BigQuery
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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.

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

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 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.
How does Sifflet maintain visual and interaction consistency across its observability platform?
We use a reusable component library based on atomic design principles, along with UX writing guidelines to ensure consistent terminology. This helps users quickly understand telemetry instrumentation, metrics collection, and incident response workflows without needing to relearn interactions across different parts of the platform.
What makes Sifflet different from other data observability tools?
Sifflet stands out as a metadata control plane that connects technical reliability with business context. Unlike point solutions, it offers AI-native automation, full data lineage tracking, and cross-functional accessibility, making it ideal for organizations that need to scale trust in their data across teams.
How do classification tags support real-time metrics and alerting?
Classification tags help define the structure and importance of your data, which in turn makes it easier to configure real-time metrics and alerts. For example, tagging a 'country' field as low cardinality allows teams to monitor sales data by region, enabling faster anomaly detection and more actionable real-time alerts.
How does data lineage enhance data observability?
Data lineage adds context to data observability by linking alerts to their root cause. For example, if a metric suddenly drops, lineage helps trace it back to a delayed ingestion or schema change. This speeds up incident resolution and strengthens anomaly detection. Platforms like Sifflet combine lineage with real-time metrics and data freshness checks to provide a complete view of pipeline health.
How does data ingestion relate to data observability?
Great question! Data ingestion is where observability starts. Once data enters your system, observability platforms like Sifflet help monitor its quality, detect anomalies, and ensure data freshness. This allows teams to catch ingestion issues early, maintain SLA compliance, and build trust in their data pipelines.
What is a data platform and why does it matter?
A data platform is a unified system that helps companies collect, store, process, and analyze data across their organization. It acts as the central nervous system for all data operations, powering dashboards, AI models, and decision-making. When paired with strong data observability, it ensures teams can trust their data and move faster with confidence.
What can I expect from Sifflet’s upcoming webinar?
Join us on January 22nd for a deep dive into Sifflet’s 2024 highlights and a preview of what’s ahead in 2025. We’ll cover innovations in data observability, including real-time metrics, faster incident resolution, and the upcoming Sifflet AI Agent. It’s the perfect way to kick off the year with fresh insights and inspiration!

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