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: September 2026).

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Data Hub vs. Dynatrace

September 2026

Helped 915,341 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

2.9

Data Hub boosts efficiency via time savings and error reduction, but Atlan is quicker for specific tasks, with mixed ROI feedback.

Sentiment score

6.8

Dynatrace delivers significant ROI by enhancing efficiency, reducing costs, and improving application performance through automation and integration.

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.

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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 service is responsive and helpful, with useful forums and webinars, though some report occasional setup challenges.

Sentiment score

7.1

Dynatrace support is praised for responsiveness, expertise, and efficient chat, despite occasional response delays, enhancing customer satisfaction.

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.

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

Scalability Issues

Sentiment score

5.4

Data Hub scales efficiently, handling diverse data sources, though optimization is needed for extensive datasets, supporting data mesh.

Sentiment score

7.2

Dynatrace offers exceptional scalability and effective monitoring for cloud-native and hybrid environments, simplifying management and expansion despite costs.

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 and Acryl Data are highly stable, reliable, and comparable to Oracle with minimal downtime and rare minor issues.

Sentiment score

7.6

Dynatrace is highly stable and reliable, with minimal downtime and strong support, rated highly by most users.

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

It is quite reliable and on par with what is created using the Oracle database.

reviewer2849139

Lead Business Analyst at a tech vendor with 10,001+ employees

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

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.

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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 integration, automation, UI, analytics, and security, with enhancements in metadata, memory, and AI functions.

Enhancements for Dynatrace include better session replay, licensing, dashboards, integration, support, pricing transparency, documentation, and legacy application support.

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.

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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

Enterprise users find Data Hub affordable and effective, connecting multiple data sources with a $100,000 budget, with favorable feedback.

Dynatrace's perceived high cost is justified by its comprehensive features, requiring careful budget and licensing management planning.

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

Your initial investment in licensing costs pales in comparison to the question of why you would charge a license fee and usage for a tool if you are going to be three times higher in processing the data you are sending via the agents.

Brad Dyke

DSRE Director Of Dev Ops/Operations (Management Executive) at a outsourcing company with 51-200 employees

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

Valuable Features

Data Hub enhances data exploration, collaboration, and governance with seamless integrations, metadata management, and user-friendly interface, boosting efficiency.

Dynatrace's AI-powered features and user-friendly interface boost system visibility, enhance performance, and improve incident response for IT teams.

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

9th

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

361

Ranking in other categories

Application Performance Monitoring (APM) and Observability (2nd), Log Management (6th), Mobile APM (3rd), Container Monitoring (2nd), AIOps (2nd)

Mindshare comparison

As of October 2026, in the AI Observability category, the mindshare of Data Hub is 0.6%. The mindshare of Dynatrace is 2.5%, down from 24.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Observability Mindshare Distribution
Product	Mindshare (%)
Dynatrace	2.5%
Data Hub	0.6%
Other	96.9%

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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915,341 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

16%

Outsourcing Company

13%

Construction Company

Manufacturing Company

Financial Services Firm

18%

Outsourcing Company

Manufacturing Company

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	7
Large Enterprise	15

By reviewers
Company Size	Count
Small Business	81
Midsize Enterprise	50
Large Enterprise	300

Questions from the Community

What needs improvement with Data Hub?

Data Hub can be improved with easy accessibility. I think integration with other environments is needed to enhance accessibility.

What is your primary use case for Data Hub?

My main use case for Data Hub is for data governance, specifically the use of data lineage and data catalog. I use Data Hub for data governance and data lineage in my day-to-day work by checking th...

What advice do you have for others considering Data Hub?

I do not have any advice to give to others looking into using Data Hub. I found this interview valuable and do not think anything needs to change for the future. My overall review rating for Data H...

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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Datadog vs Data Hub

Compared 10% of the time

SAP PowerDesigner vs Data Hub

Compared 6% of the time

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Avantra vs Dynatrace

Compared 3% of the time

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Product Reports

Download Data Hub product report

Data Hub

October 2026

Download Dynatrace product report

October 2026

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 is advancing observability for today’s digital businesses, helping to transform the complexity of modern digital ecosystems into powerful business assets. By leveraging AI-powered insights, Dynatrace enables organizations to analyze, automate, and innovate faster to drive their business forward. To learn more about how Dynatrace can help your business, visit www.dynatrace.com, visit our blog and follow us on LinkedIn and X @dynatrace.

Sample Customers

Information Not Available

TD Bank, Zurich North America, Accenture, Macquarie, United Airlines, Raymond James, BT, Vodafone, Air France-KLM, TELUS, American Airlines, Air Canada, ADT, Virgin Money