Top 12 Data Observability Tools of 2025: Key Features Compared

Updated December 04th, 2024

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Data observability tools are essential for monitoring and analyzing data pipelines to ensure high quality, reliability, and performance.

Leading data observability platforms such as Monte Carlo, Acceldata, AppDynamics (part of Cisco), Amazon CloudWatch, Datadog, Dynatrace, Elastic Observability, Instana (an IBM Company), Lightstep (from ServiceNow), New Relic One, Splunk Observability Cloud, and StackState offer comprehensive monitoring solutions.
See How Atlan Simplifies Data Governance – Start Product Tour

These tools help organizations detect anomalies, resolve issues automatically, and maintain data integrity, thereby preventing pipeline failures and optimizing data workflows.

By providing predictive analytics and real-time alerts, they enable teams to act proactively and make informed, data-driven decisions.

Data observability tools help you monitor the performance of your distributed environment and correlate it with your business outcomes.

According to Barr Moses, the co-founder, and CEO of Monte Carlo Data:

Data observability tools, like their DevOps counterparts, uses automated monitoring, alerting, and triaging to identify and evaluate data quality and discoverability issues, leading to healthier pipelines, more productive teams, and happier customers.

Unlike traditional monitoring tools, observability tools provide 24/7, end-to-end visibility into your systems and proactively spot potential issues so that you can mitigate them before they become too serious.

As per the 2024 Global Data Observability Market Analysis survey , the global data observability market was valued at approximately USD 2.3 billion in 2023. It is projected to reach USD 7.01 billion by 2033, growing at a compound annual growth rate (CAGR) of 11.8% during the forecast period from 2024 to 2033.

This article presents the most widely used observability tools (DataOps and DevOps), featured on popular review portals such as Gartner and G2.

Here are the twelve most popular observability tools in 2025:

  1. Monte Carlo Data Observability Platform
  2. Acceldata Data Observability Cloud
  3. Appdynamics Business Observability Platform (part of Cisco)
  4. Amazon CloudWatch
  5. Datadog Observability Platform
  6. Dynatrace
  7. Elastic Observability
  8. Instana (an IBM Company)
  9. Lightstep (from ServiceNow)
  10. New Relic One
  11. Splunk Observability Cloud
  12. StackState

Table of contents #

  1. Monte Carlo Data Observability Platform
  2. Acceldata Data Observability Cloud
  3. Appdynamics Business Observability Platform
  4. Amazon CloudWatch
  5. Datadog Observability Platform
  6. Dynatrace
  7. Elastic Observability
  8. Instana Enterprise Observability
  9. Lightstep Observability
  10. New Relic One
  11. Splunk Observability Cloud
  12. StackState
  13. How organizations making the most out of their data using Atlan
  14. FAQs on data observability tools
  15. Data observability tools: Related reads

Monte Carlo Data Observability Platform #

Monte Carlo is a data reliability company and claims to have built the first end-to-end data observability platform.

Monte Carlo uses ML algorithms to learn what your data (and consequently, good data) looks like. This helps the platform spot bad data proactively and alert you so that you keep your data clean and credible.

The platform also explores potential data downtime, gauges its impact, and notifies the right folks so that they can fix the issue right away. Here’s how the company defines data downtime:

We’ve met hundreds of data teams that experience broken dashboards, poorly trained ML models, and inaccurate analytics — and we’ve been there ourselves. We call this problem data downtime, and we found it leads to sleepless nights, lost revenue, and wasted time.

Monte Carlo also claims to be the only data observability solution to achieve SOC 2 compliance with their security-first architecture.

What are the main capabilities of Monte Carlo Data Observability Platform?

  • Automate root cause discovery to resolve issues faster
  • Observe all your data from data lakes, data warehouses, ETL, business intelligence tools, and catalogs in one place automatically and use it to visualize all data dependencies
  • Set up the platform without using any code and integrate it seamlessly with your data stack

Monte Carlo Resources

Product tour (Video) | The big book of data observability (Guide) | Documentation

Also, read → AIOps And Observability for Operational Insights | Creating Trust in Data with Data Observability | Data observability driven by active metadata and AI/ML

Acceldata Data Observability Cloud #

Acceldata offers a multidimensional observability platform to improve data reliability, optimize data pipeline performance, and reduce inefficiencies.

