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

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Frequently asked questions
What features should we look for in scalable data observability tools?
When evaluating observability tools, scalability is key. Look for features like real-time metrics, automated anomaly detection, incident response automation, and support for both batch data observability and streaming data monitoring. These capabilities help teams stay efficient as data volumes grow.
What does Sifflet plan to do with the new $18M in funding?
We're excited to use this funding to accelerate product innovation, expand our North American presence, and grow our team. Our focus will be on enhancing AI-powered capabilities, improving data pipeline monitoring, and helping customers maintain data reliability at scale.
What makes Sifflet a strong alternative to Monte Carlo for data observability?
Sifflet stands out as a modern data observability platform that combines AI-powered monitoring with business context. Unlike Monte Carlo, Sifflet offers no-code monitor creation, dynamic alerting with impact insights, and real-time data lineage tracking. It's designed for both technical and business users, making it easier for teams to collaborate and maintain data reliability across the organization.
Can classification tags improve data pipeline monitoring?
Absolutely! By tagging fields like 'Low Cardinality', data teams can quickly identify which fields are best suited for specific monitors. This enables more targeted data pipeline monitoring, making it easier to detect anomalies and maintain SLA compliance across your analytics pipeline.
How does Sifflet help reduce alert fatigue in data teams?
Great question! Sifflet tackles alert fatigue by using AI-native monitoring that understands business context. Instead of flooding teams with false positives, it prioritizes alerts based on downstream impact. This means your team focuses on real issues, improving trust in your observability tools and saving valuable engineering time.
How does the shift to poly cloud impact observability platforms?
The move toward poly cloud environments increases the complexity of monitoring, but observability platforms are evolving to unify insights across multiple cloud providers. This helps teams maintain SLA compliance, monitor ingestion latency, and ensure data reliability regardless of where workloads are running.
Can Sifflet help me trace how data moves through my pipelines?
Absolutely! Sifflet’s data lineage tracking gives you a clear view of how data flows and transforms across your systems. This level of transparency is crucial for root cause analysis and ensuring data governance standards are met.
How does Sifflet help with data discovery across different tools like Snowflake and BigQuery?
Great question! Sifflet acts as a unified observability platform that consolidates metadata from tools like Snowflake and BigQuery into one centralized Data Catalog. By surfacing tags, labels, and schema details, it makes data discovery and governance much easier for all stakeholders.




















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