The product is not complex; I do not have to create stored procedures, functions, or views. It's just simple to use.

Speaking about the features that have proven to be the most effective for managing database operations, I would say that RabbitMQ is used for the middleware layer, which will give good performance in terms of TPS and availability.

With RabbitMQ, I appreciate that it's an open-source platform. 

RabbitMQ provides access to SDKs for development and the ability to raise and log tickets if we encounter issues. We can integrate RabbitMQ using various languages like Java or Python using the provided SDKs.

By comparison, Anypoint MQ it is a bit difficult. It's not open-source, so we have less flexibility. It's tightly coupled inside the Anypoint platform. This gives an advantage to clients already heavily dependent on the Anypoint platform, as they can easily adopt Anypoint MQ. 

Aside from that, Anypoint MQ is generally well-optimized inside Anypoint. Other aspects like reliability are standard across queueing systems. 

I haven't encountered any issues with the features I've used within the queueing functionality. Since queues provide message reliability, that's our primary concern.

RabbitMQ integrates easily. It's open-source and provides the required SDKs for development.

Overall, the aspects I've used – queues and monitoring – have been up to the mark.

The stretched cluster is a valuable feature.

I like many features in RabbitMQ. 

Number one, reliability for the messages is key. RabbitMQ ensures your messages are safe. They are not deleted and stuff. 

Number two, they have a very good feature called Retrying messages – it's all about retries. You can easily retry a message through RabbitMQ. So, if processing fails, you can push the message back into RabbitMQ. Maybe you can re-consume it and so on.

They also have features like tags, which we call "dead-lettering (DLXs)." If it's approved, it means the messages have been delivered. If it's false or missing the first time you get it, you can make decisions based on that. The feature assigned to code for it is called the dead-letter queue.

Moreover, the management interface is so cool. It's simple. It's able to give us an overview of the messages that have been consumed, pending messages, messages that have been delivered, messages that have been acknowledged, and so on. We can also extend that management to tools like... we can extend the management through, like, Kibana or Grafana

The solution's best feature is its exceptional speed, delivering efficient utilization of resources. It uses a memory desk processor very efficiently. It offers high performance while maintaining a low cost.

The most valuable features of the solution stem from the fact that it is flexible, resilient, and easy to improve and develop our company's projects.

The optimizer of the product is very efficient and the solution has a good data retention policy. Greenplum has good data analytics features and provides good monitoring tools. 

It is open-source.

It provides the database load-balancing capability itself. It has data, like Pgpool, an open-source database, and a load balancer also.

We can also create the cluster in between the database in active, standby mode.

The solution is stable.

It's scalable. 

It's fully open source, has a high throughput, and the serviceability is good.

The latest version is better than the older ones. The solution updates very fast. The loading speed is very good.

The most valuable feature is asynchronous calls, which are easy to configure.

Its most valuable feature is the ability to quickly run through data. It has a good processing speed as well.

The features of the solution are similar to Anypoint MQ. The transaction of data is pretty fast. Large amounts of data can be moved pretty fast using the solution.

The product's feature of data transaction works fast.

In general, I think this is a very good platform, particularly as it's open source. It fits very well with use cases for public institutions or universities, where there is not always a big budget.

The solution is simple to use.

It's great for messaging and consumer publishing.

Companies can scale the solution, so long as they have server room.

The stability is good.

The queues and the publishing are quite useful. We're able to create hierarchies and control channels and flows to control what is going from which queue.

The solution can scale.

It is stable and reliable. 

Tanzu Greenplum's most valuable features include: the integration of modern data science approaches across an MPP platform, including the ability to massively denormalize data and spread it across your MPP segments; the ability to index data and make it searchable, which significantly reduces the need for ETL; its fast performance. Tanzu Greenplum is also very active in providing additional functionalities and software when needed, like GPText indexing of JSON events.

The high availability and not having to replicate is valuable as is the message consumer. It can be configured depending on the use case to be a very fast message broker. I like the stability, the built-in admin tools and the plugin architecture. One of the things that makes it unique is that all of the components for messaging can be created programmatically, meaning you can have services or applications that get spun up or have auto incrementing instances. If you're in an elastic environment, you don't have to pre-configure the messaging system and the keys don't have to be known ahead of time. 

Some of the most valuable features are “publish and subscribe”, fanout queueing, and scalability.

