VMware Tanzu Data Solutions Reviews

I've found that the database warehouse, data compression, and ETL to be the most valuable features for us.

Loading batch data has really improved the efficiency of our organization.

I'd like so see better scaling, better performance from in-memory databases, and a higher compression rate. We have been facing some performance issue when doing batch loading with optimizer the scaling does works fine. They are working on having optimization techniques which made me write room for improvement.

I've used it for over two years. I have been working very closely with the EMC folks.

Yes, at times, but it depends on your modeling and data retrieval.

It's been stable for us.

Its scalability needs to be improved.

I would rate technical support as good and there is not much technical expertise at the start of the SR.

We tried other MPP’s.

It was complex, but there was a change in the setup.

We got support from the vendor at the start.

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

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.

• 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.

I am still comparing RabbitMQ and Kafka, but based upon the information I have gathered RabbitMQ is an awesome tool.

RabbitMQ will help to remove a lot of the complexities and create a loosely coupled codebase.

I like the high throughput of 20K messages/sec, and that it supports multiple protocols. The flexible routing is great as well.

The next release should include some of the flexibility and features that Kafka offers.

I have used IBM MQ software, but it was not applicable to this application.

I have evaluated and researched Axon, RabbitMQ, Kafka, and IBM MQ.

• AMQP protocol
• The simplicity to set up a cluster
• All patterns available (Topic, Routing, RPC, etc.)
• Enables you to provide a response to most problems encountered

I introduced RabbitMQ to my company to bring about more scalability and redirect asynchronous tasks outside of the frontal web servers.

• Scalability was provided by the possibility to add on workers behind RabbitMQ. Asynchronous is the keyword for the message broker.
• I started to split our monolith website and remove all the heavy processes from our web servers.
• Dispatching the workload correctly helped to increase the response time.
• The frontal web server must focus on replying and not on doing reports/PDFs. You must have a dedicated server for this.
• The solution is written in Erlang. Erlang was built for telecommunication systems. One of its assets is that it can upgrade a service in production without downtime. That’s a good point!

• Have more features such as being able to replay a sequence of what was received.
• Handle more messages per second.
• Consume fewer resources: NATS can handle millions of requests within a few minutes. RabbitMQ handles hundreds of requests with the same resources (RAM). Finding a way to be more efficient in this aspect would open them up to other markets, like IoT or embedded systems.

I have used this solution for around two years.

There were no stability issues.

There were no scalability issues.

The documentation from the internet is good.

The setup was pretty straightforward.

I have looked at Apache ActiveMQ and Kafka.

This product is fantastic. Before you use it, make sure you analyze your real needs. In the world of message brokers, there is no single solution. Today, you have solutions that are specialized for specific use cases.

For instance, you have the NATS solution which is another great message broker tool. It is focused on handling a tremendous number of topics and messages per second and it consumes few resources.

However, it’s not as resilient as Kafka. Kafka can replay all the messages received from a date range. However, this one can handle fewer topics.

NATS is written in Golang. Golang is the Go programming language, which I love.

The use case involves the transferring of messages between services. It includes asynchronous messaging and I also need messages flowing to multiple microservices. In this case, it's basically a fan-out mechanism.

RabbitMQ is configurable and quite flexible.

The performance is fast and reliable.

It is easy to use. The addition of more queues and more services can be managed very easily.

There are some security concerns that have been raised with this product.

The configuration works with a config file, where all of the controls, including that of the administrator and user access, are stored there. The security isn't very stringent or very elaborate.

RabbitMQ is a stable solution.

This is a scalable product. There are six or seven services that are connected and using it.

I have not been involved with anything that necessitated contacting the support team.

Previously, we were using MSMQ, which is the Microsoft Message Queuing service. However, with the new version of .NET 4, support for MSMQ was no longer available. We could no longer get the libraries. As a result, we had to switch to a different queue mechanism. 

I have also used Azure Queue.

The initial deployment was quite easy. It took less than half an hour to get it up and running.

I deployed it myself.

The pricing for RabbitMQ is reasonable. It is worth the cost.

We researched ZeroMQ, RabbitMQ, and Kafka. We found that Kafka was a bit of an overkill because our requirements were quite simple. RabbitMQ was pretty easy to set up, which is why we chose it.

My advice for anybody who is implementing this product is to establish your users and exchanges properly.

In general, I'm quite comfortable with RabbitMQ. It satisfies my requirements and the main complaint I have is about the security.

I would rate this solution a seven out of ten.

• Transaction processing between microservices
• Message queue integration with Spring and RPC over Rabbit.

• Quick and simple to implement

• Easy to build proof-of-concept modules based on working examples from Pivotal.

• Out-of-the-box setup and configuration
• Great documentation and support from Pivotal
• Very useful management console

• Difficult to integrate with automated test and CICD 
• Moving beyond basic configurations can be challenging
• Not clear how to implement durable subscriber connections
• Not clear how a Rabbit service restart allows subscriber auto re-connect
• Service cluster failover depends on shared disk infrastructure.

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.