VMware Tanzu Data Solutions Reviews

Legacy queuing systems have been replaced by RabbitMQ. The performance has been increased to a great extent.

The message routing is the most valuable feature. It is effective and flexible.

The debugging capabilities and testing flexibilities need to be improved.

The stability was fine.

There were no scalability issues as such. The scalability was fine.

I would give technical support a rating of 5/10.

Initially, we were using different queuing technologies. Due to the message routing feature and flexibility that RabbitMQ provided, we made the switch to this tool.

The setup was easy enough, once we had done proper research before the implementation.

The pricing is okay.

This product needs to be understood completely before implementing it. One should not be mistaken that it will replace the whole messaging system as such.

• Load balancing through queues
• Guaranteed messaging
• Configurable pre-fetch count
• Queue mirroring

RabbitMQ helped us build a database synchronization framework that allowed us to transfer our clients data to our cloud based data processing centers.

The web management tool.

I have used this solution since 2013.

We had several de-clustering problems.

We did not have any scalability problems.

I have never used support.

This is the first solution we implemented.

It was a very simple setup. We had some issues with the home folder being on a non-standard system drive (The location of the RMQ cookie was changed.)

The Community Edition works fine for us.

We evaluated several other solutions; the MQSeries and MSMQ.

Use it for implementations that require a queuing solution. It is easy to overuse it as a universal communication bus of the entire system.

It has solved issues such as queue processing, real-time data processing and decrease count of Cron jobs.

The management of Cron jobs is very hard for me. If you are using RabbitMQ, you may not need a Cron job since you can create a consumer for a queue and listen to the queue in real-time. For example, you need to send an email for your new members but you want to do this from your software. The reason being if you encounter an SMTP error, you should handle it or sending an email from your membership activity can take about 5-10 seconds. If you don't use RabbitMQ, you need to create a Cron job and check for new members to send an email to them. But, if you use RabbitMQ, you do not need a Cron job. You can send a message to your queue and consume it in real-time and then send the email.

I have used this solution for changing stock of products in an e-commerce project. We update the stock of products from RabbitMQ. Some of our clients can change the stock of a product, i.e., either increase or decrease it. They send the stock count to RabbitMQ as a message and our e-commerce platform changes the stock when it gets the message. Consequently, we update the database, cache, search engine service and so on.

There are too many advantages of using this solution. You can scale your processes and update your data in a nearly synchronous way.

RabbitMQ needs 2 additional features:

• It is lacking a good dashboard on the web interface; maybe they can develop a dashboard for monitoring.
• There is no alert mechanism. For example, sometimes consumers may be killed or the input messages in queues are greater than the consumed messages. Thus, I would like them to define a rule for alert; maybe they can develop an alert mechanism.

I have used this product for three or four years.

I have not encountered any stability issues.

I have not encountered any scalability issues either.

Since it is open source, you can resolve your problems either from forums or Stack Overflow in regards to RabbitMQ.

We have used different solutions before. We have tried MySQL as a queue and it was terrible.

It is not complex.

However, if you try sharding or cluster, it is just a little complex because of Erlang.

It is open source.

I did not look into other solutions.

I would recommend this product. It is great and resolves a lot of problems.

Allowing for a fully asynchronous solution is crucial for this particular feature. The seamless nature of creating and connecting to a queue makes it really easy to code and understand. Pivotal Cloud Foundry allows us to easily scale the number of consumers (or receivers) as well. So far, no hiccups have been found with the PCF implementation.

RabbitMQ allows for asynchronous solutions where previously everything was synchronous.

The product works pretty well, but one small thing could be an improvement to the monitoring site. It could be a little bit more modern, instead of postback refreshing, etc.

We have been using Rabbit for a while and I started integrating it into the mobile project a few months ago.

Every so often, I need to clear out the queue during development. This could be a symptom of something else, but unpacked requests tend to get trapped in the queue at times.

PCF allows us to scale the consumers.

I haven't used any technical support yet.

To my knowledge, this is the only queuing system my company has used.

Thanks to Pivotal Cloud Foundry, initial setup was straightforward. We simply created a new RabbitMQ service, obtained credentials for the queue and started developing.

I personally have not explored other queuing solutions, but have used Akka HTTP, with is a fully asynchronous web server of sorts. It's not a queuing system, but I mention it because of the asynchronous behavior. RabbitMQ was perfect for our current solution, however.

The RabbitMQ documentation is pretty good. I'd only suggest making sure to read through it for the implementation language of your choice first.

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 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.

• 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.

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.