Monday, July 22, 2024

7 Top Data Observability Tools for 2024

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The best data observability tools let you monitor, maintain, and manage your enterprise’s data to ensure it is accurate, reliable, and available to everyone who needs it. By automating monitoring and tracking the many concerns of your data estate and detecting anomalies in near real-time, data observation tools help identify and resolve issues before they become unmanageable.

We evaluated the most popular options on the market today against key business requirements to see how they compared on features and price—here are our recommendations for the best data observability tools for 2024:

Top Data Observability Tool Comparison At a Glance

The table below summarizes the core features of our top data observability tools and provides details on the free trial duration and pricing offers of each vendor.

Data Pipeline Monitoring Data Lineage and
Dependency Mapping
Root Cause Analysis Free Trial Duration Pricing

Data ingestion, storage, and consumption

Data transformation

Advanced Superior 15 days Starts at $0.08
SolarWinds Observability Data ingestion, storage, and consumption Basic Superior 30 days Starts at $5
Datadog Observability Platform

Data ingestion, storage, and consumption

Data transformation

Advanced Superior 14 days Starts at $18
Grafana Cloud

Data ingestion, storage, and consumption

Data transformation

Advanced Superior 14 days Starts at $0
(free version)
Cisco AppDynamics

Data ingestion, storage, and consumption

Data transformation

Advanced Superior 15 days Starts at $.06
Splunk Observability Cloud

Data ingestion, storage, and consumption

Data transformation

Advanced Superior 14 days Starts at $15
IBM Databand

Data ingestion, storage, and consumption

Data transformation

Advanced Standard N/A* N/A*

*Vendor does not disclose information

Dynatrace icon.


Best Overall

Overall Rating: 4.4/5

  • Costs: 3.5/5
  • Core Features: 5/5
  • Integrations: 4.3/5
  • Customer Support: 4.7/5
  • Ease of Use: 3.8/5

Dynatrace delivers observability, application performance monitoring (APM), and digital experience capabilities and follows a unified approach to monitoring and optimizing applications, infrastructure, and user experience in real-time. This tool’s observability features include full-stack visibility, automatic and intelligent observability across cloud and hybrid environments, and continuous auto-discovery of hosts, virtual machines (VMs), serverless, cloud services, containers, and Kubernetes.

Dynatrace emerged as our best data observability tool overall because of its AI-powered, automated capabilities that align well with the principles of continuous integration, delivery, and iterative improvement. This tool is a lifesaver for teams by automating monitoring and troubleshooting, freeing up their time to focus on more strategic projects. It also provides real-time visibility across the entire software stack, which helps teams identify and resolve issues promptly, elevating the quality and reliability of their software delivery. And, by monitoring the digital experience, teams can gain insights into how users interact with their applications, allowing them to make improvements based on real-time feedback.

In addition to its advanced features, Dynatrace integrates with a wide variety of third party tools and offers a broad selection of customer support options to its users. The tool has a simple configuration and can be deployed quickly.

Screenshot showing the Dynatrace user interface.
The Dynatrace user interface.

Product Design

Dynatrace’s interface features multiple visualizations including line graphs, bar graphs, and maps presented on a single page. This page displays various information at a glance, like the status of applications, services infrastructure, and database. While the design makes data easily accessible, it is a bit cluttered and could be confusing, especially for new users.

Product Development

Dynatrace’s new OpenPipeline streamlines petabyte-scale data ingestion into the platform, enhancing secure, cost-effective analytics, AI, and automation. OpenPipeline gives teams comprehensive control over their data and processes data streams faster than traditional technologies. This recent update expands the utility of the Dynatrace platform, enabling more teams to leverage its AI-driven insights and automations.

Why We Picked Dynatrace

Dynatrace is on top of our recommended data observability tools due to its advanced capabilities and adaptability to various use cases. It performed well in terms of pricing offers, built-in features, customer support, and integrations. Whether you’re a part of a DevOps team looking to streamline your workflows, an IT operations team aiming to maintain high availability, or a business leader seeking to align IT performance with business outcomes, Dynatrace offers a powerful and flexible solution for your business.

