Best Data Observability Tools for Databricks
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Compare the Top Data Observability Tools that integrate with Databricks as of October 2026
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This a list of Data Observability tools that integrate with Databricks. Use the filters on the left to add additional filters for products that have integrations with Databricks. View the products that work with Databricks in the table below.
What are Data Observability Tools for Databricks?
Data observability tools help organizations monitor the health, quality, and performance of data systems throughout the entire data lifecycle. They automatically track metrics such as freshness, volume, schema changes, and anomaly detection to identify issues before they impact analytics or business processes. These tools often provide dashboards, alerts, and root-cause insights that make it easier for data engineers and analysts to troubleshoot problems quickly. Many data observability solutions integrate with data warehouses, data lakes, ETL/ELT pipelines, and BI platforms for comprehensive visibility. By improving transparency and reliability, data observability tools help teams maintain trust in their data and accelerate delivery of accurate insights. Compare and read user reviews of the best Data Observability tools for Databricks currently available using the table below. This list is updated regularly.
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DataHub
DataHub
You can't fix what you can't see—and in modern data platforms, visibility is the difference between proactive management and crisis response. DataHub provides comprehensive data observability that helps teams detect, diagnose, and resolve data issues before they impact business operations. Monitor data freshness, volume, schema changes, and quality metrics across your entire data estate with intelligent anomaly detection that learns normal patterns and alerts on deviations. When issues arise, DataHub's lineage graph becomes your debugging tool, tracing problems from symptoms back to root causes across complex multi-hop pipelines. Understand blast radius instantly: which dashboards, reports, and ML models are affected by this upstream failure? Integrate with incident management workflows to route issues to the right owners and track resolution. -
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DataBuck
FirstEigen
DataBuck is an AI-powered data validation platform that automates risk detection across dynamic, high-volume, and evolving data environments. DataBuck empowers your teams to: ✅ Enhance trust in analytics and reports, ensuring they are built on accurate and reliable data. ✅ Reduce maintenance costs by minimizing manual intervention. ✅ Scale operations 10x faster compared to traditional tools, enabling seamless adaptability in ever-changing data ecosystems. By proactively addressing system risks and improving data accuracy, DataBuck ensures your decision-making is driven by dependable insights. Proudly recognized in Gartner’s 2024 Market Guide for #DataObservability, DataBuck goes beyond traditional observability practices with its AI/ML innovations to deliver autonomous Data Trustability—empowering you to lead with confidence in today’s data-driven world. -
3
Decube
Decube
Decube is a data management platform that helps organizations manage their data observability, data catalog, and data governance needs. It provides end-to-end visibility into data and ensures its accuracy, consistency, and trustworthiness. Decube's platform includes data observability, a data catalog, and data governance components that work together to provide a comprehensive solution. The data observability tools enable real-time monitoring and detection of data incidents, while the data catalog provides a centralized repository for data assets, making it easier to manage and govern data usage and access. The data governance tools provide robust access controls, audit reports, and data lineage tracking to demonstrate compliance with regulatory requirements. Decube's platform is customizable and scalable, making it easy for organizations to tailor it to meet their specific data management needs and manage data across different systems, data sources, and departments. -
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MetricSign
MetricSign
MetricSign monitors your entire data stack and detects incidents before your stakeholders do. Connect Power BI via Microsoft OAuth in 2 minutes. MetricSign immediately starts detecting refresh failures, slow datasets, and missed schedules — classifying each with the exact error code and a root cause hint. Beyond Power BI, MetricSign monitors Azure Data Factory, Databricks, dbt Cloud, dbt Core, and Microsoft Fabric. When an ADF pipeline fails and cascades into a Power BI refresh failure, you get one incident — not five separate alerts from five different tools. Key capabilities: - Refresh failure detection with 98+ error code classifications - End-to-end lineage: source → pipeline → dataset → report - Slow refresh and missed schedule detection - Alerts via email, Telegram, webhook - Free plan available — no credit card requiredStarting Price: 69€/3 workspaces -
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Bigeye
Bigeye
