Data Hub vs Dynatrace comparison

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Data Hub

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Executive Summary

We performed a comparison between Data Hub and Dynatrace based on real PeerSpot user reviews.

Find out in this report how the two AI Observability solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Data Hub vs. Dynatrace Report (Updated: June 2026).

Data Hub vs. Dynatrace

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Helped 902,894 peers since 2012

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

ROI

Sentiment score

3.0

Data Hub reduces errors, saves time in incident management, improves Power BI troubleshooting, and centralizes data cataloging and classification.

Sentiment score

6.9

Organizations achieved increased efficiency, reduced costs, and improved performance with Dynatrace, enhancing innovation, customer satisfaction, and return on investment.

Atlan has a better approach compared to Data Hub.

For more quotes and insights, download the Data Hub report

Data Quality Engineer at truelogic

Data Hub centralizes data cataloging and classification, saving us from having to disclose PII column information to teams not utilizing it.

PrashantGupta2

Software Engineer L2 at a tech vendor with 5,001-10,000 employees

It is very helpful in building data quality for the company, leading to approximately thirty percent improvement in efficiency.

Anhnq Ho

Finance Feedback Committee at MB Shinsei Finance Limited Liability Company

Using Dynatrace directly improved application uptime and reduced customer impacting incidents.

senior DevOps engineer at a tech services company with 10,001+ employees

ROI is hard to specify; however, incidents like impending ransomware attacks highlight its value, though those are exceptional events.

For more quotes and insights, download the Dynatrace report

Enterprise Architect at DXC Technology

Save money by identifying problems, thereby reducing monetary losses on their application side.

Abednego Petrus

Technical Manager, Consulting at a outsourcing company with 1,001-5,000 employees

Customer Service

Sentiment score

3.8

Data Hub's customer support is responsive and effective, utilizing Slack, webinars, and documentation for timely and genuine assistance.

Sentiment score

7.1

Dynatrace's support is responsive and expert, with swift resolutions, though complex issues may require improved response times.

When I was working with Atlan, and needed support, they were very good at attending to my requests directly.

For more quotes and insights, download the Data Hub report

Data Quality Engineer at truelogic

Customer support for Data Hub is quite good.

Shubham-Agarwal

Manager - Projects at Cognizant

Customer support for Data Hub is very genuine, and they are responsive and attentive.

Akashkhurana Hirana

Senior Software Engineer 2 at Porch

They have a good reputation, and the support is commendable.

Enterprise Architect at DXC Technology

The technical support from Dynatrace is excellent.

System Administrator at a manufacturing company with 10,001+ employees

Whenever we faced any issues, we could get timely resolution from their support.

For more quotes and insights, download the Dynatrace report

senior DevOps engineer at a tech services company with 10,001+ employees

Scalability Issues

Sentiment score

5.5

Data Hub scales efficiently, seamlessly managing growing users and datasets while integrating diverse sources for expansive organizational growth.

Sentiment score

7.3

Dynatrace is scalable, efficiently handling large deployments with strong adaptability, integration, and management, despite cost implications.

We have successfully onboarded over 1000 datasets from various sources without any issues.

Akashkhurana Hirana

Senior Software Engineer 2 at Porch

Data Hub's scalability is advantageous, as we onboard data from over one hundred fifty tables in SQL Server to Snowflake, and adding new tables to Data Hub is not time-consuming.

Shubham-Agarwal

Manager - Projects at Cognizant

Data Hub's scalability is very easy, as we were able to add users and new datasets very quickly and smoothly.

For more quotes and insights, download the Data Hub report

Data Quality Engineer at truelogic

If it's an enterprise, increasing the number of instances doesn’t pose problems.

Enterprise Architect at DXC Technology

It is a powerful tool and helped us to reduce customer downtime and increase work efficiency.

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senior DevOps engineer at a tech services company with 10,001+ employees

The scalability of Dynatrace is very significant, especially considering the current improvements in their features.

Abednego Petrus

Technical Manager, Consulting at a outsourcing company with 1,001-5,000 employees

Stability Issues

Sentiment score

8.0

Data Hub is praised for exceptional stability and reliability, with minimal downtime during upgrades, ensuring consistent performance.

Sentiment score

7.6

Dynatrace is highly reliable with minimal downtime, praised for stability, efficient resource use, and proactive uptime alerts.

Since I've been using Data Hub, it has always been very stable; I can say it was one hundred percent stable.

