VMware Tanzu Data Solutions Reviews

We were using it as a high-volume text messaging solution. 

It's fully open source, has a high throughput, and the serviceability is good.

This solution struggled with multi-regional synchronization.

The stability of this solution was very good. 

The scalability was good. It's very comparable to the other databases on the market. We were doing 5,000 transactions a second, and probably one third of those were unique users at any one time.

The tech support was fine. We rarely needed to use it. 

The setup was pretty straightforward, just standard database install stuff.

For maintenance, we had a single DBA, and a couple of people on our software development team served as their backups.

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

We were using SQL Server in some circumstances, and the two solutions are very comparable, other than one being open source and one not. We've also used Cassandra, Bitquery, and a whole bunch of others. Each solution has unique advantages and disadvantages. From a feature standpoint, this solution is relatively close to parity in the environment. It stands out because it's opensource and cost effective, and it does everything pretty well.

For those looking into implementing this solution, my advice would be to think about how you want to handle multi-regional replication.

I would rate this solution as an eight out of ten. 

We specifically use the solution for queuing purposes, and it has proven to be fantastic in that aspect.

The solution's best feature is its exceptional speed, delivering efficient utilization of resources. It uses a memory desk processor very efficiently. It offers high performance while maintaining a low cost.

The solution is a fine product. However, to make it perfect, in some cases, there might be a need to traverse the queue. RabbitMQ currently lacks the capability for archiving the queue, which essentially turns it into a log.

For such requirements, you may need to explore other options like Kafka or custom drivers that allow traversing the entire queue. In RabbitMQ, while you can traverse the entire queue, you need to devise a workaround to handle the messages. For example, you can read a message from one queue, publish it to another queue or keep it in some other way to retain the desired entries, and then stop at that point.

Additionally, the need for support may vary depending on the usage and potential heavy loads on the system. The support feature could benefit from some improvement in terms of accessibility and responsiveness.

I don't encounter significant challenges or areas that require improvement while using the solution. Everything works smoothly, and I find it well thought out. It's got excellent compliance with MQP 9.0. Overall, I have had a positive experience with the solution.

I have been using the solution since 2017.

The solution is highly stable. As an example, at this moment, I am in front of my admin panel and can confirm that it has been running continuously for the past 173 days.

The solution is scalable, although I still need to utilize the clustering option. A single server is sufficient and efficiently handles most of our workloads. It effectively uses system resources such as memory, CPU, and disks, resulting in excellent performance with minimal resource usage.

So far, we have not needed any support from the solution's official support team or community. We rely on Google search and our team's research, leveraging various online resources to explore and implement solutions independently.

When I joined my current company, I initially explored Apache Kafka, but I realized that Kafka is primarily a log system rather than a queuing system. I encountered limitations with Kafka, such as maintaining pointers for each process and manually removing messages from the queue.

Comparatively, RabbitMQ proved to be more convenient as it automatically deletes messages from the queue when using auto or manual acknowledgment. Considering these factors, we switched from Kafka to this solution due to its efficiency.

The solution's installation process was straightforward, especially if you have good skills in installing software and a good command of Linux. Once the Bandit software is downloaded and extracted, the installation is completed.

After that, accessing the admin interface allows for a user-friendly GUI experience. The deployment process took around half an hour. 

We have a private cloud infrastructure using VMware, which means our servers are running on-premises and are owned by our company. We have a limited number of servers running the solution.

Specifically, we have one primary server and one secondary server without implementing clustering. Replicating these two servers is sufficient for our workload, and they can be installed by a single system administrator in just half an hour without any issues, provided they have DPU-installed Linux available.

Overall, I would rate the setup experience as nine out of ten.

The solution's pricing is cost-effective as it does not involve significant expenses. Licensing is required only for the server, while clients do not need any licensing. Therefore, it proves to be a cost-efficient option.

In my previous organization, we heavily relied on Tibco messaging solutions like Tibco RD (Rendezvous) and Tibco RV (Rendezvous) for the entire rating system. I have also explored Apache Kafka.

If you are looking for a queuing system for your application that guarantees insured delivery and ensures single delivery without duplicates, RabbitMQ is the right solution as it provides all these capabilities with ease of use.

With RabbitMQ, your application doesn't need to worry about receiving duplicate messages as the solution handles that internally, ensuring that each message goes through a single process for one delivery.

I highly recommend the solution and would rate it an eight out of ten.

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 is very good.

Maintenance is time-consuming. It takes time to VACUUM and ANALYZE the tables to remove the fragmentations.

I have been using the solution for five years.

The solution is stable.

Compared to Snowflake, Greenplum is not scalable. The solutions used on premises are not scalable compared to the cloud solutions. Around 200 to 300 people use the product in our organization.

Support is fine. We do not use high-level support. The support team is quite supportive.

The setup is easy. It is not complex.

We must set up the instance and run scripts to deploy the product. It is very simple. We can deploy the scripts with one or two commands. One person is enough to deploy the solution.

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

We are using the latest version of the solution. Some of our clients asked us why we were not using Snowflake, so we are evaluating Snowflake as per their request. If we replace Greenplum with Snowflake, the purpose would be to minimize maintenance time and enhance scalability. If someone is looking for a cheap solution, Greenplum is a good choice for them. Overall, I rate the product a six out of ten.

