Best Data Observability Tools for Apache Airflow
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Compare the Top Data Observability Tools that integrate with Apache Airflow as of July 2026
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This a list of Data Observability tools that integrate with Apache Airflow. Use the filters on the left to add additional filters for products that have integrations with Apache Airflow. View the products that work with Apache Airflow in the table below.
What are Data Observability Tools for Apache Airflow?
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 Apache Airflow currently available using the table below. This list is updated regularly.
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1
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. -
2
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
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. -
4
DQOps
DQOps
DQOps is an open-source data quality platform designed for data quality and data engineering teams that makes data quality visible to business sponsors. The platform provides an efficient user interface to quickly add data sources, configure data quality checks, and manage issues. DQOps comes with over 150 built-in data quality checks, but you can also design custom checks to detect any business-relevant data quality issues. The platform supports incremental data quality monitoring to support analyzing data quality of very big tables. Track data quality KPI scores using our built-in or custom dashboards to show progress in improving data quality to business sponsors. DQOps is DevOps-friendly, allowing you to define data quality definitions in YAML files stored in Git, run data quality checks directly from your data pipelines, or automate any action with a Python Client. DQOps works locally or as a SaaS platform.Starting Price: $499 per month -
5
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. -
6
Telmai
Telmai
A low-code no-code approach to data quality. SaaS for flexibility, affordability, ease of integration, and efficient support. High standards of encryption, identity management, role-based access control, data governance, and compliance standards. Advanced ML models for detecting row-value data anomalies. Models will evolve and adapt to users' business and data needs. Add any number of data sources, records, and attributes. Well-equipped for unpredictable volume spikes. Support batch and streaming processing. Data is constantly monitored to provide real-time notifications, with zero impact on pipeline performance. Seamless boarding, integration, and investigation experience. Telmai is a platform for the Data Teams to proactively detect and investigate anomalies in real time. A no-code on-boarding. Connect to your data source and specify alerting channels. Telmai will automatically learn from data and alert you when there are unexpected drifts. -
7
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. -
8
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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9
Datafold
Datafold
Prevent data outages by identifying and fixing data quality issues before they get into production. Go from 0 to 100% test coverage of your data pipelines in a day. Know the impact of each code change with automatic regression testing across billions of rows. Automate change management, improve data literacy, achieve compliance, and reduce incident response time. Don’t let data incidents take you by surprise. Be the first one to know with automated anomaly detection. Datafold’s easily adjustable ML model adapts to seasonality and trend patterns in your data to construct dynamic thresholds. Save hours spent on trying to understand data. Use the Data Catalog to find relevant datasets, fields, and explore distributions easily with an intuitive UI. Get interactive full-text search, data profiling, and consolidation of metadata in one place. -
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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. -
11
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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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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