VMware Tanzu Data Solutions Reviews

Some valuable features of this product are:

• Message queuing
• Good support
• Provided scalability and a distributed environment
• Easy usability with NodeJS
• Could easily withstand and pass stress/load testing with more than 10K API calls
• Helped achieve a distributed environment and implement a 3-tier structure
• Helped to develop a highly scalable system by decoupling front end and back end

We had a project where we had huge responses to APIs from the front-end and had to handle such large responses/requests without losing any of them. RabbitMQ efficiently handled this problem by providing message queuing and decoupling our front end and back end.

I would love to see better documentation/demo for few technologies. There is need for better stability in the Windows environment.

I have used this product for around six months.

We encountered a few problems with Windows while clustering and hence we used Linux.

We did not encounter any scalability issues.

I would give the technical support a 7/10 rating.

It involved more of research as how to use RabbitMQ. For clustering, it was little bit complex but I was able to follow the documentation provided.

We did not evaluate any other solution prior to this one.

It gives product support with your technology.

The primary use of this solution is for horizontal scalability.

We have an on-premises deployment.

The most valuable feature for us is horizontal scaling.

The deployment process for this solution could be made easier.

I saw some limitation with respect to the column store, and removing this would be an improvement.

This solution is stable enough. 

We are not yet sure about scalability, but we will see in the future.

We have not been in touch with technical support for this solution.

We were using MySQL prior to this solution.

The initial setup of this solution was not simple, but it was not too complex, either. I would say that it was of medium complexity.

Our deployment took approximately two days, although this was only a partial deployment. We have yet to add another node.

We performed the deployment in-house.

We are using the open-source version of this solution.

Prior to choosing this solution, we evaluated MySQL and PostgreSQL.

I would rate this solution a seven out of ten.

The most valuable features are MPP and that it's polymorphic.

We have a set of workflow flows that takes 10 hours in Oracle Exadata, now it takes 4 hours with EMC Greenplum.

It doesn't work as efficiently as we'd like because it requires more segment node capacity (size, RAM, CPU) than we currently have.

I've used it since 2013.

At the moment, we don't have any issues with deployment.

At the moment, we don't have any issues with stability.

At the moment, we don't have any issues with scalability.

8/10

8/10

Yes, we have Oracle Exadata.

The initial setup is straightforward. It was simple for our personnel to install on the Unix system and network.

It's a good product when comparing the price to its functionalities.

You should consider the capacity of your data before you buy it.

The most valuable features for us are speed and persistent messaging.

We are now able to leverage real time applications and event driven architecture.

The documentation needs to be improved. There's a learning curve on setting it up and there are issues arising from slower networks that they lack documentation on.

We have been using the product for three years.

Stability issues experienced were only due to a slow network at the client.

We did not encounter any scalability issues.

I never used the technical support.

I am not aware of any previous solutions.

We are a container shop and the only issues during setup were around proper clustering and configuring the application to use it.

We evaluated Apache Kafka also.

I would advise potential customers to use something to wrap their interaction, like Spring for Java.

The Greenplum loaders – their methods, insert, update and merge – were most valuable. The external table feature within Greenplum was also very valuable in achieving performance and scalability

A lot of customers use Greenplum and for their ETL use cases they use Informatica PWX for Greenplum, which helps them achieve transfer of data from a number of data sources into Greenplum. We can help customers to load data effortlessly and quickly.

It would be best if Greenplum would support array writes through ODBC drivers . Currently through Data Direct Greenplum drivers we can have single row inserts into Greenplum only. It would help if array loading is supported.

I have been using Greenplum loaders to load data into GreenPlum for three years.

There were no issues in deploying it.

The external table feature helps to make this a stable solution.

It scales to our and our customers needs as required.

The ongoing response from their support on certain technical issues has been slow. It would help if we can have a faster turn-around here.

There was no other solution used previously.

The installation and configuration is straightforward.

We implemented through a in-house team . The overall solution will be useful and is not complex

If the use case is going to be requiring a huge data transfer and big data analytics. This is a good product to use.

It's super easy to deploy and it also supports different languages and analytics.

As of now, I honestly don't see any room for improvement. I am very happy with this solution, It's helped me a lot.

They should add more analytics. Their documentation could also be improved so that I don't have to bother my coworkers and tech support so often. 

They should advertise more and make it easier for consumers to leave feedback. That way, customers could read the reviews and decide if it's the right solution for them and their needs.

I have been using VMware Tanzu Greenplum for roughly one year.

VMware Tanzu Greenplum is both scalable and stable.

Their technical support is great.

The initial setup is very easy. Deployment took us roughly one or two days.

I would definitely recommend VMware Tanzu Greenplum. It's open-source and the support is great. It's also relatively cheap compared to similar solutions.

Overall, on a scale from one to ten, I would give this solution a rating of ten — I really like it.

MPP (Massive Parallel Processing); processing large amount of data.

We process billions of rows of data every hour; MPP architecture is important to process data in such volume.

• Better integration with big data tech stack
• Scalability: for example, system schema (pg_catalog) is one bottleneck for scalability

I've used it for four to five years.

The system has some bottleneck, like system schema; some commands (batch loading) is bottlenecked by master DB architecture

It's very poor.

It's very poor.

Used Greenplum originally. We have evaluated other products; the final decision is based on ROI

Products was installed by vendor; all ETL scripts and schema are designed and implemented in-house.

The product was installed by vendor; all ETL scripts and schema are designed and implemented in-house.

Greenplum has an open source version now.

Scalability is one major concern; once data reach certain level; the performance is dropped and much more issues will be triggered, like disk error; out of memory; etc. Be sure that you proper scope your need (including growth) before the decision of system and its size.

It's a good core database. Scalability and performance are very good. I also like the fact the solution is open-source, so you can use it free of charge.

Some integration with other platforms like design tools, and ETL development tools, that will enable some advanced functionality (like fully down processing, etc.) would be helpful in future releases. Also, if the solution could offer automated creation of DDL statements from power designers, for example, it would be very useful. 

It's a software, and like any software, it has some bugs. However, you can add new features to improve it. Overall, our customers, who are big telcos, have been very satisfied with the platform and with its stability and performance

Scalability is simple because it's an MPP database. If you need more processing power or you need more storage, you just add a few more nodes in the cluster. It works on common commodity hardware. You can use any type of server. You don't need to have proprietary hardware. It's fairly flexible.

The solution requires a minimum amount of downtime when scaling. You can even add additional nodes without any downtime at all. I'm not 100% sure, but I think you can just reconfigure it and the background processes, and Greenplum will do the redistribution of the data.

Technical support is very good. The model they are using to fund the development of their open-source product is via revenue from support for enterprise customers, so they are very attentive when issues arise.

The solution is very straightforward to set up and is also easy to administer and develop using other open-source tools.

It's open-source, so it's free to use.

I'm a partner that works mainly with enterprises. Mostly the partners are big telcos and we deal with tens of terabytes of data.

MPP and columnar databases are the future of the analytical landscape. The era of appliances is over, so implementation of an MPP database on-premises or on the cloud is the way to go. Greenplum is definitely one of the leaders in this area.

I would rate the solution eight out of ten. If they improved the integration with other platforms in the landscape I would rate it higher.