Best Observability Tools for Azure Databricks

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Compare the Top Observability Tools that integrate with Azure Databricks as of July 2025

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This a list of Observability tools that integrate with Azure Databricks. Use the filters on the left to add additional filters for products that have integrations with Azure Databricks. View the products that work with Azure Databricks in the table below.

What are Observability Tools for Azure Databricks?

Observability tools are software platforms that help monitor, measure, and gain insights into the performance and health of systems, applications, and infrastructure. These tools provide a comprehensive view of the system by collecting and analyzing data from various sources, including logs, metrics, traces, and events. Observability tools are essential for identifying and diagnosing issues, improving system reliability, and optimizing performance. They enable real-time monitoring, anomaly detection, root cause analysis, and alerting, which allows teams to respond proactively to potential problems. By offering detailed insights into system behavior, observability tools are critical for DevOps, cloud-native environments, and microservices architectures. Compare and read user reviews of the best Observability tools for Azure Databricks currently available using the table below. This list is updated regularly.

  • 1
    Sifflet

    Sifflet

    Sifflet

    Automatically cover thousands of tables with ML-based anomaly detection and 50+ custom metrics. Comprehensive data and metadata monitoring. Exhaustive mapping of all dependencies between assets, from ingestion to BI. Enhanced productivity and collaboration between data engineers and data consumers. Sifflet seamlessly integrates into your data sources and preferred tools and can run on AWS, Google Cloud Platform, and Microsoft Azure. Keep an eye on the health of your data and alert the team when quality criteria aren’t met. Set up in a few clicks the fundamental coverage of all your tables. Configure the frequency of runs, their criticality, and even customized notifications at the same time. Leverage ML-based rules to detect any anomaly in your data. No need for an initial configuration. A unique model for each rule learns from historical data and from user feedback. Complement the automated rules with a library of 50+ templates that can be applied to any asset.
    1 Rating
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  • 2
    KloudMate

    KloudMate

    KloudMate

    Squash latencies, detect bottlenecks, and debug errors. Join a rapidly expanding community of businesses from around the world, that are achieving 20X value and ROI by adopting KloudMate, compared to any other observability platform. Quickly monitor crucial metrics, and dependencies, and detect anomalies through alarms and issue tracking. Instantly locate ‘break-points’ in your application development lifecycle, to proactively fix issues. View service maps for every component in your application, and uncover intricate interconnections and dependencies. Trace every request and operation, providing detailed visibility into execution paths and performance metrics. Whether it's multi-cloud, hybrid, or private architecture, access unified Infrastructure monitoring capabilities to monitor metrics and gather insights. Supercharge debugging speed and precision with a complete system view. Identify and resolve issues faster.
    Starting Price: $60 per month
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  • 3
    LOGIQ

    LOGIQ

    LOGIQ.AI

    LOGIQ.AI’s LogFlow provides centralized control of your observability data pipelines. As data streams arrive, they are automatically organized and optimized for your business teams and knowledge workers. XOps teams can centralize data flow management, gain data EPS control, and increase data quality and relevance. Built on any object store, LogFlow’s InstaStore enables infinite data retention and on-demand data replay to any target observability platform of your choice. Analyze operational metrics across applications and infrastructure and gain actionable insights that help you scale with confidence while maintaining high availability. Fuel business decisions and better user experiences by collecting, transforming, and analyzing behavioral data and usage patterns from business systems. Don’t let new attack techniques catch you off guard. Detect and analyze threat patterns from multiple sources and automate threat prevention and remediation.
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