Pros and Cons

Pros Cons
Full-stack visibility Steep learning curve
Solid real-user monitoring Poor documentation
Clear pricing Occasional false positives in alerts


  • Full-Stack Monitoring: $0.08 per hour
  • Infrastructure Monitoring: $0.04 per hour
  • Kubernetes Monitoring: $0.002 per hour
  • Application Security: $0.018 per hour
  • Real User Monitoring: $0.00225 per session
  • Synthetic Monitoring: $0.001 per synthetic request

Log Management and Analytics

  • Ingest & Process: $0.20 per GiB
  • Retain: $0.0007 per GiB, per day
  • Query: $0.0035 per GiB

Learn more on Dynatrace’s pricing page.


  • Unified observability and security
  • Causal AI
  • Full-stack monitoring
  • Application security
  • Digital experience monitoring
  • Business analytics
  • Cloud-native architecture

SolarWinds icon.

SolarWinds Observability

Best for Complete IT Infrastructure Visibility

Overall Rating: 4.3/5

  • Costs: 3.5/5
  • Core Features: 5/5
  • Integrations: 4.3/5
  • Customer Support: 4.7/5
  • Ease of Use: 3.8/5

SolarWinds Observability is an integrated solution that connects data from web applications, their services, cloud, and hybrid infrastructure for holistic business insights, operational intelligence, and smart automation. This data observability tool simplifies the complexity of managing and monitoring distributed environments and helps DevOps and IT Ops teams fine-tune the performance and ensure the reliability of business-critical systems.

SolarWinds Observability gives comprehensive visibility across various aspects of IT infrastructure. It unifies data from logs, metrics, traces, database queries, and user experience, offering a single source of truth for your organization. The tool does this through automated instrumentation and dependency mapping, which present an in-depth view of the application and server connections. As a result, teams can visualize and continuously analyze business service and component relationships, deviations, and dependencies. Additionally, the platform’s support for open-source frameworks and third-party integrations highlights its versatility and compatibility with different IT environments.

Screenshot showing the SolarWinds Observability interface.
The SolarWinds Observability user interface.

Product Design

SolarWinds Observability has a highly-detailed interface with separate tabs that allow you to monitor database, network, log, infrastructure, and digital experiences. Clicking on each tab displays a different dashboard, each of which has panels containing multiple charts. While undeniably informative, the design could be overwhelming to some users.

Product Development

SolarWinds upgraded its observability solutions with complete hybrid visibility across on-premises and cloud networks. This includes VMs, hypervisors, containers, Kubernetes, and Infrastructure-as-a-Service (IaaS) resources. SolarWinds has also improved its database monitoring capabilities with the addition of a query explorer and visual explain plans. These tools can help your business better understand and optimize database operations, boosting performance and efficiency.

Why We Picked SolarWinds Observability

We picked SolarWinds Observability for several reasons. It provides total visibility and a unified approach to data, and it offers advanced diagnostic tools that help give insights into the health of the IT infrastructure. It also has flexible hosting options that can accommodate cloud-native, on-premises, or hybrid environments. This flexibility allows the platform to scale and meet the needs of businesses, regardless of their size.

Pros and Cons

Pros Cons
AIOps-enhanced Steep learning curve
Extensive support options Complex management
Clear pricing Time-consuming customization


  • Application Observability: Starts at $27.50 per application instance
  • Network and Infrastructure Observability: Starts at $15.00 per active network device or host
  • Log Observability: Starts at $5.00 per GB
  • Database Observability: Starts at $70.00 per database instance
  • Digital Experience Observability (Synthetic): Starts at $10.00 per 10 uptime checks
  • Digital Experience Observability (Real User Monitoring): Starts at $10.00 per 10 uptime checks

Learn more on the SolarWinds pricing page.


  • Unified single source of truth
  • Support for open-source frameworks
  • AIOps-enhanced with machine learning
  • Automated instrumentation and dependency mapping
  • Modular options for scalability
  • Cloud-native, on-premises, and hybrid monitoring
  • Option for self-hosted and on-premises monitoring

Datadog icon.

Datadog Observability Platform

Best for Complex Technology Stacks

Overall Rating: 4.2/5

  • Costs: 3.5/5
  • Core Features: 4.9/5
  • Integrations: 5/5
  • Customer Support: 3.5/5
  • Ease of Use: 3.8/5

The Datadog Observability Platform provides end-to-end visibility into the health and performance of your entire tech stack and simplifies the monitoring and management of complicated systems by bringing metrics, traces, and logs together. What sets Datadog apart is its ability to integrate with over 700 technologies and services, making it an ideal choice for complex tech stacks. It can give a full view of the health and performance of each layer of your environment and allow for customized insights tailored to your specific stack.