For more quotes and insights, download the Data Hub report

Data Quality Engineer at truelogic

When I used Data Hub, I did not experience any lagging, crashing, or downtime.

Igor Uchoa

Senior Data Engineer at a tech services company with 1-10 employees

Data Hub is stable in my experience.

PrashantGupta2

Software Engineer L2 at a tech vendor with 5,001-10,000 employees

Generally, all are stable at ninety-nine point nine nine percent, but if the underlying infrastructure is not deployed correctly, stability may be problematic.

Enterprise Architect at DXC Technology

There have been no stability issues with Dynatrace.

For more quotes and insights, download the Dynatrace report

System Administrator at a manufacturing company with 10,001+ employees

Dynatrace is a SaaS product with frequent agent management updates.

Anil Kumar-AIOK

Principal Consultant at a tech consulting company with 11-50 employees

Room For Improvement

Data Hub needs better data quality, integration, automation, user experience, and analytics to simplify features for all users.

Dynatrace needs improved UI/UX, clearer pricing, better customization, and enhanced automation with unified data and deeper integrations.

Providing consulting or support with professionals who are qualified to use Data Hub would be interesting, along with providing training and certifications for the tool so that those who are implementing it can specialize increasingly in its features.

For more quotes and insights, download the Data Hub report

Data Quality Engineer at truelogic

The impact is very positive, and there are many benefits for us using Data Hub because it was easier to make data governance, create centralized metadata management, improve data discoverability, and manage data in general.

reviewer2784462

Software Engineer at a tech vendor with 10,001+ employees

I wonder if it can automate the classification exercise, possibly using AI to auto-classify PII direct and indirect items.

Azhagarasan Annadorai

Director at a university with 1-10 employees

The definition of enterprise is loosely used, however, from a holistic security perspective, including infrastructure, network, ports, software, applications, transactions, and databases, there are areas lacking, especially in network monitoring tools.

For more quotes and insights, download the Dynatrace report

Enterprise Architect at DXC Technology

Dynatrace could enhance cost and licensing structures, as the current pricing can be expensive for large-scale deployments.

Vaibhav Karad

BizOps Engineer at a tech company with 10,001+ employees

I'm specifically looking at AIOps and how we can monitor AIOps-related things, considering we have LLMs and all that stuff.

reviewer1246755

Performance Architect at a tech vendor with 5,001-10,000 employees

Setup Cost

Dynatrace is costly but valued for features; pricing complexity challenges budgeting; discounts possible for large deployments or long-term contracts.

Regarding experience with pricing, setup cost, and licensing, I think if we have a budget of one hundred thousand US dollars, we will be able to deploy a reasonable version and connect to a number of data sources.

Azhagarasan Annadorai

Director at a university with 1-10 employees

It costs about zero since, if we win the setup, it probably results in no cost.

Anhnq Ho

Finance Feedback Committee at MB Shinsei Finance Limited Liability Company

For more quotes and insights, download the Data Hub report

Dynatrace is known to be costly, which delayed its integration into our system.

System Administrator at a manufacturing company with 10,001+ employees

If setting up in a large scale environment, it is overwhelming because it is expensive.

For more quotes and insights, download the Dynatrace report

senior DevOps engineer at a tech services company with 10,001+ employees

The cost can be controlled from our side, and it is very transparent with Dynatrace regarding DPS and licensing.

Abednego Petrus

Technical Manager, Consulting at a outsourcing company with 1,001-5,000 employees

Valuable Features

Data Hub enhances data discoverability, governance, and collaboration with features like tagging, scalability, and seamless tool integration for actionable insights.

Dynatrace enhances efficiency with AI-driven anomaly detection, real user monitoring, and comprehensive observability tools for improved user satisfaction.

Data Hub became a single source of truth for metadata, supporting both compliance requirements and day-to-day operational needs.

reviewer2784462

Software Engineer at a tech vendor with 10,001+ employees

Data Hub has positively impacted our organization by bringing the tribal knowledge that resides with team members into a single place where users can discover and understand the data elements before they make use of it.

Azhagarasan Annadorai

Director at a university with 1-10 employees

Having a tool that shows the data lineage from the source until the target tables helps us a lot.

For more quotes and insights, download the Data Hub report

Data Quality Engineer at truelogic

The integration with Power BI for generating detailed reports is a standout feature.

For more quotes and insights, download the Dynatrace report

System Administrator at a manufacturing company with 10,001+ employees

Dynatrace's AI-driven Davis engine absolutely helps identify performance issues by showing root cause analysis for us up to 200%; whatever is integrated, if it is visible, it can stitch and show.

reviewer1935579

Technical Associate at a manufacturing company with 10,001+ employees

Dynatrace links compute with services and services with code and other components.