Acceldata offers three product suites:

  1. Pulse: For performance monitoring
  2. Torch: For data reliability
  3. Flow: For data pipeline observability

The Acceldata product stack integrates seamlessly with the rest of your data stack such as ETL tools and orchestration pipelines.

What are the main capabilities of the Acceldata Data Observability Cloud?

  • Predict operational issues before they occur so that the DataOps team can implement fixes
  • Use Acceldata Flow to trace the journey of every data asset through your systems
  • Automate data reliability across data lakes and data warehouses

Acceldata Resources

Documentation | Acceldata Flow Product Info | PubMatic (Case Study)

Appdynamics Business Observability Platform #

The Appdynamics Business Observability Platform is a part of Cisco and was named a “Leader in the 2021 APM Magic Quadrant” by Gartner. The platform lets you connect app performance to customer experience and business outcomes by visualizing every infrastructure component.

Appdynamics integrates well with several languages and frameworks, DevOps tools, cloud environments, mobile IoT, and other such tools in the DataOps tech stack.

What are the main capabilities of Appdynamics Business Observability Platform?

  • Unearth root causes of performance issues in real-time to understand what went wrong and how it’s affecting your key business metrics
  • Spot app, code, and network security vulnerabilities in real-time
  • Use the Smart Code Instrumentation to set up the entire platform in minutes

Appdynamics Resources

Datasheet | Case Studies | Analyst Coverage

[Download ebook] → Building a Business Case for DataOps

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Amazon CloudWatch #

Amazon CloudWatch is an observability and monitoring solution for AWS resources. You can collect, access, and correlate telemetry across all AWS resources on a single platform — CloudWatch.

It collects data at every layer of the performance stack, from frontend to infrastructure. You can view metrics graphs for your AWS resources and create alarms to notify you when certain conditions are met (such as an instance CPU utilization exceeding 70%).

Using the data gathered in near real-time, you can identify trends or patterns in your infrastructure’s performance to reduce MTTR (mean time to repair).

What are the main capabilities of Amazon CloudWatch?

  • Set alarms and automate actions using either predefined thresholds or machine learning (ML) algorithms to spot anomalies
  • Explore, analyze, and visualize your logs to troubleshoot operational issues
  • No setup or maintenance needed, and you only pay for the queries you run

Amazon CloudWatch Resources

Amazon CloudWatch | Features | Pricing

Datadog Observability Platform #

The Datadog Observability Platform provides complete visibility into the health and performance of your apps, infrastructure, and third-party services. It provides 500+ integrations to bring together end-to-end traces, metrics, and logs so that you can capture and correlate data from any stack in real-time.

Datadog offers a free 14-day trial for its entire platform to help you get started.

What are the main capabilities of Datadog Observability Platform?

  • Visualize the status of your microservices in a single pane
  • Proactively spot performance issues in real-time with machine learning
  • Track incidents with synchronized dashboards
  • Analyze and search through logs to troubleshoot issues
  • Use Synthetic Monitoring to set up code-free tests and run simulations of any event

Datadog Resources

Datasheet | Documentation | Resources

Dynatrace #

Dynatrace’s AI-powered platform uses AIOps to predict and resolve problems before they affect your users or business. Dynatrace offers a single platform that supports hybrid distributed cloud observability, automatic code-level root-cause detection and profiling, DevSecOps automation, and more.

It also supports integrations with 600+ technologies such as cloud services, containers technologies, Kubernetes, and more.

Dynatrace offers a free 15-day trial (no credit card required) to get you started.

What are the main capabilities of Dynatrace?

