VMware Tanzu Data Solutions Reviews and Pricing

Engineer at a marketing services firm with 51-200 employees

Dec 26, 2013

We use Greenplum for production but not for development and testing because it consumes too much resources

We use the scrum methodology to manage our engineering work. And while this does give us the flexibility to quickly respond to changing business needs, it also means that our databases’ schemas are not set in stone. To accomodate frequent changes to the way we structure our data, we’ve adapted Ruby on Rails database migrations for use beyond our Rails apps.

Our first challenge was to find and extract the migration’s functionality from Rails so that we could use it without needing to bring in the other features of Rails we didn’t need. Thankfully, this turned out to be relatively simple because almost all of the functionality can already be accessed by Rake tasks, so it was just a matter of building a Rake file that contains the tasks we wanted from Rails. The only thing we had to add to get started was a Rake task for creating new migrations, since Rails creates them either automatically when creating new model objects or through the `rails generate migration` command.

We quickly ran into problems, though, because we use Greenplum, a DBMS built on Postgres that adds support for features that help accomodate big data. Unlike standard Postgres, Greenplum adds features, such as table partitions, not found in most DBMSes, but Rails is designed to be DBMS agnostic so you can easily switch between, say, Postgres and MySQL and SQLite without any more trouble than changing a single configuration file. So we decided to cut around Rails’s database abstractions and instead directly write SQL in our migrations and dump SQL structure files rather than Rails schema files.

Only this didn’t work because, to complicate matters further, although we use Greenplum in production and staging environments, for local testing and development we use Postgres because Greenplum has minimum requirements that ended up consuming too much of our workstations’ resources. So we ultimately ended up developing a hybrid solution that allows us to support Greenplum and Postgres in the same migrations.

The two main aspects of the solution are selective application of options when making changes to the database and keeping two separate dumps of the database, one for Greenplum with Greemplum-only syntax and one for Postgres with Greenplum-only syntax removed. For example, we rewrote the Rails `create_table` migration function to add support for Greenplum options like partitions, data distribution, and append-only tables, but then have the function ignore those options when running the migrations against our development Postgres databases. This allows us to use a single set of migrations on all of our databases, drastically simplifying what could have otherwise been a gnarly challenge.

By no means is our solution complete or perfect. It only supports those features of Greenplum that we use, and new features only get added in as we need them, plus there is some risk associated with keeping two separate schema dumps and the potential for them to get out-of-sync. Still, compared to maintaining a database with frequently changing schemas without the aid migrations, it’s lightyears better.

Disclosure: I am a real user, and this review is based on my own experience and opinions.

it_user740442

IT Software Engineer at a financial services firm with 10,001+ employees

Sep 24, 2017

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Its valuable because of its high performance, integration with Spring Framework, easy installation, and configuration

What is most valuable?

High performance
Integration with Spring Framework
Easy installation and configuration

How has it helped my organization?

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.

What needs improvement?

In build monitoring, the interface could be improved.

For how long have I used the solution?

18 months.

What do I think about the stability of the solution?

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.

What do I think about the scalability of the solution?

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

How are customer service and technical support?

A nine out of 10.

Which solution did I use previously and why did I switch?

No, GemFire was always good enough for us.

How was the initial setup?

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

What's my experience with pricing, setup cost, and licensing?

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

Which other solutions did I evaluate?

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

What other advice do I have?

Trust GemFire and use the benefits of their strong documentation.

Disclosure: I am a real user, and this review is based on my own experience and opinions.

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Statistician376

Statistician at a financial services firm with 1,001-5,000 employees

Apr 17, 2017

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We were able to analyze and produce output on large volumes of data very quickly which saved us lots of time.

What is most valuable?

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.

How has it helped my organization?

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.

For how long have I used the solution?

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

What do I think about the stability of the solution?

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

What do I think about the scalability of the solution?

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

How are customer service and technical support?

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.

Which solution did I use previously and why did I switch?

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)

What about the implementation team?

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

What was our ROI?

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

What other advice do I have?

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

Disclosure: My company has a business relationship with this vendor other than being a customer: To my knowledge, we were one of the biggest customers in Canada, they were looking for our feedback to improve the product offerings.

it_user571806

Founder Partner and CTO at Rogue Startup

Jan 5, 2017

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It allows developers to focus on application functionality without having to re-invent interprocess communication.

What is most valuable?

The most valuable feature is it’s robustness. Message queues need to be extremely reliable as they are the glue between system components.

Also, the speed is important and its good scaling capabilities.

How has it helped my organization?

It allows developers to focus on application functionality without having to re-invent interprocess communication, which is difficult.

I also allows us to develop smaller, more efficient, and less complex subcomponents of a larger application.

What needs improvement?

I would like to see better documentation on how to set up complex webs of RabbitMQ servers — master/slave, multi-master, etc.

For how long have I used the solution?

I have been using RabbitMQ for 7+ years.

What do I think about the stability of the solution?

We have not encountered any stability issues.

What do I think about the scalability of the solution?

We have not encountered any scalability issues.

Which solution did I use previously and why did I switch?

We were using IBM MQ, but it was too costly and not open source.

How was the initial setup?

The initial setup was simple for my applications, but I have not used RabbitMQ on a complex project that would require clusters of servers.

What other advice do I have?

My advice is to read the message boards and play with the API.

Disclosure: I am a real user, and this review is based on my own experience and opinions.

it_user371805

Senior Business Intelligence Developer at a tech services company with 501-1,000 employees

Jun 18, 2016

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

Valuable Features

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

Improvements to My Organization

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

Room for Improvement

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.

Deployment Issues

We haven't had any issues with deployment.