Anil Kumar-AIOK

Principal Consultant at a tech consulting company with 11-50 employees

Categories and Ranking

Data Hub

Ranking in AI Observability

7th

Average Rating

8.2

Reviews Sentiment

4.8

Number of Reviews

Ranking in other categories

Metadata Management (4th)

Dynatrace

Ranking in AI Observability

3rd

Average Rating

8.8

Reviews Sentiment

7.0

Number of Reviews

359

Ranking in other categories

Application Performance Monitoring (APM) and Observability (2nd), Log Management (5th), Mobile APM (3rd), Container Monitoring (2nd), AIOps (2nd)

Mindshare comparison

As of July 2026, in the AI Observability category, the mindshare of Data Hub is 0.6%. The mindshare of Dynatrace is 3.3%, down from 25.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Observability Mindshare Distribution
Product	Mindshare (%)
Dynatrace	3.3%
Data Hub	0.6%
Other	96.1%

AI Observability

Featured Reviews

Akashkhurana Hirana

Senior Software Engineer 2 at Porch

Metadata management has streamlined lineage tracking and data discovery for our teams

The best features Data Hub offers include its integration capability with many popular tools like Apache Airflow, Snowflake, dbt, Looker, Apache Kafka, and BigQuery. These tools provide us with data in various places, and we commonly use Apache Airflow for the DAG, while utilizing BigQuery as our database and Apache Kafka for consuming messaging queues. Data Hub easily connects with all these tools and features excellent data discovery and visualization capabilities. We can see data visibility, where it comes from, its upstream and downstream relationships. If we remove a column, we can assess the impact of that change. Furthermore, if there are duplicate datasets being used by different teams that do not communicate regularly, onboarding all data to Data Hub allows us to identify these duplicates easily. Out of all those features, I believe data discovery and impact analysis are the most valuable for my team because when we want to add or drop a column, we can assess the impact analysis to understand the downstream effects. This helps us know who owns a dataset, and we can easily contact the owner. Tracking the data lineage back to the source table is also a key benefit. Data Hub has positively impacted my organization by significantly reducing manual work that was previously needed to identify upstream and downstream data relationships, as well as recognizing duplicate datasets. If a data contract is broken, we now easily get notified of those issues, making the process much easier and more efficient. It is particularly useful for data engineers and platform teams to check for problems directly within Data Hub. Data Hub has saved our team a lot of time. For example, in a large company like Porch, if I want to know whether a specific dataset exists, I can check Data Hub, as it serves as a centralized point for managing the metadata of our data. While it does not contain all data, it does contain the metadata necessary for understanding the dataset's origin. If a dataset does not exist, I can simply see who the owner is and reach out to them, which reduces the dependency on others by providing direct access to information in Data Hub.

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AI-driven insights have reduced downtime and improved cross-team collaboration

senior DevOps engineer at a tech services company with 10,001+ employees

We encountered some challenges while using Dynatrace. Although the initial setup was smooth, fine-tuning alert thresholds and custom metrics took some time. Another challenge was that Dynatrace charges based on host units, so we had to carefully plan our agent deployments. The licensing model is expensive. Additionally, the complexity of setup is an issue. While OneAgent and auto-discover services are powerful, the setup is more complex compared to other tools such as Prometheus and Grafana. These integrations are simple and basic, but Dynatrace setup requires more complexity based on the environment. For new users wanting to use Dynatrace, it is difficult. However, the AI-related solutions and metrics took us to the next level for identifying and fixing things. Dynatrace requires an agent for operation. OneAgent is powerful, but it is also resource-heavy. On lightweight nodes or older systems, the agent can slightly impact performance. If Dynatrace could implement a lightweight agent behavior, we could make things faster. Additionally, if Dynatrace could add a long-term retention policy so that we could store more data and find fine-grained details, that would help us. While Dynatrace managed edition supports on-premises deployment, the SaaS version depends on cloud connectivity. For highly regulated or air-gapped environments, setup and updates can be challenging. Although the initial setup is smooth, if someone wants to fine-tune it and fully understand the tool end-to-end, it could be tricky.

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Outsourcing Company

12%

Wholesaler/Distributor

Manufacturing Company

Financial Services Firm

20%

Manufacturing Company

Computer Software Company

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	7
Large Enterprise	14

By reviewers
Company Size	Count
Small Business	80
Midsize Enterprise	50
Large Enterprise	299

Questions from the Community

What needs improvement with Data Hub?

