Apache Hadoop vs VMware Tanzu Data Solutions comparison

Apache and VMware are both solutions in the Data Warehouse category. Apache is ranked #6 with an average rating of 7.9, while VMware is ranked #7 with an average rating of 8.1. Apache holds a 5.0% mindshare in Data Warehouse, compared to VMware’s 5.4% mindshare. Additionally, 88% of Apache users are willing to recommend the solution, compared to 89% of VMware users who would recommend it.

Apache Hadoop

Read 37 Apache Hadoop reviews

2,045 Views
1,721 Comparison Views

88% willing to recommend

VMware Tanzu Data Solutions

Read 82 VMware Tanzu Data Solutions reviews

1,659 Views
1,360 Comparison Views

89% willing to recommend

Apache Hadoop

VMware Tanzu Data Solutions

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Executive Summary

We performed a comparison between Apache Hadoop and VMware Tanzu Data Solutions based on real PeerSpot user reviews.

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

To learn more, read our detailed Apache Hadoop vs. VMware Tanzu Data Solutions Report (Updated: September 2024).

Buyer's Guide

Apache Hadoop vs. VMware Tanzu Data Solutions

September 2024

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Categories and Ranking

Apache Hadoop

Ranking in Data Warehouse

6th

Average Rating

7.8

Number of Reviews

Ranking in other categories

No ranking in other categories

VMware Tanzu Data Solutions

Ranking in Data Warehouse

7th

Average Rating

8.0

Number of Reviews

Ranking in other categories

Database Development and Management (7th), Relational Databases Tools (9th), Message Queue (MQ) Software (4th)

Mindshare comparison

As of September 2024, in the Data Warehouse category, the mindshare of Apache Hadoop is 5.0%, down from 6.6% compared to the previous year. The mindshare of VMware Tanzu Data Solutions is 5.4%, up from 4.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Warehouse

Featured Reviews

Akhilesh Chipre

Senior Assosiate Consultant at Applied Materials

Apr 11, 2024

Handles huge data volumes and create your own workflows and tables but you need to have deeper knowledge

We primarily use Kafka for intensive data streaming. For batch-based processing, we use Hadoop. Additionally, we have our own custom batch catalog that likely helps prepare data for further analysis or use. We have many projects where our main data storage is done in Hadoop only. All projects take data from Hadoop to provide data insights and reports. Hadoop YARN for resource management is a really good aspect. It is is very good for managing large data volumes. It allows us to monitor data processing effectively. We can see how much data there is, the consumption of RAM or ROM, and how resources are allocated. It's good for managing and previewing the scale of data processing.

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Nasir Niamat

Principal Software Engineer - Db Ops at i2c Inc.

Jun 30, 2023

An open-source solution with a good loading speed, but maintenance is time-consuming

We are using the product for analytical purposes like reporting and billing We maintain the servers on our premises. Compared to Snowflake, Greenplum is a cheap solution for analytical purposes. The latest version is better than the older ones. The solution updates very fast. The loading speed…

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Quotes from Members

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

Pros

"The best thing about this solution is that it is very powerful and very cheap."

"Hadoop File System is compatible with almost all the query engines."

"What comes with the standard setup is what we mostly use, but Ambari is the most important."

"The platform's quick data processing capabilities have been instrumental in supporting our AI-driven projects."

"Since both Apache Hadoop and Amazon EC2 are elastic in nature, we can scale and expand on demand for a specific PoC, and scale down when it's done."

"One valuable feature is that we can download data."

"Two valuable features are its scalability and parallel processing. There are jobs that cannot be done unless you have massively parallel processing."

"It's good for storing historical data and handling analytics on a huge amount of data."

More Apache Hadoop pros

"Some of the most valuable features are publish and subscribe, fanout, and queues."

"I like the high throughput of 20K messages/sec, and that it supports multiple protocols."

"RabbitMQ will help to remove a lot of the complexities and create a loosely coupled codebase."

"The most valuable feature is that it's really customizable."

"The message routing is the most valuable feature. It is effective and flexible."

"After creating a RabbitMQ service, they provide you with a sort of web management dashboard."

"The solution is stable."

"Helps us to achieve large-scale analytics."

Cons

"In certain cases, the configurations for dealing with data skewness do not make any sense."

"I think more of the solution needs to be focused around the panel processing and retrieval of data."

"The stability of the solution needs improvement."

"The upgrade path should be improved because it is not as easy as it should be."

"The solution is not easy to use. The solution should be easy to use and suitable for almost any case connected with the use of big data or working with large amounts of data."

"It could be more user-friendly."

"The solution needs a better tutorial. There are only documents available currently. There's a lot of YouTube videos available. However, in terms of learning, we didn't have great success trying to learn that way. There needs to be better self-paced learning."

"Real-time data processing is weak. This solution is very difficult to run and implement."

More Apache Hadoop cons

"We needed to configure additional plugins. While it was relatively easy to do this on-premises, it became more challenging in the cloud."