Its Observability Pipelines capability facilitates the collection, processing, and routing of logs from any source to any destination in the infrastructure that you own or manage. This is complemented by AI-powered anomaly detection, which simplifies the complexity of modern systems by identifying unusual patterns or behaviors. Last but not least, Datadog promotes collaboration among teams by giving a shared single pane of glass so team members can resolve issues quickly and efficiently.

Screenshot showing the Datadog Observability Platform interface.
The Datadog Observability Platform user interface.

Product Design

The Datadog Observability Platform interface is highly-detailed, well-structured, and intuitive. Despite presenting a wealth of data, it organizes information in a way that is easy to understand and navigate. Additionally, it lets you customize dashboards so you can focus on the data that matters most to you, reducing clutter and information overload.

Product Development

Datadog recently expanded its security and observability support for AWS serverless applications built on AWS Lambda and Step Functions services. This gives users better visibility into serverless applications, increasing efficiency in troubleshooting of issues, as well as security. This expansion also proves Datadog’s commitment to adapting to the evolving needs of its users.

Why We Picked Datadog Observability Platform

Datadog Observability Platform made it to our list because of its unified view, sophisticated features, and unique offerings that ensure the health and performance of tech stacks. Aside from that, it’s not limited to a specific type of infrastructure—it can monitor cloud, hybrid, or multi-cloud environments, as well as on-premises servers.

Pros and Cons

Pros Cons
Extensive third-party integration Separate support plan purchase required to get in touch with Datadog customer support team
Full-stack observability Steep learning curve
Clear pricing Resource-intensive


  • Infrastructure Pro: Starts at $18 per host, per month
  • Infrastructure Enterprise: Starts at $27 per host per month
  • Infrastructure DevSecOps Pro: Starts at $27 per host per month
  • Infrastructure DevSecOps Enterprise: Starts at $41 per host per month

Learn more on Datadog’s pricing page.


  • Unified observability platform
  • AI-powered anomaly detection
  • Watchdog identifies symptoms and pinpoints causes
  • Over 700 integrations
  • Observability pipelines

Grafana icon.

Grafana Cloud

Best for Observability Cost Management

Overall Rating: 3.8/5

  • Costs: 4.5/5
  • Core Features: 4.5/5
  • Integrations: 3.9/5
  • Customer Support: 2.5/5
  • Ease of Use: 2.5/5

Grafana Cloud brings a suite of observability features that enable real-time monitoring of your infrastructure, applications, and logs. Some of its key functionalities include Grafana Cloud Application Observability, Grafana Service Level Objective (SLO), Adaptive Metrics, and Grafana Live. Together, these provide a consolidated view of your system’s health and performance, making it easier to find and address issues promptly.

This tool has a strong focus on observability cost management, with special features that support you in analyzing, attributing, monitoring, and optimizing your metrics and logs usage, so you can minimize costs without compromising your observability strategy. Additionally, it has customizable dashboards that let you know which information matters most, potentially reducing the volume of data you need to store and process.

Grafana Cloud’s free tier delivers an affordable entry point for smaller businesses or teams just starting with their observability journey.

Screenshot showing the Grafana Cloud interface.
The Grafana Cloud user interface.

Product Design

Grafana Cloud’s interface displays an abundance of visualizations including line graphs, bar data source permissions, reporting, and usage insights. It has a sidebar with icons that let you display different sets of information. While this is highly informative, the interface is quite busy and can be difficult to comprehend due to its complexity.

Product Development

Grafana Labs has acquired to refine Grafana Cloud’s ability to understand observability data and detect issues. This acquisition will provide more detailed insights into how system components interact, making it easier to analyze the root cause of issues and offering more in-depth, context-aware analytics. As a result, Grafana users will be able to understand their applications better and resolve issues more quickly, enhancing the overall user experience.

Why We Picked Grafana Cloud

We chose Grafana Cloud for its versatility and cost-effectiveness. It integrates with numerous data sources and comes with real-time, customizable dashboards that make it a flexible solution for diverse use cases. On top of that, only Grafana Cloud offers a free tier out of all the tools we evaluated, making it a practical choice for organizations of all sizes. Whether you’re a small startup or a large enterprise, it can equip you with the observability features you need at a cost that fits your budget.

Pros and Cons

Pros Cons
Free version Requires advanced technical expertise
Highly customizable Limited customer support options
Clear pricing Limited reporting capabilities


  • Pro Pay As You Go: Starts at $0, then pay for any usage beyond the free tier
  • Advanced Premium Bundle: Starts at $299 per month

Learn more on the Grafana Cloud pricing page.


