VMware Tanzu Greenplum Reviews

Append Only tables, data compression and bulk load and extraction using External Tables are very valuable features for us.

We have improved our quarterly statements turnaround dramatically and could sustain for increasing data.

With the ORCA optimizer the earlier Append-Only feature has been upgraded to Append-Optimized where now we can update the data on earlier Append-Only tables just like any other heap tables. But I found this has increased the time taken for Vacuum Analyze operation on these tables like from 10 mins to 1 hr + (on large tables). In our case we don't need an update on our Append Only tables and hence this became a drawback. VA on Append-Optimized tables need to be improved.

Backup & Restore performance need to be improved.

ORCA optimizer when turned on is not showing consistency. Some workloads shows improved performance and some workloads became very slow. This need to be improved for consistency.

I have used it for about 4 years now.

Pre ORCA version was stable. ORCA release is not stable. Some workloads slowed down with new release even when the new optimizer is not turned ON.

Tech support is average. They lack information about new features in the new releases and the possible impact of them.

Earlier we were using OLTP based RDBMS solution. We realized we needed a OLAP solution and also something that can scale horizontally.

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.

Processing speed – especially loading and transformation of large data sets.

Before we implemented Greenplum, our weekly data loads (for third party marketing data sets) were taking over three days. (We also had some monthly data that could take up to 3 days to load and transform via Informatica.) After we implemented Greenplum, the loads were reduced to less than nine hours. Previously, we were receiving data early Wed a.m. and not getting out to the salesforce (if we were lucky) until noon on the following Monday. Now we get the data to the field early Friday mornings before they wake up.

The Greenplum appliance itself has had some reliability issues, so it would be great if that could be improved in the next version. More critical, though, is that the latest devices are not backward compatible. i.e., We have to replace our entire environment to upgrade. That’s quite an expense. I would hope they could improve the upgrade roadmap in the future.

We have used EMC Consulting for some projects, and we have lots of EMC storage.

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.

• Parallel Processing and Data Distribution based architecture.
• HA built-in with mirroring.
• Highly configurable and lots of tuning features.

• This has helped us bring down our end-to-end EDW load time to 1/3 the time.
• It has enabled faster and efficient data analysis.
• Scalable environment without adding too much cost.

• It needs a much more robust and user friendly monitoring and management front-end tool.
• More stability and auto-recovery with the segments.
• Report generations on system health and recommendations.

I've used it for two years.

Up to now, we've had no issues with deployment.

Up to now, we've had no issues with stability.

Up to now, we've had no issues with scalability.

The response is fairly good but would like more support from the R&D on more complex issues. Also, they need to ensure there are logs that can be used without causing any downtimes to the system for any case analysis.

It's a highly efficient and faster DB with lot of features at much less cost to that of other MPP DB’s evaluated.

It was complex as we have to code convert everything into GP functions so as to best be able to use the GP parallel. Pushdown feature was not available via Informatica. The initial parameter setup took quite some time to test to get the sweet spot for performance.

In-house with vendor support.

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.

The most valuable feature of Greenplum is the Massively Parallel Processing (MPP).

With this solution, we've reduced load on the OLTP systems.

The fact GreenPlum is using an older version of Postgres means developers coming from other products will find many missing features in PostgreSQL, features which you would assume are standard.

Greenplum is based on Postgres 8.2.15 which was released in 2009. While the SQL syntax and functionality has continued to evolve in other platforms in the ensuing years it appears to have stagnated in Greenplum.

We haven't had any issues with deployment.

It's been stable for us.

It's scaled for our needs.

The community around GreenPlum is very small, making it difficult to learn from others experience via forums or blog posts.

We can integrate the Hadoop with DCA V2. This will be huge development in the big data technologies.

It increased the read/write process because of it MPP architecture.

EMC already developed DCA V3, But if the hardware is little stable, I prefer DCA V2.

I am from a support background, and have used this on multiple accounts, for the last four years.

There have been no issues with the deployment.

Hardware failure is a concern.

We have had no issues scaling it for our needs.

Technical support is excellent.

I know many customers are migrating from Oracle to Greenplum due to its faster processing.

It is straightforward,open source system.

Better chose EMC to perform the implementation. More over, it is not complex and we can do it easily in our environment with a little knowledge.

Greenplum is an opensource system, but they do charge for support.

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

The MPP element is crucial, so far as it allows us to query millions of rows at a time, at speed.

The previous data warehouse was built in Oracle. One of the things which has improved in GreenPlum is that we can query millions of rows at speed, without creating lags. We’ve also built far more views; slowly changing dimensions can instantaneously update without creating the issue of having to rebuild tables to reflect new hierarchies, for example.

We found some issues with larger tables that have daily data appended, where after a while this seems to create lag in the query speed. This might just have to do with local knowledge rather than the product itself.

We have a table which is currently contains 27.6m rows and has a daily delta added to it of roughly 16.5k rows per day. While this isn’t particularly large, we have noticed the table begins to perform poorly when queried, in spite of having set up a VACUUM process to be performed weekly. It may be that the VACUUM process needs to be performed more frequently (like daily), but we’ve not yet found the optimal way of maintaining this particular table.

It’s worth saying that this is one table out of over 400 perfectly well performant tables and views in the same database. Hope that helps,

I have used for approximately 30 months.

I have not encountered any deployment, stability or scalability issues.

I have not raised any service issues/tech queries, so I can’t really say.

We used Oracle previously. We based our choice on expertise in our US operation, where we have a GreenPlum expert who provided some amazing use case examples to help us in our selection process.

Implementation was done in-house.

Not within my area I’m afraid, but I understand that this was a very good fit from an ROI point of view

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.

There are many valuable features, such as parallel loading and the solution's scalability.

It's allowed us to do a lot of data analytics with it that we weren't able to do before.

The performance needs to be improved.

As a whole, two years on multiple versions.

We've had no issues with deployment.

It's stable, but slowness is an issue.

It's scaled find for us.

Customer service is OK.

Technical support is OK.

No solution was used previously.

Implementation was done by a vendor team.

Pricing is pretty much OK compared to others.