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

Can business-aware observability improve SLA compliance?

Absolutely. By connecting data health to business workflows, business-aware observability enables more accurate SLA monitoring. Sifflet’s platform helps teams track service level indicators and proactively manage incidents before they breach SLAs, improving both reliability and accountability.

Why is data storage so important for data observability?

Great question! Data storage is the foundation of any data observability strategy. Without reliable storage, you can't trust the data you're monitoring or trace issues back to their source. At Sifflet, we believe observability starts with making sure your data is stored correctly, consistently, and accessibly. That way, your alerts, dashboards, and root cause analysis are built on solid ground.

Is Sifflet available for VPC deployment on Google Cloud?

Yes it is! You can deploy Sifflet’s observability platform within your own private Google Cloud environment using VPC deployment, giving you full control over data governance and security.

What role does MCP play in improving data quality monitoring?

MCP enables LLMs to access structured context like schema changes, validation rules, and logs, making it easier to detect and explain data quality issues. With tool calls and memory, agents can continuously monitor pipelines and proactively alert teams when data quality deteriorates. This supports better SLA compliance and more reliable data operations.

What are the key components of an end-to-end data platform?

An end-to-end data platform includes layers for ingestion, storage, transformation, orchestration, governance, observability, and analytics. Each part plays a role in making data reliable and actionable. For example, data lineage tracking and real-time metrics collection help ensure transparency and performance across the pipeline.

How does this integration help with root cause analysis?

By including Fivetran connectors and source assets in the lineage graph, Sifflet gives you full visibility into where data issues originate. This makes it much easier to perform root cause analysis and resolve incidents faster, improving overall data reliability.

Can Sifflet support real-time metrics and monitoring for AI pipelines?

Absolutely! While Sifflet’s monitors are typically scheduled, you can run them on demand using our API. This means you can integrate real-time data quality checks into your AI pipelines, ensuring your models are making decisions based on the freshest and most accurate data available. It's a powerful way to keep your AI systems responsive and reliable.

Why is combining data catalogs with data observability tools the future of data management?

Combining data catalogs with data observability tools creates a holistic approach to managing data assets. While catalogs help users discover and understand data, observability tools ensure that data is accurate, timely, and reliable. This integration supports better decision-making, improves data reliability, and strengthens overall data governance.

Read all FAQs

Want to try Sifflet on your BigQuery Stack?

Get in Touch Now!

I want to Try