Bigeye is the data observability platform that helps teams measure, improve, and communicate data quality clearly at any scale. Every time a data quality issue causes an outage, the business loses trust in the data. Bigeye helps rebuild trust, starting with monitoring. Find missing and busted reporting data before executives see it in a dashboard. Get warned about issues in training data before models get retrained on it. Fix that uncomfortable feeling that most of the data is mostly right, most of the time. Pipeline job statuses don't tell the whole story. The best way to ensure data is fit for use, is to monitor the actual data. Tracking dataset-level freshness ensures pipelines are running on schedule, even when ETL orchestrators go down. Find out about changes to event names, region codes, product types, and other categorical data. Detect drops or spikes in row counts, nulls, and blank values to ensure everything is populating as expected. -
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Anomalo
Anomalo
Anomalo helps you get ahead of data issues by automatically detecting them as soon as they appear in your data and before anyone else is impacted. Detect, root-cause, and resolve issues quickly – allowing everyone to feel confident in the data driving your business. Connect Anomalo to your Enterprise Data Warehouse and begin monitoring the tables you care about within minutes. Our advanced machine learning will automatically learn the historical structure and patterns of your data, allowing us to alert you to many issues without the need to create rules or set thresholds. You can also fine-tune and direct our monitoring in a couple of clicks via Anomalo’s No Code UI. Detecting an issue is not enough. Anomalo’s alerts offer rich visualizations and statistical summaries of what’s happening to allow you to quickly understand the magnitude and implications of the problem. -
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Orchestra
Orchestra
Orchestra is a Unified Control Plane for Data and AI Operations, designed to help data teams build, deploy, and monitor workflows with ease. It offers a declarative framework that combines code and GUI, allowing users to implement workflows 10x faster and reduce maintenance time by 50%. With real-time metadata aggregation, Orchestra provides full-stack data observability, enabling proactive alerting and rapid recovery from pipeline failures. It integrates seamlessly with tools like dbt Core, dbt Cloud, Coalesce, Airbyte, Fivetran, Snowflake, BigQuery, Databricks, and more, ensuring compatibility with existing data stacks. Orchestra's modular architecture supports AWS, Azure, and GCP, making it a versatile solution for enterprises and scale-ups aiming to streamline their data operations and build trust in their AI initiatives. -
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Matia
Matia
Matia is a unified DataOps platform designed to simplify modern data management by combining multiple core functions into a single, integrated system. It brings together ETL, reverse ETL, data observability, and a data catalog, eliminating the need for multiple disconnected tools and reducing the complexity of managing fragmented data stacks. It enables teams to move data quickly and reliably from various sources into data warehouses using advanced ingestion capabilities, including real-time updates and error handling, while also allowing them to push trusted data back into operational tools for business use. Matia emphasizes built-in observability at every stage of the data pipeline, providing monitoring, anomaly detection, and automated quality checks to ensure data accuracy and reliability before issues impact downstream systems. -
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Unravel
Unravel Data
Unravel is an AI-native data observability platform designed to help modern enterprises detect, resolve, and prevent data issues at scale. It uses intelligent, automated agents that work alongside data teams to surface insights, guide decisions, and reduce operational toil. Unravel brings data observability and FinOps together, enabling organizations to improve performance, ensure reliability, and optimize cloud data spending. The platform provides end-to-end visibility across pipelines, workloads, and infrastructure. With agent-driven actionability™, Unravel can take action on behalf of teams, integrate directly with existing tools, or recommend next-best actions. It supports major data platforms including Databricks, Snowflake, and Google Cloud BigQuery. By combining automation with human control, Unravel transforms data observability into a collaborative, always-on partner. -
10
IBM watsonx.data integration is a data integration platform designed to help organizations transform raw data into AI-ready data at scale. The platform enables data teams to build, manage, and optimize data pipelines across multiple environments, including on-premises systems and hybrid or multi-cloud infrastructures. With a unified control plane, watsonx.data integration supports multiple integration styles such as batch processing, real-time streaming, and data replication within a single solution. The platform also offers no-code, low-code, and pro-code development options, allowing both technical and non-technical users to design and manage data pipelines efficiently. By simplifying data integration workflows and reducing reliance on multiple tools, watsonx.data integration helps organizations deliver reliable data for analytics and AI applications.
