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 does the Sifflet and Google Cloud partnership mean for users?

Great question! This partnership allows Google Cloud users to integrate Sifflet’s data observability platform directly within their private cloud environment. That means better visibility, reliability, and trust in your data from ingestion all the way to analytics.

How does Sifflet help with real-time anomaly detection?

Sifflet uses ML-based monitors and an AI-driven assistant to detect anomalies in real time. Whether it's data drift detection, schema changes, or unexpected drops in metrics, our platform ensures you catch issues early and resolve them fast with built-in root cause analysis and incident reporting.

What does it mean to treat data as a product?

Treating data as a product means prioritizing its reliability, usability, and trustworthiness—just like you would with any customer-facing product. This mindset shift is driving the need for observability platforms that support data governance, real-time metrics, and proactive monitoring across the entire data lifecycle.

How does data observability fit into the modern data stack?

Data observability integrates across your existing data stack, from ingestion tools like Airflow and AWS Glue to storage solutions like Snowflake and Redshift. It acts as a monitoring layer that provides real-time insights and alerts across each stage, helping teams maintain pipeline health and ensure data freshness checks are always in place.

What tools can help me monitor data consistency between old and new environments?

You can use data profiling and anomaly detection tools to compare datasets before and after migration. These features are often built into modern data observability platforms and help you validate that nothing critical was lost or changed during the move.

Why did jobvalley choose Sifflet over other data catalog vendors?

After evaluating several data catalog vendors, jobvalley selected Sifflet because of its comprehensive features that addressed both data discovery and data quality monitoring. The platform’s ability to streamline onboarding and support real-time metrics made it the ideal choice for their growing data team.

How can I measure whether my data is trustworthy?

Great question! To measure data quality, you can track key metrics like accuracy, completeness, consistency, relevance, and freshness. These indicators help you evaluate the health of your data and are often part of a broader data observability strategy that ensures your data is reliable and ready for business use.

Why is data quality management so important for growing organizations?

Great question! Data quality management helps ensure that your data remains accurate, complete, and aligned with business goals as your organization scales. Without strong data quality practices, teams waste time troubleshooting issues, decision-makers lose trust in reports, and systems make poor choices. With proper data quality monitoring in place, you can move faster, automate confidently, and build a competitive edge.

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