Best Anomaly Detection Software for Google Cloud BigQuery

Compare the Top Anomaly Detection Software that integrates with Google Cloud BigQuery as of October 2025

This a list of Anomaly Detection software that integrates with Google Cloud BigQuery. Use the filters on the left to add additional filters for products that have integrations with Google Cloud BigQuery. View the products that work with Google Cloud BigQuery in the table below.

What is Anomaly Detection Software for Google Cloud BigQuery?

Anomaly detection software identifies unusual patterns, behaviors, or outliers in datasets that deviate from expected norms. It uses statistical, machine learning, and AI techniques to automatically detect anomalies in real time or through batch analysis. This software is widely used in cybersecurity, fraud detection, predictive maintenance, and quality control. By flagging anomalies, it enables early intervention, reduces risks, and enhances operational efficiency. Advanced versions offer customizable thresholds, real-time alerts, and integration with analytics dashboards for deeper insights. Compare and read user reviews of the best Anomaly Detection software for Google Cloud BigQuery currently available using the table below. This list is updated regularly.

  • 1
    Netdata

    Netdata

    Netdata, Inc.

    The open-source observability platform everyone needs! Netdata collects metrics per second and presents them in beautiful low-latency dashboards. It is designed to run on all of your physical and virtual servers, cloud deployments, Kubernetes clusters, and edge/IoT devices, to monitor your systems, containers, and applications. It scales nicely from just a single server to thousands of servers, even in complex multi/mixed/hybrid cloud environments, and given enough disk space it can keep your metrics for years. KEY FEATURES: πŸ’₯ Collects metrics from 800+ integrations πŸ’ͺ Real-Time, Low-Latency, High-Resolution πŸ˜Άβ€πŸŒ«οΈ Unsupervised Anomaly Detection πŸ”₯ Powerful Visualization πŸ”” Out of box Alerts πŸ“– systemd Journal Logs Explorer 😎 Low Maintenance ⭐ Open and Extensible Try Netdata today and feel the pulse of your infrastructure, with high-resolution metrics, journal logs and real-time visualizations.
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    Starting Price: Free
  • 2
    Elastic Observability
    Rely on the most widely deployed observability platform available, built on the proven Elastic Stack (also known as the ELK Stack) to converge silos, delivering unified visibility and actionable insights. To effectively monitor and gain insights across your distributed systems, you need to have all your observability data in one stack. Break down silos by bringing together the application, infrastructure, and user data into a unified solution for end-to-end observability and alerting. Combine limitless telemetry data collection and search-powered problem resolution in a unified solution for optimal operational and business results. Converge data silos by ingesting all your telemetry data (metrics, logs, and traces) from any source in an open, extensible, and scalable platform. Accelerate problem resolution with automatic anomaly detection powered by machine learning and rich data analytics.
    Starting Price: $16 per month
  • 3
    InsightFinder

    InsightFinder

    InsightFinder

    InsightFinder Unified Intelligence Engine (UIE) platform provides human-centered AI solutions for identifying incident root causes, and predicting and preventing production incidents. Powered by patented self-tuning unsupervised machine learning, InsightFinder continuously learns from metric time series, logs, traces, and triage threads from SREs and DevOps Engineers to bubble up root causes and predict incidents from the source. Companies of all sizes have embraced the platform and seen that business-impacting incidents can be predicted hours ahead with clearly pinpointed root causes. Survey a comprehensive overview of your IT Ops ecosystem, including patterns, trends, and team activities. Also view calculations that demonstrate overall downtime savings, cost of labor savings, and number of incidents resolved.
    Starting Price: $2.5 per core per month
  • 4
    DoiT

    DoiT

    DoiT

    DoiT is a global technology company that delivers a comprehensive cloud operations platform powered by proactive, industry-defining expertise so you can increase your operating margins and fuel innovation. DoiT Cloud Intelligence is the only context-aware multicloud intelligence platform that enables you to optimize, scale, and innovate. You turn insights into actions hand-in-hand with our cloud architects to make their cloud performant, reliable, and secure. An award-winning strategic partner of AWS, Google Cloud, and Microsoft Azure, we bring specializations in Kubernetes, GenAI, CloudOps, and more, to help more than 4,000 customers worldwide leverage the cloud to drive business growth and innovation.
    Starting Price: $0
  • 5
    Metaplane

    Metaplane

    Metaplane

    Monitor your entire warehouse in 30 minutes. Identify downstream impact with automated warehouse-to-BI lineage. Trust takes seconds to lose and months to regain. Gain peace of mind with observability built for the modern data era. Code-based tests take hours to write and maintain, so it's hard to achieve the coverage you need. In Metaplane, you can add hundreds of tests within minutes. We support foundational tests (e.g. row counts, freshness, and schema drift), more complex tests (distribution drift, nullness shifts, enum changes), custom SQL, and everything in between. Manual thresholds take a long time to set and quickly go stale as your data changes. Our anomaly detection models learn from historical metadata to automatically detect outliers. Monitor what matters, all while accounting for seasonality, trends, and feedback from your team to minimize alert fatigue. Of course, you can override with manual thresholds, too.
    Starting Price: $825 per month
  • 6
    Qualytics

    Qualytics

    Qualytics

    Helping enterprises proactively manage their full data quality lifecycle through contextual data quality checks, anomaly detection and remediation. Expose anomalies and metadata to help teams take corrective actions. Automatically trigger remediation workflows to resolve errors quickly and efficiently. Maintain high data quality and prevent errors from affecting business decisions. The SLA chart provides an overview of SLA, including the total number of SLA monitoring that have been performed and any violations that have occurred. This chart can help you identify areas of your data that may require further investigation or improvement.
  • 7
    Acryl Data

    Acryl Data

    Acryl Data

    No more data catalog ghost towns. Acryl Cloud drives fast time-to-value via Shift Left practices for data producers and an intuitive UI for data consumers. Continuously detect data quality incidents in real-time, automate anomaly detection to prevent breakages, and drive fast resolution when they do occur. Acryl Cloud supports both push-based and pull-based metadata ingestion for easy maintenance, ensuring information is trustworthy, up-to-date, and definitive. Data should be operational. Go beyond simple visibility and use automated Metadata Tests to continuously expose data insights and surface new areas for improvement. Reduce confusion and accelerate resolution with clear asset ownership, automatic detection, streamlined alerts, and time-based lineage for tracing root causes.
  • 8
    Validio

    Validio

    Validio

    See how your data assets are used: popularity, utilization, and schema coverage. Get important insights about your data assets such as popularity, utilization, quality, and schema coverage. Find and filter the data you need based on metadata tags and descriptions. Get important insights about your data assets such as popularity, utilization, quality, and schema coverage. Drive data governance and ownership across your organization. Stream-lake-warehouse lineage to facilitate data ownership and collaboration. Automatically generated field-level lineage map to understand the entire data ecosystem. Anomaly detection learns from your data and seasonality patterns, with automatic backfill from historical data. Machine learning-based thresholds are trained per data segment, trained on actual data instead of metadata only.
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