VMware Tanzu Data Solutions Reviews

We use it for data warehousing.

For complex queries, which would normally take a long time, and for reporting, it is very efficient. It doesn't take a long time for the execution of any report for the end-user.

• It's one of the fastest databases in the market.
• It's easy to use.
• From a maintenance perspective it's a good product.
• The segmentation, or architecture of the product is different than other databases such as Oracle. So even in 10 years, the data distribution for such segments will not affect other segments.
• The query performance of the product, for complex queries, is very good.
• It has good integration with Hadoop and Big Data.

The implementation of an upgrade takes a long time. But maybe it's different from one instance to another, I'm not sure.

Also, one of the disadvantages, not a disadvantage with the product itself, but overall, is the expertise in the marketplace. It's not easy to find a Greenplum administrator in the market, compared to other products such as Oracle. We used to work with such products, but for Greenplum, it's not easy to find resources with the knowledge of administration of the database.

If we face any issues they're normal and we open tickets.

It's scalable. I would rate scalability seven out of 10.

We hired one DB admin for Greenplum. If he faces any issues he opens tickets with the vendor, but most of the issues, 90% of them, he is able to solve without support.

We used to other products before, but when we worked with Greenplum, as compared to other products on the market, we found it's a good product.

Before Greenplum, we used Oracle but it was mostly obsolete. So we had to upgrade our tools. We needed to have a database with an API tool.

I'm not a professional in the setup but setup of the environment itself was managed by us. We managed between development, testing, and production servers. We are able to maintain it. I don't think it is complicated.

Most of the issues can be solved without referring back to support. A very small minority of issues required support from the vendor.

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

We did a comparison among some databases, one of them Greenplum. We assessed features, did a comparison in terms of the price, then we chose Greenplum. And we've retained it. We've found it's a good product, to date.

Oracle Exadata was part of the comparison, as was IBM Netazza. In terms of quality and the price, compared to the other products, we chose Greenplum. Also, to be honest, at that time we got a good offer: Use it for the first year with a minimal price. Then they opened a support contract with us, later. That was one of the advantages.

I give it an eight out of 10. To bring it up to a 10, they need to interact more with customers. They need to explain the features, especially when there are new releases of Greenplum. I know just from information I've found that it has other features, it can be used to for analytics, for integration with Big Data, Hadoop. They need to focus on this part with the customer. 

Also they need to enhance integration with other Big Data products. They need to adapt more, give more features, because customers are looking for these things in the market now. They have the product itself already, but they need to integrate with Big Data platforms and to open a bi-directional connection between Greenplum and Big Data. They need to focus on these features more.

But, from my perspective, for what I'm looking for, I can say it's a good product. Most of the features I'm looking for are available.

I've found that the data compression and ETL are the most valuable features for us.

In 4.3.8.1 Pivotal confirmed that even restoring schema level backup is possible from a DB backup.

- restoring schema from a DB level backup has been tested and working fine .

ORCA - the Pivotal Optimizer does a good query plan but does not works with all business logics. This needs to be tested based on your requirement.

Loading batch data has really improved the efficiency of our organization.

Running Extracts has drastically improved the timings. Being MPP which is a bulk operator - we were able to do 1.5 million calculation in 15 minutes.

Scaling of the solution needs to be improved.

HD connection is available where as, not to any file system.

Connecting Greenplum with Gemfire(In-Memory) to load, sync, and reconcile data would be really valuable.

I've used it for nearly for 3 years

We had deployment issues after installing new patches. Every new patches has some or other business hit where the release notes needs to be reviewed.

It's been stable for us.

They have a quick turn around but to dig into the actual information takes time, based on the Severity.

First level of technical support would not be that effective (based on own observation).

We were using Sybase and handling massive data, bulk operation was not possible.

It was simple.

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.

We use RabbitMQ for tasks that need to communicate in real-time. I have used it as a microservices message broker.

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

When you have simple tasks, it is easy to use.

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

There are several things that you have to do manually, so there should be better tools for that.

I began working with RabbitMQ several years ago.

I have had no issues with stability. It has been perfect.

Scalability is not a problem.

Because we are using the open-source version, we do not use technical support and have been solving problems on our own.

The complexity of the setup depends on the scope of the implementation. For a single node, the initial setup is very easy. On the other hand, for setting up RabbitMQ with a cluster, you really have to know what you're doing.

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

My advice for anybody who is implementing this product is to learn the concept of message queuing and study the AMQP 0-9-1 protocol. If you study this then you won't have any problem learning the system and working with it.

In summary, this is a good product that I will continue to use, and I recommend it.

I would rate this solution a seven out of ten.

We use VMware RabbitMQ to transfer information from one point to another. 

VMware RabbitMQ needs to create a new queue system. 

I have been using the solution for one and a year. 

My company has 10-15 users for VMware RabbitMQ. 

VMware RabbitMQ's installation is easy. However, it doesn't come with any manuals. 

You should use the solution if you want to store your details in the queue and start taking inputs from the queue. I rate it a nine 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 use the solution on our SaaS platform to speedup and simplify customer access across services.

Some of the most valuable features are “publish and subscribe”, fanout queueing, and scalability.

We have a number of different use cases in our scenario. A key one is “publish and subscribe”. We have spent the last year breaking up a large monolithic application into microservices and each microservice has to subscribe to different events for the purpose of CQRS and other kinds of updates. RabbitMQ is perfect for “publish and subscribe”. It does an awesome job at fanout, perfect for CQRS, messages are delivered to all subscribers with almost no additional latency.

RabbitMQ provides the ability to scale queues in a very simple and elegant way. If it had a “failure queue” with robust delivery and recovery built-in with the same power, that would be great. We use a completely different queuing system for failures. So there is a little more effort to take messages in a failure queue, analyze them, figure out what went wrong and then restart them in Rabbit. It is doable, and we do it, but if we had a round trip solution in Rabbit, that would be awesome.

For me, having a robust failure queue, is high on the list of improvements needed in the near future. This is an important update needed because right now we are using Doctrine for our failure queue. Doctrine does a great job.

I have been using the solution in the past year.

Rabbit is a very scalable solution. We could easily queue 50,000 messages in less than a minute. The first day we introduced Rabbit to replace another queueing system that we were using, there was disbelief on the part of the product team because the response was so fast. We need tens of thousands of messages queued in a short period of time, approximately one minute. For example, one user action could spawn 65,000 messages. We also need the ability to segregate different queues. This solution did a great job.

The installation is very simple and elegant, and we love the graphics. It lets us see exactly what is happening with the ability to start the queue, stop the queue, consume messages on the queue. This is a huge help.

We design, develop and deploy the solution ourselves.

We are also evaluating Apache Kafka. Our process is very disciplined. We look at the analytics, the abstraction, the architecture relative to our technical architecture, we ask ourselves questions about the use case, which is better for use A or B. Kafka is not as simple for “publish and subscribe”. You can do it, but not the best fit for us. However as a queueing system, Kafka is great. The records are stored on the queue in the order they are received, However, you can easily search by topic no matter how large the list. Important if you keep track of everything.

There are many different use cases for each technology, as well as many approaches. So have the architecture team graph and document every solution. Have a few training days to clarify the goal, the solution and the implementation. One of the things we do in our training is to actually create prototypes, the abstract model of our ideal state. This demonstrates exactly what we all need to do. Developers understand more quickly with a model. It flattens their learning curve and they are more productive more quickly.

I rate VMware RabbitMQ a ten out of ten.