  • Natively supports both OpenTelemetry, an observability framework (API), and Prometheus, a monitoring and alerting toolkit
  • Grafana SLO for service level objectives management and tracking
  • Adaptive metrics
  • Unified application and infrastructure observability
  • Cost management
  • Grafana Live supports streaming
  • Role-based access control (RBAC) update

Cisco icon.

Cisco AppDynamics

Best for Application Performance Monitoring

Overall Rating: 4/5

  • Costs: 3.5/5
  • Core Features: 5/5
  • Integrations: 3.5/5
  • Customer Support: 4/5
  • Ease of Use: 3.8/5

Cisco AppDynamics delivers a wide range of features to help you monitor and optimize application performance across your organization. It provides AI/ML capabilities, code-level visibility, business transaction correlation, process monitoring, cross-domain visibility, and correlated metrics, events, logs, and traces (MELT) data. It also supports multi-cloud infrastructure and has multi-cloud Kubernetes observability.

AppDynamics is particularly well-suited for application performance management (APM) because it can present end-to-end visibility into application behaviors, pinpoint root causes of application problems in real time, and give insights for remediation. Its code-level visibility allows for a deeper understanding of application performance, and is particularly beneficial for development teams looking to optimize performance and detect bottlenecks.

Screenshot showing the Cisco AppDynamics interface.
The Cisco AppDynamics user interface.

Product Design

Cisco AppDynamics has a user-friendly and simple interface, featuring a few but valuable graphs and information neatly grouped together. It also has top navigation menus that let you select which specific type of data to display, reducing confusion. This balance between simplicity and thoroughness makes it easier to see relevant data and decreases information overload.

Product Development

Cisco AppDynamics recently introduced the Smart Agent feature that optimizes agent lifecycle management, increases operational efficiency, and bolsters security, giving users a more streamlined and efficient user experience.

Why We Picked Cisco AppDynamics

We selected Cisco AppDynamics because it can deliver full-stack business insights and real-time system performance metrics to aid IT teams quickly uncover and resolve critical issues. It’s also easy to set up, integrates well with other technologies, and is part of Cisco’s portfolio, ensuring future performance.

Pros and Cons

Pros Cons
Code-level visibility identifies performance bottlenecks and optimizes code Steep learning curve
Business transaction monitoring and database performance trending Limited native integrations
Clear pricing Limited dashboard functionalities


  • Infrastructure Monitoring Edition: $6 per month, per CPU core
  • Premium Edition: $33 per month, per CPU core
  • Enterprise Edition: $50 per month, per CPU core
  • Enterprise Edition for SAP Solutions: $95 per month, per CPU core
  • Real User Monitoring: $.06 per month, per 1,000 tokens
  • Cisco Secure Application: $13.75 per month, per CPU core

Learn more on the Cisco AppDynamics pricing page.


  • AI/ML insights and automated actions
  • Code-level visibility
  • Business transaction correlation
  • Process monitoring
  • Cross-domain visibility
  • Correlated MELT data
  • Multi-cloud infrastructure and Kubernetes observability

Splunk icon.

Splunk Observability Cloud

Best for Large-Scale Deployments

Overall Rating: 4.3/5

  • Costs: 3.5/5
  • Core Features: 5/5
  • Integrations: 5/5
  • Customer Support: 4/5
  • Ease of Use: 2.5/5

Splunk Observability Cloud brings a comprehensive view of IT infrastructure, applications, and user interfaces by collecting real-time metrics, traces, and logs to give a complete and accurate picture of your systems. Unlike traditional data observability tools, Splunk Observability Cloud collects all data without sampling, ensuring you have the most faithful representation of your environment. This allows you to swiftly identify and troubleshoot issues, optimize performance, and improve the user experience.

The tool is designed to handle massive amounts of data, making it ideal for large-scale deployments. It supports more than 100 open standards-based integrations, letting it ingest data from a wide variety of sources, both on-premise and in the cloud. Also, its real-time streaming analytics capability shows immediate insights and faster problem resolution, even in the most complex and high-volume environments.

In addition, Splunk Observability Cloud also has advanced diagnostic algorithms and techniques, like AI-directed troubleshooting and automated root cause identification. These features accelerate finding and resolving issues in enterprise data, minimizing downtime and boosting optimal performance.

