability plays a huge role in SLA compliance by enabling real-time alerts and proactive monitoring of data freshness, completeness, and accuracy. When issues occur, observability tools help teams quickly perform root cause analysis and understand downstream impacts, speeding up incident response and reducing downtime. This makes it easier to meet service level agreements and maintain stakeholder trust.

What is a Single Source of Truth, and why is it so hard to achieve?

A Single Source of Truth (SSOT) is a centralized repository where all organizational data is stored and accessed consistently. While it sounds ideal, achieving it is tough because different tools often measure data in unique ways, leading to multiple interpretations. Ensuring data reliability and consistency across sources is where data observability platforms like Sifflet can make a real difference.

What role does Sifflet’s Data Catalog play in data governance?

Sifflet’s Data Catalog supports data governance by surfacing labels and tags, enabling classification of data assets, and linking business glossary terms for standardized definitions. This structured approach helps maintain compliance, manage costs, and ensure sensitive data is handled responsibly.

What’s new in Sifflet’s data quality monitoring capabilities?

We’ve rolled out several powerful updates to help you monitor data quality more effectively. One highlight is our new referential integrity monitor, which ensures logical consistency between tables, like verifying that every order has a valid customer ID. We’ve also enhanced our Data Quality as Code framework, making it easier to scale monitor creation with templates and for-loops.

What makes business-aware data observability so important?

Business-aware observability bridges the gap between technical issues and real-world outcomes. It’s not just about detecting schema changes or data drift — it’s about understanding how those issues affect KPIs, dashboards, and decisions. At Sifflet, we bring together telemetry instrumentation, data profiling, and business context so teams can prioritize incidents based on impact, not just severity. This empowers everyone, from data engineers to product managers, to trust and act on data with confidence.

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