If you have a data warehouse use case for complex analytics on terabytes of data, without the need for real-time data, and you can wait an hour or two for data coming from source, this is a very affordable product that provides reliable service in a consistent way. The product requires minimal maintenance and support, without any need for supervision. Once you design your system, it's very easy to onboard all your queries quickly. 

Although this is a cheaper solution, there are issues with stability and scalability which is key in today's world, as is cloud compatibility, so I therefore rate this solution seven out of 10. 

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 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 are currently in the process of upgrading from version 5.26 to 6.11 and I can already see a lot of improvements. I can't wait to try them. According to the roadmap, there are a lot of new improvements coming in the V7 version, which is due out next year.

My advice for anybody who is implementing Greenplum is that they really need an expert to assist them. They might hire consultants or grow experts in-house, although that takes time and it is not always straightforward. You can use Greenplum out of the box but to really leverage all of the capabilities, you definitely need to tune your system and also design your database objects.

When people think about a database they usually think about Oracle, Mircosoft SQL, or maybe MySQL. Greenplum is a distributed database that needs a completely different mindset. I think that when people start to use it, they don't really understand. For example, you cannot switch from Oracle to Hadoop because you will need the same change, but when they switch to Greenplum from Oracle, or just put data from Oracle to Greenplum, they don't consider this change as seriously as they would for Hadoop.

Overall, I am very happy with this product.

I would rate this solution a nine out of ten.

When considering implementing Tanzu Greenplum, I recommend viewing it as a massive lakehouse, not just a data warehouse. I would rate Tanzu Greenplum ten out of ten.

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.

If you want to implement this product, you would need to scale your product well before trying to implement.

Ensure that this is the right tool for your needs. For instance, Greenplum is not the best tool for cases where data has to be kept up to date in real time. Capacity planning is key to success, once you do decide it is the right tool for you.

You need strong DBAs and architects to support the initial transfer.

My primary reason for reducing points on this rating is due to the fact that Greenplum is based on a fork of PostgreSQL v8.2.15 (EOL by the PostgreSQL project on Dec 2011). The current version of PostgreSQL is v9.5. There are a number of current PostgreSQL features of which we would like to take advantage (JSON support, materialized views, full text search, XML support, column-based permissions, row-based permissions, etc.).

I would recommend this solution specifically for the scalability. This solution has a more futuristic technology, as opposed to the old school kind of data warehousing. If people are interested in getting something that is more future-proof, then I would recommend this solution.

So far, we're comfortable with what we've seen. What we have configured is addressing our needs at the moment.

I would rate this solution an eight out of ten.

Greenplum is a MPP (massively parallel processing) database which is extremely fast. If people are dealing with very high volume of data, it is definitely a product to consider seriously

For the support network of the product, overall installation, use of the database, and improved database and support, I rate the solution an eight out of ten.

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.

If you can, do a benchmark with other MPP options including cloud alternatives. Although our Greenplum implementation was very successful (going on 4 years ago), I wish we had benchmarked against Teradata and Netezza (now IBM) at least. Today, I would consider not even buying hardware… just doing it all in the cloud.

Make sure you have the designs, approaches and architecture in place before kicking of the implementation. Its best to have someone involved with prior migration experience.

Installation is very simple, make sure to set the configuration values based on the requirement.

Investigate whether this solution works for you. It is worth creating a rating matrix to compare other similar products, and it is very useful to look deeply at whether the third-generation MPP software might be a good fit.

Very good and cost effective with very good customer support.

It’s a good product. We moved from Oracle and I don’t want to go back.

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

I would rate this solution a seven out of ten.

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

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.

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.

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.

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

Read the documentation and always perform tests.

It's a very good product for reducing the time and man power needed for database optimization for datawarehousing purposes.

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.