Screenshot showing the Splunk Observability Cloud interface.
The Splunk Observability Cloud user interface.

Product Design

Splunk has a modernized, sleek, and user-centric interface that prioritizes ease of navigation and comprehension. The clean layout lets users focus on the critical information without unnecessary distractions. Despite this simplicity, the interface is incredibly informative, showing users the insights they need to make informed decisions.

Product Development

Cisco acquired Splunk in 2023, strengthening security and resilience for existing Splunk users while improving network observability. Splunk’s expertise in data analytics will complement Cisco’s security cloud strategy, providing a more comprehensive suite of tools for managing security threats and vulnerabilities.

Why We Picked Splunk Observability Cloud

Splunk Observability Cloud stands out as one of our preferred choices due to its impressive array of features, scalability, and dedication to open standards. Also, its robust integrations with various third-party applications and diverse customer support options make it an optimal solution for organizations seeking effective data observability.

Pros and Cons

Pros Cons
Full-fidelity tracing and unified metrics, traces, and logs Resource intensive
AI-directed troubleshooting Steep learning curve
Clear pricing Report extraction is time consuming


  • Infrastructure Tier: Starts at $15 per host, per month
  • App and Infra Tier: Starts at $60 per host, per month
  • End-to-End Tier: Starts at $75 per host, per month

Learn more on Splunk’s pricing page.


  • Full-fidelity monitoring across infrastructure, applications, and user interfaces
  • Full-fidelity ingestion, real-time streaming analytics, and scalability
  • Unified metrics, traces, and logs
  • AI-directed troubleshooting
  • Support for open standards-based integrations
  • Unified entity model
  • Prepackaged integrations
  • Splunk On-Call Integration for expediting incident response

IBM icon.

IBM Databand

Best for Data Lineage and Impact Analysis

Overall Rating: 3.2/5

  • Costs: 0/5
  • Core Features: 4.5/5
  • Integrations: 3.5/5
  • Customer Support: 4.8/5
  • Ease of Use: 2.5/5

IBM Databand is an observability tool made for data pipelines and warehouses. It introduces a range of features that automatically collects metadata to build historical baselines, detect anomalies and triage alerts to remediate data quality issues. Its core strength is its data lineage and impact analysis feature—it provides a complete view of your data’s journey from its source to its final destination and helps you understand the impact of data incidents on upstream and downstream data flows.

This is valuable in finding the root cause of data issues and implementing corrective measures. By providing a clear map of how data is transformed and moved across different systems, it enables organizations to trace any data element back to its source, which is crucial for troubleshooting, impact analysis, and regulatory compliance.

Screenshot showing the IBM Databand interface.
The IBM Databand user interface.

Product Design

IBM Databand boasts a minimalistic and intuitive interface. Its clean aesthetic, free from clutter, promotes a user-friendly experience that is not overwhelming. The side panel features easily recognizable icons, giving quick access to different functionalities. The interface also incorporates well-placed graphs with relevant information at a glance, further enhancing the usability of the platform without overloading you with data.

Product Development

In 2023, IBM Databand made a significant update to enhance its integration with Snowflake and added a new RBAC  with Databand Groups. This update allows for better “data-at-rest” monitoring by applying out-of-the-box data quality alerts on Snowflake tables. It also heightens overall platform security and refines data access management.

Why We Picked IBM Databand

Despite the lack of pricing transparency, we still chose IBM Databand because its capabilities greatly outweigh its cost ambiguity. This tool has a wide-ranging feature set that delivers deep insights into data systems, including incident management, pipeline monitoring, data quality monitoring, anomaly detection, data lineage and  impact analysis, and alerting. IBM Databand’s scalability is another strong point, capable of handling large data volumes and growing alongside business needs. This future-proofing aspect can provide long-term value for your business.

Pros and Cons

Pros Cons
Automated metadata collection and anomaly detection No free trial
Historical baselines Lacks pricing transparency
End-to-end data lineage Steep learning curve


  • Pricing information not published
  • Contact IBM Databand sales to learn more


  • Automated metadata collection
  • Historical baselines
  • Automated remediation
  • End-to-end data lineage
  • Automated anomaly detection and alerts
  • Single pane view
  • Incident management
  • Integration with AI/ML

5 Key Features of Observability Tools

Data observability tools enable your data teams to monitor, manage, and maintain data assets in a proactive and efficient manner. Collectively, the key features enable data observability by providing visibility, insights, and diagnostic capabilities across the data lifecycle, ultimately driving improved business outcomes and competitiveness.

