VMware Tanzu Greenplum Reviews

Strong integration with Greenplum Servers.

Loading data to Greenplum server after batch processing.

6 months

Yes, issues with HA and issues in syncing with the Greenplum.

You cannot expect a split second response.

On a scale of 10 I would rate it as 7

On a scale of 10 I would rate it as 8

This has just been used for POC period not for a regular use.

Initial setup works absolutely well. We were using with only Greenplum.

Processing speed of queries used for ‘Reporting’ solutions is the most valuable feature.

Not Applicable for the area I was responsible for, as we ended up migrating away from Greenplum.

Stability and scalability for large number of concurrent applications & their users. The results we got were very inconsistent, depending on number of connections taken up by multiple applications and users.

When our application was first deployed using Greenplum, the number of users of the rrack on which Greenplum was deployed was very limited. We got excellent query performance results at that time. But as more applications started getting deployed, we started getting very inconsistent performance results. Sometimes the queries would run in sub-seconds, and sometimes same queries would run 10 times longer. The reason we found this was that Greenplum limits the number of active concurrent connections. Once all connections are being used, any new query gets queued, and thus response time suffers.

The impression we got was that the EMC Sales team that sold Greenplum to the organization did a great job. But later on the ball was dropped when it came to educating on which type of applications are suitable to Greenplum , and how to configure it to get optimal performance. When Pivotal took over support of Greenplum, their consultant visited us to go over the issues we were having. He advised us that Greenplum is not the best environment for our application needs. We ended up migrating our application out of Greenplum, along with a few other applications.

There was no issue with the deployment.

There were issues with the stability.

There were issues with the scalability.

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.

• Parallel processing
• Takes minutes to process millions of records

This has improved our daily load process reducing the run time at least by three to four hours which made other departments within the organization to look for data from the Enterprise Data Warehouse.

It acts like a mainstream product not a novice any more. There are a lot of areas that can be improved. The bug fixes come as many patches like a start up instead of having scheduled release with proper improvements.

I've used it for five years.

It took a while to fully take advantage of this as we had to come up with lots proprietary solutions when linking to other products.

There is no issue with its stability.

There is no issue scaling it.

8/10 we had a technician come out on a new year in 2012 to fix some hardware failure.

We were one of pioneers in adopting this solution. So we got the best deal when compared to the competitors in several areas.

The initial move was a bulk transfer from the old system to new Greenplum based solutions. It was all done by Greenplum contractors. But to get it working with other products was challenging.

Greenplum employees developed and supported the initial move. Later they became remote consultants with support through phone and in-person as needed.

They gave a really good deal and we have been with them for five years even though the product got bought over by couple of different companies.

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

Of particular value to our environment and applications are the following Greenplum capabilities:

1. Scalable (Massive) Parallel Processing (MPP) – The ability to bring to bear large amounts of compute against large data sets with Greenplum and the EMC DCA has proven itself to be very effective.
2. Fast load of data into Greenplum – We experience performance of approximately 1TB per hour loading data to Greenplum without the use of specialized hardware.
3. MADlib (madlib.net) – There are a number of statistical and analytical functions available within MADlib upon which we rely. Among these are linear regression, logistic regression, apriori, k-means, principle component analysis, etc.
4. User Defined Functions in Python (UDFs in PL/Python) – Where MADlib does not provide a direct solution to an application problem, the ability to quickly prototype and deploy user defined functions with Python has been effective.

We would like to see Greenplum maintain a closer relationship with and parity to features implemented in PostgreSQL. The current version of Greenplum is based on a fork of PostgreSQL v8.2.15. This edition of PostgreSQL was EOL by the PostgreSQL project on Dec 2011. The current version of PostgreSQL is v9.5.

We began production use in November, 2011. Alongside Greenplum, we're also using EMC Data Computing Appliance v2.3.3 (8/10), of which we have two and a half racks in production, and one and a quarter racks in dev/tests.

We had no issues with the deployment.

The only issues with stability we’ve experience have been the sporadic fail over of primary to mirror segments. The environment continues to operate in this instance with the failure of queries that were in flight at the time of the fail-over.

We have had no issues with scalability whatsoever.

The service and support we’ve received from both Pivotal and EMC has been exemplary. The exceptions to this would be:

1. The EMC Request for Product Qualification (RPQ) process – EMC DCA support is contingent upon EMC approval of all third party software installed onto a DCA. There have been times that this approval has taken as long as 60 days to process.
2. Root Cause Analysis of Greenplum Database Incidents – When Greenplum Database incidents have occurred (e.g. primary database segments failing over to their backup), and Pivotal has been called for support, the response has been near immediate (30 minutes or less). Additionally, the incident resolution provided has been equally expedient. Where this has caused some disappointment is the response to our request for a root cause of the incident. These requests tend to queue up and we don’t seem to get answers beyond the typical vendor response of “that’s been fixed in the next release”.