"If messages pile up until the space of the memory is full, then basically, the cluster goes down, and someone has to log in through the backend and purge all messages."

"It doesn't have any GUI-based monitoring tools."

"Extra filters would be helpful."

"Maintenance is time-consuming."

"The product has to improve the crisis management, especially in memory issues."

"When you have complex tasks, RabbitMQ is hard to use."

"We would like to see Greenplum maintain a closer relationship with and parity to features implemented in PostgreSQL."

Pricing and Cost Advice

"The price of Apache Hadoop could be less expensive."

"There are no licensing costs involved, hence money is saved on the software infrastructure."

"We don't directly pay for it. Our clients pay for it, and they usually don't complain about the price. So, it is probably acceptable."

"If my company can use the cloud version of Apache Hadoop, particularly the cloud storage feature, it would be easier and would cost less because an on-premises deployment has a higher cost during storage, for example, though I don't know exactly how much Apache Hadoop costs."

"The price could be better. Hortonworks no longer exists, and Cloudera killed the free version of Hadoop."

"It's reasonable, but there's room for improvement in cost-effectiveness."

"This is a low cost and powerful solution."

"For any big enterprise the costs can be handled, and it is suitable for big enterprises because the scale of data is large. For medium and small enterprises, the tool is on the high-price side."

More Apache Hadoop pricing and cost advice

"are using the open-source version, which can be used free of cost."

"It’s an open-source solution."

"The pricing is okay."

"The pricing for RabbitMQ is reasonable. It is worth the cost."

"The product is available for free use since it is an open-source technology."

"The price is pretty good."

"Pricing is good compared to other products. It's fine."

"On a scale of one to five, with five being the most competitive pricing, I would rate this solution as a four."

More VMware Tanzu Data Solutions pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

30%

Computer Software Company

12%

University

Manufacturing Company

Financial Services Firm

29%

Computer Software Company

16%

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Apache Hadoop?

It's primarily open source. You can handle huge data volumes and create your own views, workflows, and tables. I can also use it for real-time data streaming.

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What is your experience regarding pricing and costs for Apache Hadoop?

I would rate the product's subscription-based pricing a six out of ten. It's reasonable, but there's room for improvement in cost-effectiveness.

See all answers

What needs improvement with Apache Hadoop?

The product's availability of comprehensive training materials could be improved for faster onboarding and skill development among team members.

See all answers

How does IBM MQ compare with VMware RabbitMQ?

IBM MQ has a great reputation behind it, and this solution is very robust with great stability. It is easy to use, simple to configure and integrates well with our enterprise ecosystem and protocol...

See all answers

What do you like most about VMware Tanzu Greenplum?

The loading speed is very good.

See all answers

What is your experience regarding pricing and costs for VMware Tanzu Greenplum?

It’s an open-source solution. There are no expenses for using it.

See all answers

Comparisons

Azure Data Factory vs Apache Hadoop

Compared 20% of the time

Oracle Exadata vs Apache Hadoop

Compared 15% of the time

Microsoft Azure Synapse Analytics vs Apache Hadoop

Compared 14% of the time

BigQuery vs Apache Hadoop

Compared 10% of the time

SAP IQ vs Apache Hadoop

Compared 3% of the time

More Apache Hadoop Competitors

IBM MQ vs VMware Tanzu Data Solutions

Compared 14% of the time

MuleSoft Anypoint Platform vs VMware Tanzu Data Solutions

Compared 10% of the time

Red Hat AMQ vs VMware Tanzu Data Solutions

Compared 9% of the time

Apache Kafka vs VMware Tanzu Data Solutions

Compared 9% of the time

Oracle Exadata vs VMware Tanzu Data Solutions

Compared 8% of the time

More VMware Tanzu Data Solutions Competitors

Product Reports

Buyer's Guide

Apache Hadoop

September 2024

Download Apache Hadoop product report

Buyer's Guide

VMware Tanzu Data Solutions

August 2024

Download VMware Tanzu Data Solutions product report

Also Known As

No data available

Greenplum, Pivotal Greenplum, VMware RabbitMQ, VMware Tanzu GemFire, VMware Postgres

Learn More

Apache

VMware

Overview

The Apache Hadoop project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

VMware Tanzu is a robust platform tailored for data warehousing, complex analytics, BI applications, and predictive analytics. It excels in scalability, performance, and parallel processing, enhancing data handling efficiency. Users report significant productivity improvements and streamlined operations, making it ideal for comprehensive data solutions.

Sample Customers

Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web Lab

General Electric, Conversant, China CITIC Bank, Aridhia, Purdue University

Buyer's Guide

Apache Hadoop vs. VMware Tanzu Data Solutions

September 2024

Free Report: Apache Hadoop vs. VMware Tanzu Data Solutions

Find out what your peers are saying about Apache Hadoop vs. VMware Tanzu Data Solutions and other solutions. Updated: September 2024.

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We monitor all Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.