  • Use Dynatrace OneAgent to automate end-to-end data collection, auto-detect all the active processes, and auto-inject the necessary sub-agents to gather the relevant metrics
  • Enable distributed tracing and code-level visibility with Dynatrace PurePath
  • Get automatic, real-time topology mapping with context using Dynatrace SmartScape
  • Scale across hundreds of thousands of hosts, millions of entities, and the largest multi-cloud environments

Dynatrace Resources

Quick demos | Documentation | Observability ebook

Elastic Observability #

Elastic Observability is built on the Elastic Stack (also known as the ELK Stack) and enables observability on search to speed up root cause analysis and boost developer productivity.

Elastic Observability integrates with hundreds of technologies and offers apps for APM, logging, and metrics. Moreover, it uses a pay-as-you-go pricing so that you only pay for the hardware resources you used to store, search, and analyze your data.

The 2021 Gartner Magic Quadrant for Application Performance Monitoring named Elastic a Visionary.

The solution offers a free 14-day trial (no credit card required) to help you get started.

What are the main capabilities of Elastic Observability?

  • Ingest all telemetry data (metrics, logs, and traces) in an open and scalable platform
  • Use traces to identify performance bottlenecks across the entire tech stack
  • Leverage searchable snapshots for more log, metrics, and APM data
  • Scale both horizontally (by adding more nodes) and vertically (by adding more resources to each node) to support large-scale deployments

Elastic Observability Resources

Documentation | Introduction to Elastic Observability (Webinar) | Elastic Observability 8.1 (Updates)

Instana Enterprise Observability #

Instana Enterprise Observability enables end-to-end discovery, mapping, monitoring, and troubleshooting of containerized microservice applications. It ingests all performance metrics, lets you trace all requests, and profiles every process automatically.

Instana offers a free 14-day trial (no credit card required) to the full version of the product.

What are the main capabilities of Instana Enterprise Observability?

  • Automate root cause analysis and feedback to ensure optimum performance of your applications
  • Proactively discover issues, get answers, and perform a deep analysis with proper context
  • Use Instana’s Dynamic Graph (full-stack model), Context Guide (Architectural UX), and Unbounded Analytics (correlated analytics) to understand the correlation between app components and services

Instana Enterprise Observability Resources

Instana’s APM Observability Sandbox (simulation) | Foundations of enterprise observability (ebook) | Documentation

Lightstep Observability #

Lightstep Observability was created by the founders and maintainers of OpenTracing and OpenTelemetry. It lets you observe every upstream and downstream dependency, including third-party services, in real-time. You can also use Lightstep Observability to monitor your performance SLAs and SLOs.

Lightstep Observability supports hundreds of languages, frameworks, and platforms and promises to help you spot the root cause of any anomaly in three clicks or less.

What are the main capabilities of Lightstep Observability?

  • Understand any change (planned or unplanned) using real-time insights from your tech stack
  • Proactively detect changes to your application or infrastructure, and see how it will impact the customers and your business outcomes
  • Spend less time troubleshooting with automated root cause detection and analysis

Lightstep Resources

Lightstep Observability Learning Portal | Observability: A complete overview for 2021 (developer guide)

New Relic One #

New Relic One lets you aggregate, analyze, and visualize all the telemetry and infrastructure in one place. This helps you detect, triage, and eliminate errors faster. It enables end-to-end observability and integrates with 440+ technologies using pre-built instrumentation, dashboards, and alerts.

Getting started is free. You only pay for the hardware resources you consume at $0.25 per GB.

What are the main capabilities of New Relic One?

  • Ingest and search through logs in the right context to correlate events easily
  • Automatically spot anomalies or performance issues across all apps, services, and logs and get instant alerts
  • Correlate alerts and events from any source automatically to cut down on redundant alerts by up to 90%
  • Use NewRelic Lookout to uncover blind spots and unknown relationships

New Relic One Resources

Documentation | Full-stack observability in New Relic One (Datasheet) | 2021 Observability Forecast (Ebook)

Splunk Observability Cloud #

The Splunk Observability Cloud integrates the capabilities of NoSample™ Full-Fidelity Ingest, real-time streaming, AI/ML-driven analytics, and OpenTelemetry to improve developer productivity, reduce downtime, and improve the overall release quality and speed.

