VMware Tanzu Data Solutions Reviews

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.

• High performance
• Integration with Spring Framework
• Easy installation and configuration

We have been using GemFire for a Telco project, which we need process network data in real time and meanwhile access some reference data. GemFire has done a great job, as we have managed to process over 200,000 messages per second.

In build monitoring, the interface could be improved.

18 months.

With version 8.1, we had some issues while we were querying data from memory, but it has been fixed in version 8.2, and after that we have never had problems.

No, even when we had serious network problems, GemFire managed to recover.

A nine out of 10.

No, GemFire was always good enough for us.

Initial setup was easy. In older versions, the user interface was not helpful, but it's improved lately.

As a developer, I was never a part of this pricing decision. That's why I have no advice.

No, because we've started to use GemFire as part of a real-time intelligence platform.

Trust GemFire and use the benefits of their strong documentation.

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.

External tables to load the data from flat files.

It helped us in loading millions of data in quick time & improved query response.

More stability in terms of query result.

Five years - DCA V2.0 EMC & Greenplum Database

No.

Not usually, but sometimes we do.

No issues encountered.

Excellent.

Excellent.

We applied MPP Appliance for the first time earlier we were running on SMP’s.

It was straightforward.

Vendor- they had good expertise.

Good.

We also looked at Teradata.

Very good and cost effective with very good customer support.

• Distributed data for performance
• File loaders to reduce dependency on Informatica

Batch processing times have dramatically decreased from over 12 hours to under three hours. Much of this is converting off of Informatica and using distributed processing. Reporting performance also improved greatly.

Since we are upgrading to a new version at this time, it’s hard to say. But we seem to be replacing a disk on the appliance every week.

I've used it for six years.

There's no issues, although we are currently in the middle of an upgrade to v4.3.5.

It seems like we are replacing a disk on the appliance every week. Not a noticeable issue for users and batch processing is not adversely impacted.

Very good. Response times are good for service calls.

I believe the set-up was straightforward. I don’t remember any issues.

We used a vendor team. My advice is that the planning is critical. When converting processing jobs, convert them to PostgreSQL for better performance. Yes they could work as written previously, but you benefit using the features of the product immediately if you adjust the processing to match appliance. Use the bulk processing to your advantage.

We evaluated other solutions. Not sure of the reasons why this product was chosen.

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

The security is great. The interface is nothing special. The support hasn't been that great. You need to go online yourself, to the developer community for support. But apart from that, I think it's fairly secured, quite robust, and stable so far.

Their implementation is quite tricky. It's not that easy to implement RabbitMQ as a cluster. It would be great if they could improve that. 

I have been using RabbitMQ for three years. 

It's quite stable.

It is very scalable. I have a team of 10 developers who are using it and I have some of our clients using it in our Cloud cluster. We have about 10 banks running on the platform now.

I would rate their support as a five out of ten. It has room for major improvement. 

The initial setup is quite straightforward. It can be done within a few hours. 

I would recommend it, but I think there should be a proof of concept for the developer team to get their hands around it and have full control of it before they go into production.

I would rate RabbitMQ a seven out of ten. 

In the next release, I would like for them to improve the UI.

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.