I think Data Hub can be improved by supporting the open source version better. Many features have moved to the paid version now, making it difficult for small-scale companies to operate on Data Hub...

What is your primary use case for Data Hub?

My main use case for Data Hub is that we use it as a library for all the data assets that we generate. It serves as an internal data mart where people can search for whatever data they need, and th...

What advice do you have for others considering Data Hub?

Data Hub does most of the job it is designed to do, but there could still be improvement as the industry progresses, particularly around metadata discovery. Regarding Data Hub's AI capabilities, it...

Any advice about APM solutions?

The key is to have a holistic view over the complete infrastructure, the ones you have listed are great for APM if you need to monitor applications end to end. I have tested them all and have not f...

What cloud monitoring software did you choose and why?

While the environment does matter in the selection of an APM tool, I prefer to use Dynatrace to manage the entire stack. Both production and Dev/Test. I find it to be quite superior to anything els...

Any advice about APM solutions?

There are many factors and we know little about your requirements (size of org, technology stack, management systems, the scope of implementation). Our goal was to consolidate APM and infra monitor...

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Product Reports

Data Hub

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Also Known As

Acryl Data

No data available

Overview

Data Hub is an advanced platform designed to streamline data management processes, enhance data accessibility, and provide comprehensive analytics capabilities for informed decision-making.

Data Hub offers a unified approach to handling large-scale datasets, empowering organizations to effectively manage, analyze, and extract insights from their data infrastructure. It provides robust features for data integration, storage, and visualization, supporting diverse business needs and driving data-driven strategies.

What are the key features of Data Hub?

Data Integration: Seamlessly combines data from multiple sources for cohesive analysis.
Scalability: Supports growing data needs without performance degradation.
Analytics Tools: Enables advanced data processing and insightful reporting.
Security: Robust security protocols to ensure data compliance and protection.
Custom Dashboards: Offers customizable interfaces for tailored analytics displays.

What benefits do users experience with Data Hub?

Increased Efficiency: Streamlines data processes, reducing time and resource spent.
Cost-Effectiveness: Minimizes need for multiple data management tools.
Improved Decision Making: Provides actionable insights through data analytics.
Enhanced Collaboration: Facilitates data sharing across departments, encouraging teamwork.

Data Hub is implemented across industries such as finance, healthcare, and retail, providing tailored solutions that meet specific demands in areas like customer data analysis, patient record management, and inventory tracking. Its ability to adapt to sector-specific requirements makes it a versatile choice for businesses seeking enhanced data capabilities.

DataHub

Dynatrace offers AI-driven root cause analysis, full-stack observability, and more. Its seamless integration and automated alerts enhance operational efficiency for application performance monitoring across diverse environments.

Dynatrace provides users with comprehensive tools for proactive monitoring, leveraging AI-powered insights to detect bottlenecks and monitor user behavior. It enhances system dependency visualization via Smartscape and offers deep transaction insights through PurePath. Session Replay captures real user experiences, while custom dashboards emphasize essential metrics. Integration capabilities and seamless deployment are key, though users face challenges with navigation, integration, and licensing. Enhancing third-party training tools and optimizing real-time AI diagnostics is desired, with demands for better database monitoring reports and simpler UI.

What are Dynatrace's key features?

AI-Driven Root Cause Analysis: Identifies issues with precision.
Full-Stack Observability: Monitors across all platforms.
Smartscape: Visualizes system dependencies.
PurePath: Provides transaction flow insights.
Session Replay: Captures user interactions.
Custom Dashboards: Focuses on essential metrics.
Automated Alerts: Minimizes downtime.

What benefits should users consider?

Operational Efficiency: Enhances performance management.
Seamless Deployment: Reduces manual intervention.
Integration Capabilities: Ensures smooth system coexistence.
User Experience Insights: Tracks behavior and reliability.

Dynatrace is implemented in industries like finance for monitoring infrastructure and user experience. In manufacturing, it helps ensure system reliability. Its AI-driven approach is crucial for cloud deployments, supporting performance optimization and proactive monitoring.

Sample Customers

Information Not Available

Audi, Best Buy, LinkedIn, CISCO, Intuit, KRONOS, Scottrade, Wells Fargo, ULTA Beauty, Lenovo, Swarovsk, Nike, Whirlpool, American Express

Data Hub vs. Dynatrace