Data Monitoring

This is a key component of data observability tools as it allows businesses to keep track of data pipelines, streams, and systems in real-time. This helps you make sure that your enterprise data is flowing efficiently. By continuous data monitoring, you can guarantee the smooth operation of data management processes and quickly address any issues that may arise, preventing downtime and productivity loss.

Data Quality Monitoring

Data quality monitoring ensures the integrity and reliability of the data being analyzed. By continuously tracking data quality, you can detect inconsistencies, errors, or anomalies in your datasets, allowing you to maintain accurate insights and make informed decisions. Inaccurate or incomplete data can lead to flawed analyses, misguided business strategies, and financial losses.

Data Lineage and Dependency Mapping

These are fundamental features within a data observability tool, providing organizations with valuable information into the origin, transformation, and flow of data across their systems and processes. These capabilities let stakeholders trace the journey of data from its source through various stages of transformation to its destination.

Data lineage and dependency mapping offer a holistic understanding of data flows, so organizations can maintain data accuracy, consistency, and compliance throughout the data lifecycle. By mapping dependencies between different data assets, systems, and applications, you can precisely identify potential bottlenecks, points of failure, and areas for optimization within your data infrastructure.

Data Profiling

This feature enables you to gain deep insights into the structure, content, and quality of your enterprise data. By analyzing datasets, data profiling helps spot patterns, anomalies, and inconsistencies, so your business can assess data quality, integrity, and relevance. This feature improves data governance and the overall effectiveness of data-driven initiatives.

Root Cause Analysis

Root cause analysis helps you determine the underlying reasons behind data issues or anomalies. By pinpointing the root causes of problems, you can implement targeted solutions to prevent recurrence. This capability empowers your organization to address data issues at their source, fostering greater resilience and efficiency in data operations.

How We Evaluated The Observability Tools

To make sure that our assessment for this top data observability tools review is based on data, we carefully compared and evaluated several observability tools. Our assessment consists of five major criteria: core features, cost, customer support, integrations, and ease of use.

We gauged each tool’s performance against each of these criteria and gave scores based on their effectiveness. Then, we computed the total scores for each of the data observability tools.

Core Features | 30 Percent

We evaluated the core features of the data observability tools by checking whether they have standard features, such as data monitoring, data quality monitoring, data lineage and dependency mapping, data profiling, and root cause analysis. We also looked for additional capabilities, like data governance and compliance, real-time insights and alerts, and proactive issue identification. We not only checked if these features exist, but also how advanced they are.

Criteria Winner: Dynatrace, Cisco AppDynamics, Splunk

Costs | 20 Percent

For this criteria, we considered pricing transparency, duration of free trials, and availability of free versions of the data observability tools.

Criteria Winner: Grafana Cloud

Integrations | 20 Percent

For integrations, we researched if the tools offer out-of-the-box integrations with critical third-party solutions, such as data storage systems, data processing frameworks, ETL tools, data cataloging tools, data governance solutions, and cloud service providers. We also verified if they support custom integrations.

Criteria Winner: Datadog Observability Platform, Splunk

Customer Support | 20 Percent

For the customer support criteria, we factored in the 24×7 availability of live chat, phone, and email support, active community, and in-depth documentation or knowledge base to all users across all payment tiers. We took customer support knowledge and response times into consideration.

Criteria Winner: SolarWinds Observability

Ease of Use | 10 Percent

The ease of management and installation or configuration of the data observability tools were considered in calculating the scores for this criteria. We collated user feedback from different independent sources to find out how easy each tool is to handle for users of all skill levels.

Criteria Winner: Dynatrace, Datadog Observability Platform, Cisco AppDynamics

Bottom Line: Data Observability Tools Give Comprehensive Data Insights

The goal of these top data observability tools recommendations is to give you an idea about the most trusted names in data observability today and help you look out for the features you should be prioritizing in a data observability tool.

Before you make your final pick, consider the variety and volume of data sources in your environment, including databases, cloud services, applications, and streaming platforms. Make sure that the tool supports the ingestion and monitoring of data from these sources. Take advantage of free trials to experience working with the tools firsthand, allowing you to assess their compatibility and suitability for your specific environment and use cases.

Elevate your data management game by integrating data observability tools with cutting-edge data pipeline solutions. Read our guide and find out what are our top picks for the best data pipeline solutions.

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