The products included in the suite are:

  • Splunk Infrastructure Monitoring
  • Splunk APM
  • Splunk RUM
  • Splunk On-call
  • Splunk Log Observer

The suite of products aims to eliminate blind spots in your tech stack and help you proactively detect problems and resolve them in minutes. The Splunk Observability Cloud can ingest petabytes of data at scale across multiple containers and clouds.

Splunk offers a free 14-day trial to help you get started.

What are the main capabilities of Splunk Observability Cloud?

  • Use Splunk Log Observer to go through logs from key DevOps sources in minutes, with no code
  • Use Splunk RUM (real user monitoring) to observe web and app performance across every transaction, resource, and third-party dependency
  • Identify the root cause of every issue, view everything from a single location, and share details with your team easily
  • Automate incident response to reduce mean time to acknowledgment and resolution (MTTA and MTTR)

Splunk Resources

Overview (Video) | Splunk Observability Suite (Introduction) | Splunk Log Observer (Product Brief)

StackState #

StackState captures topology time-series data and combines it with telemetry to help you understand why something breaks and how to fix it. So, you can travel back to any point in time and see what your environment looked like before an issue popped up.

You can continuously discover the topology and correlate it with telemetry and traces in real-time. This helps you decrease MTTR, reduce outages, and save the costs involved in detecting and triaging incidents and outages.

StackState offers a free 14-day trial (no credit card required) and even offers a sandbox to try the solution without connecting your AWS or Kubernetes environments.

What are the main capabilities of StackState?

  • Automate anomaly detection to flag potential problems and fix them before they affect your business outcomes
  • Use a single pane for root cause analysis and impact analysis
  • Deploy in minutes without investing engineering resources for configuration and maintenance

StackState Resources

StackSlate Sandbox | Features | Datasheet

Also, read → A Deep Dive into 4 Observability Tools

How organizations making the most out of their data using Atlan #

The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:

  1. Automatic cataloging of the entire technology, data, and AI ecosystem
  2. Enabling the data ecosystem AI and automation first
  3. Prioritizing data democratization and self-service

These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”

For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.

A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.

Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes #


  • Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
  • After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
  • Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.

Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.

FAQs about data observability tools #

1. What are data observability tools? #


Data observability tools enable organizations to monitor and understand the health of their data pipelines. These tools provide real-time insights into data quality, pipeline performance, and system reliability by collecting and analyzing metadata, logs, metrics, and traces.

2. How do data observability tools improve data reliability? #


By providing continuous monitoring and alerting, data observability tools help teams detect and resolve data issues proactively. This reduces downtime, ensures data accuracy, and enhances trust in data-driven decision-making.

3. What are the best data observability tools available in 2025? #


Top data observability tools in 2025 include Monte Carlo, Acceldata, AppDynamics, Amazon CloudWatch, Datadog, Dynatrace, Elastic, Instana, Lightstep, New Relic One, Splunk, and StackState These tools are highly rated on platforms like Gartner and G2 for their robust monitoring, anomaly detection, and integration capabilities.

4. How can I integrate data observability into my existing pipeline? #


To integrate data observability, start by selecting a tool that fits your ecosystem. Implement it across your data pipelines by configuring it to monitor critical metrics, logs, and events. Ensure it integrates seamlessly with your data stack and aligns with your operational goals.

5. What metrics should I monitor using data observability tools? #


Key metrics include data freshness, data lineage, error rates, pipeline latency, and schema changes. Monitoring these metrics ensures your data pipelines operate efficiently and data quality issues are quickly identified and resolved.

6. What role does automation play in data observability? #


Automation is crucial in data observability, enabling real-time anomaly detection, automated alerts, and predictive maintenance. Automated systems reduce manual intervention, ensuring faster response times and minimizing the impact of data issues.

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