VMware Tanzu Data Solutions Reviews

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.

It is a very good appliance for data warehouse (DWH) usage.

• Very fast for query processing
• Parallel load
• Polymorphic storage

Before we had Oracle Exadata, some queries would take more than 20 hours of execution. With Greenplum, it take a few minutes.

It will be very useful if we could communicate with other database types from Greenplum (using a database link).

Four years.

No issues.

No issues.

Good. I would give their technical support a seven out of 10.

Yes, Oracle Exadata. Performance was the main criteria for switching to Greenplum.

It was a simple setup.

It is the best product with best fit for price/performance customer objectives.

We evaluated Oracle technology that we used before.

I encourage other customer to try Greenplum, specifically for DWH use. It is a very useful product.

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.

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.

Our primary use case is for telecom customers.

In terms of most valuable features, I'd say Pivotal Greenplum's shared-nothing architecture.

I'd like to see a bridge between Greenplum and Hadoop.

We didn't have any issues yet with stability.

In terms of scalability, I just have one master and two segment hosts.

Initial setup is a little complex. It took around two weeks to deploy.

I prefer an open-source solution but Pivotal Greenplum is cheap and has many functionalities.

On a scale of 1 -10 I'd rate it a 6.

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.

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 MPP – fast aggregation and that I still reuse the PL/SQL code.

The reports are running very fast, a matter of minutes instead of hours as it was previously.

It should support more feature that do exist on Postgres – like JSON datatype.

I've used it for three years.

Not more than any new DB – not something particular.

Not more than any new DB – not something particular.

Not more than any new DB – not something particular.

It's good.

It's good.

I used Oracle Enterprise edition – moving to Greenplum due to the MPP (it’s like Map-Reduce). I prefer a SQL solution rather Hadoop or whatever – especially if you’re moving from RDBMS. Now I’m also using Amazon Redshift from the same reason and also we don’t need to manage any server, it’s only a service and this is our best option especially because we’re already using Amazon.

It's straightforward.

In-house.

Read the documentation and always perform tests.