The purchase of Greenplum was our first interaction with Pivotal. We have been a customer of EMC for a very long time.

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.).

We install this solution for our clients. At the moment we are in the middle of an installation for a data warehouse that will be used by a telecommunications company that is based in Lesotho. We have not gone into production yet, but we have used it in a test environment and it works very well.

We are a technology company, so we handle software development, software implementation, data warehousing, and business intelligence.

We are using the on-premise deployment model. In Africa, there isn't much adoption of cloud services, so most of our clients are expecting on-premise implementation.

We chose Greenplum because of the architecture in terms of clustering databases and being able to have, or at least utilize the resources that are sitting on a database.

The installation is difficult and should be made easier. Maybe if the process was simpler it would have a quicker adoption by other developers. This could also be accomplished by providing training aids, such as videos to help with installation or using certain features. There are resources currently available on their website, but you have to search through a lot of documentation.

Our expectation is that the scalability will be good, as it is one of the main reasons that we have invested in this solution.

To this point, I have referenced the material on the website but have not really interacted with technical support.

The initial setup of this solution is not very simple. You need to properly follow the steps in terms of getting the whole architecture put together. We have a team of five people who are working on different aspects of the implementation.

Currently, we are focusing on the data layer. Next will be the ETL layer.

We are using our in-house team to implement this solution for our client.

We have used Oracle and Microsoft SQL, but we haven't had much success. We found that Oracle was not as scalable and we were having some performance bottlenecks. Also, from a licensing perspective, Greenplum was a better choice. For all of these reasons, we have chosen to invest heavily in Greenplum.

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 an MPP architecture database. Data can be distributed across multiple nodes and strong distribution will allow queries to execute on all segments at once, which is very powerful. As long as we have good SQL knowledge, we can start playing in the platform. Greenplum uses Postgres and ANSI Standard SQL. Also, it supports many other procedural languages, such as Python, C++, and Pearl.

Greenplum is a high powered, multi-node database that was chosen for its capacity to ingest and query data at extremely high rates of speed, enabling in Database Analytics and Statistical output on granular levels of data that was otherwise inaccessible before its deployment. We were able to analyze and produce output on large volumes of data very quickly which saved us lots of time (we used to wait for hours to get the same output). The management was able to get insights very quickly so that they can make informed decisions.

I used Greenplum between Aug 2011 – Aug 2015. Almost all the members in the analytics team used Greenplum on a daily basis.

There were no issues, and it was doing what it was supposed to do.

There were no issues, and it was doing what it was supposed to do.

We had a pre-sales consultant who provided end-end solution about the product. Also, he was working with our data and clearly demonstrated the advantages of Greenplum. After we purchased the product, we were provided a full time consultant who had extensive knowledge about the product. He was primarily responsible for providing hands on experience on projects and also did excellent job of teaching everyone and bringing everyone up to the speed on the new platform. We also had a technical person offshore who was responsible for fixing things if something breaks up or any other issues.

We did use other products in the company but it wasn’t an MPP architecture database. Our data was getting bigger so we needed something with MPP architecture to tackle big data challenges so Greenplum was considered. It was a management decision to purchase this product (not sure whether other similar products were considered or not)

All the set up I believe was done by Greenplum team.

I didn't have any visibility on the pricing and licensing. But I can say that, we needed product like Greenplum to store, manage and analyze huge volumes of data which can be daunting task

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

With VMware Tanzu Greenplum, one can make a huge database table and analyze the queries by adding in the SQL command. Some hint or command for the query goes over the multi-parallel execution. Also, queries can be sent quickly to other database systems.

VMware Tanzu Greenplum needs improvement in the memory area and improved methods for quick access to the disc. So, one of the quick goals of Greenplum must work on enhancing access to the disc by adding hints in the database.

I have been using VMware Tanzu Greenplum since 2017. I have installed the ring through the Dell Dell Platform, the EMC platform, and the DX100.

We use the VMware Tanzu Greenplum support directly, the community for open source Greenplum database. I rate it a seven out of ten.

Neutral

The initial setup was complex because of the architecture we used. Ten people are needed to maintain the product, of which eight are DBAs, and two support the operating system as the administrator.

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.