Data Observability Platforms
18 entriesPlatforms that monitor freshness, volume, schema, distribution and lineage to prevent data downtime.
01Monte CarloHelps data engineers ensure reliability and avoid costly data downtime. Features data catalogs, automated alerting, and observability across freshness, volume, schema, lineage, and distribution.
02IBM Data Observability (Databand)Proactive data observability platform that helps monitor and control data quality across pipelines, even when you can't control your sources. Integrated into IBM watsonx.data.
03AcceldataAcceldata's Data Observability Cloud enables data teams to build and operate great data products, eliminate complexity, and deliver reliable data efficiently. Expanded into AI observability with its acquisition of Bewgle.
04BigeyeThe data observability and AI Trust platform that helps teams measure, improve, and communicate data quality clearly at any scale. Trusted by USAA, Zoom, Hertz, and more.
05SiffletFull data stack observability platform — monitor your data assets, metadata, and infrastructure all in one place. AI agents (Sentinel, Sage, Forge) automate root-cause analysis and remediation.
06ValidioThe only data quality platform that scales with modern cloud-first organizations as they become increasingly data-driven. ML-powered monitoring with segmented anomaly detection. Raised $30M Series A (March 2026).
07LightupIntuitive, enterprise-grade data observability at the speed of light. Easily deployable, out-of-the-box data quality checks that help your teams hit data quality coverage goals 10× faster than legacy solutions.
08AnomaloAutomatically detect data issues and understand their root causes before anyone else. AI-powered anomaly detection for large-scale Snowflake and Databricks environments. Named a 2025 Gartner Peer Insights Strong Performer.
09TelmaiAutonomous ready data for Agentic AI - Continuous data validation and quality signal within your data lake and lakehouse so every decision runs on reliable data.
10Metaplane (Datadog)Data observability for modern data teams, now part of Datadog.
11DatafoldPrevent data outages by identifying and fixing data quality issues before they reach production. Automated testing for data pipelines — no more broken dashboards, data syncs, or ML models.
12SodaAllows everyone on your data team to find, analyze, and resolve data issues. This data observability platform brings everyone closer to the data, resulting in data products that you can trust.
13Elementary DataTrusted Data for the AI Era - Observability, Quality, Governance, and Discovery under one control plane. Connects engineers and business teams. Accelerated by AI.
14DQLabs PrizmOne System. Three Disciplines. Zero Blind Spots - Most platforms force you to stitch observability, quality, and cataloging together. Prizm unifies all three on one AI-native control plane — so every signal, score, and asset stays in sync across the enterprise.
15DatagapsMake Your Data Trustworthy for Enterprise Digital Transformation - A Gartner‑recognized platform enforcing consistent data definitions from source models to semantic layer, BI dashboards, and AI outputs – ensuring one trusted truth.
16RevefiFinOps, Observability and Optimization for your Data and AI Our AI agents autonomously manage cost, performance, and quality across your data warehouses, LLMs and agentic workloads
17SaturamSaturam simplifies the data engineering lifecycle with tools for the multi-cloud & modern data ecosystem. Our innovative products unite data and data consumers, enabling them to apply software engineering principles for data.
18Unravel DataUnravel autonomously optimizes every workload, dataset, and cluster. Costs drop. SLAs hold. No manual tuning.
Data Quality, Governance & Catalog Suites
3 entriesSuites that combine observability with quality, catalog and governance.
19AtaccamaTrust your data, accelerate your AI - Scalable data management for better AI and business outcomes. Data quality, observability, catalog, and lineage with a built-in AI Agent to streamline your stack and speed impact.
20CollibraThe enterprise standard for AI context and control - Collibra is the enterprise AI control plane governing context and ensuring control across every source, model and agent. Lead with certainty at the speed of AI.
21PreciselyUnified data management software for AI-ready data, including data integrity and observability capabilities.
Open Standards & Open Source
3 entriesOpen tools and standards that observability platforms build on.
22Great ExpectationsA shared, open standard for data quality. Helps data teams eliminate pipeline debt through data testing, documentation, and profiling. The leading open-source framework for data validation. The project's GitHub repository now sits under the fivetran organization.
23OpenLineageOpen standard for collecting data lineage metadata across pipelines and tools.
24Soda CoreOpen-source data contracts engine for data quality checks.
Adjacent: Infrastructure & Application Observability
5 entriesPlatforms for monitoring systems and applications, which often feed or complement data observability.
25DatadogAI-Powered Observability and Security - See inside any stack, any app, at any scale, anywhere.
26HoneycombOrganizes your telemetry data for fast, accurate exploration from a unified UI regardless of data type, allowing you to find issues for a single user or complex patterns across multiple users and services.
27Observability ToolsDirectory of observability tools on this site.
28Cloud-native Observability ToolsObservability for cloud-native environments, on this site.
29AI Observability PlatformsObservability for AI systems, on this site.
Related Pages on This Site
5 entriesMore data engineering topics.
30Data EngineeringHub of data engineering directories.
31Data Quality & Testing FrameworksOpen-source frameworks and observability platforms for data quality.
32Data Quality SolutionsAugmented data quality solutions.
33DataOps PlatformsDataOps platforms and tools.
34Enterprise Data Catalogs for DataOpsCatalog platforms with lineage and governance.
Frequently Asked Questions
What is data observability?
Data observability is continuous monitoring of the health of data and pipelines, covering freshness, volume, schema, distribution and lineage, so teams catch issues before they reach dashboards, models or agents.
How is it different from data quality testing?
Testing asserts rules you define in advance, while observability learns normal behaviour and flags unexpected changes, which catches problems nobody wrote a test for. Most teams use both.
What are the five pillars of data observability?
Freshness, volume, schema, distribution and lineage are the commonly cited pillars.
How is it different from application observability?
Tools such as Datadog and Honeycomb monitor services, logs and traces, while data observability tools monitor the data itself as it moves through pipelines and warehouses.
Why were some entries removed?
Observe.AI is a contact-centre AI platform and Splunk Lantern is a guidance centre, so neither is a data observability tool. Datadog and Honeycomb are kept in an adjacent group.