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.

Talk to an expert

Explore at your own pace

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

‍

Go Further

Data Observability for your data stack with Google BigQuery

Optimize Data Quality in BigQuery with Actionable Insights for Trust and Efficiency

Read Now

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

Still have a question in mind ?

Frequently asked questions

How does data observability improve the value of a data catalog?

Data observability enhances a data catalog by adding continuous monitoring, data lineage tracking, and real-time alerts. This means organizations can not only find their data but also trust its accuracy, freshness, and consistency. By integrating observability tools, a catalog becomes part of a dynamic system that supports SLA compliance and proactive data governance.

Why is aligning data initiatives with business objectives important for Etam?

At Etam, every data project begins with the question, 'How does this help us reach our OKRs?' This alignment ensures that data initiatives are directly tied to business impact, improving sponsorship and fostering collaboration across departments. It's a great example of business-aligned data strategy in action.

Why is table-level lineage important for data quality monitoring and governance?

Table-level lineage helps you understand how data flows through your systems, which is essential for data quality monitoring and data governance. It supports impact analysis, pipeline debugging, and compliance by showing how changes in upstream tables affect downstream assets.

Why does query formatting matter in modern data operations?

Well-formatted queries are easier to debug, share, and maintain. This aligns with DataOps best practices and supports transparency in data pipelines, which is essential for consistent SLA compliance and proactive monitoring.

Can I use Sifflet’s data observability tools with other platforms besides Airbyte?

Absolutely! While we’ve built a powerful solution for Airbyte, our Declarative Lineage API is flexible enough to support other platforms like Kafka, Census, Hightouch, and Talend. You can use our sample Python scripts to integrate lineage from these tools and enhance your overall data observability strategy.

What makes Monte Carlo a good fit for modern cloud analytics teams?

Monte Carlo shines when you're working with a modern cloud stack and need fast, low-effort data observability. Its ML-driven anomaly detection and metadata-focused monitoring make it easy to catch data issues without writing custom rules. If your team wants to improve dashboard reliability quickly, Monte Carlo is a strong option.

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.

Can Sifflet help with data pipeline monitoring in lakehouse environments?

Absolutely! Sifflet offers comprehensive data pipeline monitoring by focusing on metadata-driven signals. It monitors table health, detects missed compactions, and alerts you about retention risks, helping you maintain performance and governance in your lakehouse architecture.

Read all FAQs

Want to try Sifflet on your BigQuery Stack?

Get in Touch Now!

I want to Try