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11
Acceldata
Acceldata
Acceldata is an Agentic Data Management company helping enterprises manage complex data systems with AI-powered automation. Its unified platform brings together data quality, governance, lineage, and infrastructure monitoring to deliver trusted, actionable insights across the business. Acceldata’s Agentic Data Management platform uses intelligent AI agents to detect, understand, and resolve data issues in real time. Designed for modern data environments, it replaces fragmented tools with a self-learning system that ensures data is accurate, governed, and ready for AI and analytics. -
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Great Expectations
Great Expectations
Great Expectations is a shared, open standard for data quality. It helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. We recommend deploying within a virtual environment. If you’re not familiar with pip, virtual environments, notebooks, or git, you may want to check out the Supporting. There are many amazing companies using great expectations these days. Check out some of our case studies with companies that we've worked closely with to understand how they are using great expectations in their data stack. Great expectations cloud is a fully managed SaaS offering. We're taking on new private alpha members for great expectations cloud, a fully managed SaaS offering. Alpha members get first access to new features and input to the roadmap. -
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Pantomath
Pantomath
Organizations continuously strive to be more data-driven, building dashboards, analytics, and data pipelines across the modern data stack. Unfortunately, most organizations struggle with data reliability issues leading to poor business decisions and lack of trust in data as an organization, directly impacting their bottom line. Resolving complex data issues is a manual and time-consuming process involving multiple teams all relying on tribal knowledge to manually reverse engineer complex data pipelines across different platforms to identify root-cause and understand the impact. Pantomath is a data pipeline observability and traceability platform for automating data operations. It continuously monitors datasets and jobs across the enterprise data ecosystem providing context to complex data pipelines by creating automated cross-platform technical pipeline lineage. -
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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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Observo AI
Observo AI
Observo AI is an AI-native data pipeline platform designed to address the challenges of managing vast amounts of telemetry data in security and DevOps operations. By leveraging machine learning and agentic AI, Observo AI automates data optimization, enabling enterprises to process AI-generated data more efficiently, securely, and cost-effectively. It reduces data processing costs by over 50% and accelerates incident response times by more than 40%. Observo AI's features include intelligent data deduplication and compression, real-time anomaly detection, and dynamic data routing to appropriate storage or analysis tools. It also enriches data streams with contextual information to enhance threat detection accuracy while minimizing false positives. Observo AI offers a searchable cloud data lake for efficient data storage and retrieval. -
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Actian Data Observability
Actian
Actian Data Observability is an AI-powered platform designed to continuously monitor, validate, and manage the health, quality, and reliability of data across modern data environments. It uses automated Data Observability Agents that validate data as it arrives in data lakehouses or warehouses, detecting anomalies, explaining root causes, and coordinating resolution before issues impact dashboards, reports, or AI systems. It provides real-time visibility into data pipelines, ensuring that data remains accurate, complete, and trustworthy throughout its lifecycle. It eliminates blind spots by monitoring 100% of data rather than relying on sampling, allowing organizations to identify hidden errors that could otherwise corrupt analytics or machine learning outcomes. With built-in anomaly detection powered by AI and machine learning, it proactively identifies irregularities such as schema changes, missing data, or unexpected distributions, enabling faster diagnosis and resolution. -
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Soda
Soda
Soda drives your data operations by identifying data issues, alerting the right people, and helping teams diagnose and resolve root causes. With automated and self-serve data monitoring capabilities, no data—or people—are ever left in the dark. Get ahead of data issues quickly by delivering full observability through easy instrumentation across your data workloads. Empower data teams to discover data issues that automation will miss. Self-service capabilities deliver the broad coverage that data monitoring needs. Alert the right people at the right time to help teams across the business diagnose, prioritize, and fix data issues. With Soda, your data never leaves your private cloud. Soda monitors data at the source and only stores metadata in your cloud.
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