Stability Issues

It's been stable for us.

Scalability Issues

It's scaled for our needs.

Customer Service and Technical Support

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

Disclosure: I am a real user, and this review is based on my own experience and opinions.

it_user373128

Data Architect & ETL Lead at a financial services firm with 1,001-5,000 employees

Jan 20, 2016

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

Valuable Features:

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

Improvements to My Organization:

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

Room for Improvement:

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.

Deployment Issues:

There was no issue with the deployment.

Stability Issues:

There were issues with the stability.

Scalability Issues:

There were issues with the scalability.

Other Advice:

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.

Disclosure: I am a real user, and this review is based on my own experience and opinions.

it_user827274

IT Manager at a financial services firm with 10,001+ employees

Mar 4, 2018

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Helps to remove a lot of the complexities and create a loosely coupled codebase

Pros and Cons

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

What is our primary use case?

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

How has it helped my organization?

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

What is most valuable?

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

What needs improvement?

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

For how long have I used the solution?

Still implementing.

Which solution did I use previously and why did I switch?

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

Which other solutions did I evaluate?

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

Disclosure: I am a real user, and this review is based on my own experience and opinions.

it_user749277CTO E-Commerce with 1,001-5,000 employees

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May 2, 2018

Hi,

I am a real user too and I would say that it depends really on the context. You can consider two generation of brokers, old ones are pure brokers (RabbitMQ, ActiveMQ, ZeroMQ etc.) and new ones are stream oriented (Kafka, Artemis, etc.). The performance difference is huge, around 4000 msg/s for old brokers, around 60,000 msg/s for stream based.

we used RabbitMQ for years and we are moving right now for many reasons:
- RabbitMQ is one of the leading implementation of the AMQP protocol. Therefore, it implements a broker architecture, meaning that messages are queued on a central node before being sent to clients. This approach makes RabbitMQ very easy to use and deploy, because advanced scenarios like routing, load balancing or persistent message queuing are supported in just a few lines of code. However, it also makes it less scalable and “slower” because the central node adds latency and message envelopes are quite big.
- Nevertheless, Using standard AMQP 0.9.1, the only way to guarantee that a message isn't lost is by using transactions -- make the channel transactional, publish the message, commit. In this case, transactions are unnecessarily heavyweight and decrease throughput by a factor of 250. To remedy this, you need to implement confirmation mechanism that challenge a lot the easiness of implementation
- Replication on RabbitMQ 3.6 (the last version supporting AMQP 0.9,1) makes RabbitMQ having deadlocks between nodes and created a lot of issues in production in our systems
- Last, Erlang is a black box and many times RabbitMQ crashes with Erlang errors that were a shame to make us able to diagnose quickly and efficiently.

So my recommendation, don't use RabbitMQ on a transactional path, it remains good for back-office messages as long as you can implement your own transactions in an optimistic way (with retry and message duplication detection on application side)

In my context, we are moving to Kafka that shows performance, scalability and stability.

it_user618963

Senior Software Specialist at a security firm with 1,001-5,000 employees

Mar 13, 2017

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SSL, clustering, and integrates with LDAP.

What is most valuable?

Does SSL (security)
Does clustering (stability)
Integrates with LDAP (management)
Automatically resends data when a consumer fails
Automatically routes data
Excellent spring boot integration
Multiple programming languages provide excellent integration

How has it helped my organization?

With RabbitMQ cluster servicing micro-services, we don't have any downtime and we don't lose any data. We can update and/or upgrade the micro-services without downtime.

What needs improvement?

You cannot edit shovels other than by recreating them.
Routing of data could be more enhanced with a nice GUI. ("IF header.contains(this.thing) THEN data.goesTo(cluster_02)").
In its current form, you have to recreate a shovel with the same parameters except for the one you want to change. You end up doing more or less a delete/create.
There is no HTML form where you can click on a shovel and adjust the wrong parameter.
If I click on a shovel, I get on a page that lists the shovel, but it is not editable. You have to create a shovel and then delete the old one with all the same parameters, except for the one you want to change.
Temporarily stopping shovels is also not possible in the web interface. I do not know if the CLI version can do it, but if somebody wants to temporarily stop the incoming flow, he or she has to delete the shovel and then recreate it afterwards. This is annoying, to say the least.
RabbitMQ has to be started before one can define exchanges, queues, and even users with rabbitmqctl. See https://www.rabbitmq.com/man/r...
This is no problem if one lives in the monolithic server environment. However, if one wanted to make a RabbitMQ Docker-container with a pre-defined set of exchanges, queues, users, and shovels, you have to literally jump start the server. You would have to configure it in the Docker build phase. You would do it like this in the Dockerfile: RUN service start rabbitmq-server && wait 30 && rabbitmqctl add_user mike mikespassword.

For how long have I used the solution?

I have used RabbitMQ for four years.

What do I think about the stability of the solution?

We did have stability issues in the past. After shutting it down, the cluster did not start until we deleted some corrupted file. This occurred more than a year ago.

What do I think about the scalability of the solution?

It works as expected, i.e., flawless.

How are customer service and technical support?

We have not needed any technical support as of yet.

Which solution did I use previously and why did I switch?

We did not evaluate any previous solutions.

How was the initial setup?

Just enter this command: $ apt-get install rabbitmq-server

What's my experience with pricing, setup cost, and licensing?

It’s open source with paid support.

Which other solutions did I evaluate?

We looked at Kafka, but we needed the routing as well.

What other advice do I have?

Start it in Docker and use Java Spring Boot or Node.JS with amqplib to connect to it. It has transformed how I think data should flow in an organization.

Disclosure: I am a real user, and this review is based on my own experience and opinions.