We use the solution for the asynchronous call and POPS mechanism.

The most valuable feature is asynchronous calls, which are easy to configure.

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

I have been using VMware RabbitMQ for one year.

The product is stable. We haven't faced any issues.

VMware RabbitMQ needed to be a more scalable product. It wouldn't perform consistently if you wanted to add workload or users or reduce workload. We faced problems with it during heavy loads. The cloud version is scalable. We can scale it up or down based on our requirements, such as the number of users or workload.

Around seven or eight people were in every group, and many teams were using it for virtual use.

The infrastructure was handled by a person responsible for configuration and related tasks. I primarily focused on configuring connections as a developer. I could handle it by installing components like plugins. When issues arose in the cloud environment, we escalated them to the support channels.

We installed VMware RabbitMQ on a local computer. We are currently using Docker and Kubernetes for deployment in our local environment. It was relatively easy to deploy compared to an on-premise system.

The solution can be a bit challenging to handle. Not every configuration and deployment works seamlessly. It depends on the project team and compatibility, but is relatively easy to use.

The product is free of cost.

We configure the monitoring and alerting for the RabbitMQ setup. We transfer the message to the designated queue if there are any errors or similar issues. We use a Spring Boot application and microservices for this purpose, making it easy to route the message.

If you want to use this solution, you first need to understand the concept of exchange queues. Certain clusters require specific knowledge. The configuration may vary depending on the application type. For instance, the configuration was relatively straightforward in our case with microservices. We only needed to provide authentication and the correct URL. If it ran on a cloud environment, we would provide the instance, username, and password, and the configuration would be handled automatically. It would depend on the language and the specific type of microservice or application for more advanced customization, such as writing code.

The solution is easy to use, configure, and install.

Overall, I rate the solution an eight or nine out of ten.

My team uses Greenplum for automating a lot of job writing scripts using PSQL. We use it to automate reports. Also, I use the identity workbench for logging in. In addition, I use DBeaver as well.

It works very well with large database queries. It works efficiently even in cases where you have a query worth terabytes of data.

Its most valuable feature is the ability to quickly run through data. It has a good processing speed as well.

They should improve product performance. It does not share resources well whenever it has more than one process running. If you have a user consuming a huge load size of resources, it takes down the entire system.

They should work on resource pulling and sharing those resources properly. At present, it allows one user to completely take over everything.

We have been using the solution for eight or nine years.

I would rate the solution about seven or eight out of ten for its scalability. Around 30-40 people in my organization are using the solution. We are not looking to increase usage. 

You eventually have to get new hardware because the CPU stops quickly once the load increases. It's only limited to hardware. You don’t realize you need new hardware until you have to because it doesn’t have throttles.

We have used Snowflake, Oracle, and many such solutions.

The setup seems pretty straightforward.

The solution has its limitations. It works better with a serial-threaded program than a multi-threaded program. If you go for the solution knowing that, you should be fine. I would rate the solution as a six out of ten.

We can use the solution to move large amounts of data. Most of the functions have to be done manually.

The features of the solution are similar to Anypoint MQ. The transaction of data is pretty fast. Large amounts of data can be moved pretty fast using the solution.

The product is pretty hard to configure.

I have been using the solution for almost a month.

I rate the stability a seven and a half out of ten.

The tool's scalability is similar to that of Anypoint MQ.

The initial setup is complex. The product is deployed on the cloud. The configuration and setup are pretty complicated.

I use my own server and deploy the code with Jenkins.

It is an open-source product. We have to pay for additional features.

The configuration and setup of Anypoint MQ are easier.

People wanting to use the solution must learn how to configure and deploy it. If the documents are pretty clear, they will be easy to use. Overall, I rate the solution an eight out of ten.

The product's feature of data transaction works fast.

VMware RabbitMQ's configuration process could be easier to understand.

We have been using VMware RabbitMQ for a month.

I rate the platform's stability a seven and a half out of ten.

The initial setup is complex. It is difficult to find any documentation explaining the process. I deploy the code on the server using Jenkins.

It is an open-source platform. Although, we have to pay for additional features.

I rate VMware RabbitMQ an eight out of ten. You should know how to configure and deploy it on the servers. If they provide good documentation, it will be easier to use.

We are using this product as a database for our platform. We are customers of VMware and I'm the project architect. 

In general, I think this is a very good platform, particularly as it's open source. It fits very well with use cases for public institutions or universities, where there is not always a big budget.

I'd like to see more support for structured data and features related to queries on NoSQL keys, extra filters would be helpful. 

I've been using this solution for two years. 

We've been working on this project and using this solution for two years and it's performed very well. 

The solution is scalable. We have around 20 users working directly on this platform and around 1,000 end users. 

We also use Cloudera and Oracle so we have a few alternatives. We currently use Greenplum because one of our customers needed an open-source solution that would scale well and after some investigation, we went with Greenplum. 

The initial setup was straightforward and we carried out the implementation ourselves. 

We're using the open-source version so there are no licensing fees. 

I recommend reading as much documentation as possible before starting to use the product. It helps to know how your data model should be implemented in order to get the best out of the platform and to figure out how it can improve performance, 

This is one of the best solutions I've used and I rate it eight out of 10.