We have a number of different use cases in our scenario. A key one is “publish and subscribe”. We have spent the last year breaking up a large monolithic application into microservices and each microservice has to subscribe to different events for the purpose of CQRS and other kinds of updates. RabbitMQ is perfect for “publish and subscribe”. It does an awesome job at fanout, perfect for CQRS, messages are delivered to all subscribers with almost no additional latency.

The parallel load features mean that Greenplum is capable of high-volume data loading in parallel to all of the cluster segments, which is really valuable.

The service management capabilities are good.

The external data integration with Parquet, Avro, CSV, and unstructured JSON works well.

It has an advanced query optimizer.

The most valuable feature is that it's really customizable.

When you have simple tasks, it is easy to use.

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.

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.

In terms of most valuable features, I'd say Pivotal Greenplum's shared-nothing architecture.

The solution has really cool features to use. Its management console is excellent. You can utilize plugins to view the performance of the whole service on one network. It's wonderful. I really like it.

The most valuable feature for us is horizontal scaling.

It's a good core database. Scalability and performance are very good. I also like the fact the solution is open-source, so you can use it free of charge.

We chose Greenplum because of the architecture in terms of clustering databases and being able to have, or at least utilize the resources that are sitting on a database.

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

RabbitMQ is a solid, widely-used messaging system with a low cost-of-ownership. It is open, but with commercial support potentially available from Pivotal if required. (We have never needed it.) There is also a strong online user community.

One crucial feature was guaranteed messaging. We needed a solution that we could trust to not lose data.

Its built-in clustering capability allowed us to configure it as a highly available message broker, so that we can have confidence in the resilience of our architecture.

It can be scaled as well, although we have not tested this.

After almost two years' usage in our production environment, I am impressed by how stable the platform is - even when running on Windows Server 2012. Sure, we have had to tweak our set-up here and there as we have learned a few operational lessons along the way but overall it is very good.

• 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.

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

• Very fast for query processing
• Parallel load
• Polymorphic storage

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

Of particular value to our environment and applications are the following Greenplum capabilities:

1. Scalable (Massive) Parallel Processing (MPP) – The ability to bring to bear large amounts of compute against large data sets with Greenplum and the EMC DCA has proven itself to be very effective.
2. Fast load of data into Greenplum – We experience performance of approximately 1TB per hour loading data to Greenplum without the use of specialized hardware.
3. MADlib (madlib.net) – There are a number of statistical and analytical functions available within MADlib upon which we rely. Among these are linear regression, logistic regression, apriori, k-means, principle component analysis, etc.
4. User Defined Functions in Python (UDFs in PL/Python) – Where MADlib does not provide a direct solution to an application problem, the ability to quickly prototype and deploy user defined functions with Python has been effective.

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

The queuing system is the most valuable feature. It meets our requirements.

The product's reliability is the most valuable feature.

• Queues and topics
• Native cloud app support
• Light-weight
• Easy maintenance
• Simple and straightforward admin portals: Made it easy for users and worked out excellently for our requirements

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.

Guaranteed message delivery, queuing, and low latency delivery.

• Great management UI: The best in its class of messaging products
• Very sophisticated routing control and priority messaging capabilities

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

The most valuable features for us are speed and persistent messaging.

Complex message routing makes it very easy to replicate product data for user acceptance testing which was required in our user case.

• 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

• The publisher and consumer ACK
• High availability
• Queue mirroring
• Exchanges and topics
• Supported programming languages with well-tested libraries

The most valuable feature of RabbitMQ is the ability to set up workflows simply with configuration. We had some very complex problems (logging, auditing, sequential and parallel operations) that have been easily solved by inserting a queue in the middle of an existing workflow.

• 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

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

Some valuable features of this product are:

• Message queuing
• Good support
• Provided scalability and a distributed environment
• Easy usability with NodeJS
• Could easily withstand and pass stress/load testing with more than 10K API calls
• Helped achieve a distributed environment and implement a 3-tier structure
• Helped to develop a highly scalable system by decoupling front end and back end

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.

RabbitMQ is good in its real-time capability of delivering the sensor data in different topic abstractions. It is quite easy to setup in our sensor network system which involves data monitor agents across geographically distributed organizations.

One of our favorite features is the exchange. The queuing out-of-the-box is awesome. Also, due to the clustering, our system was highly available.

We find authentication, performance, and durable messaging the most valuable features. Due to the nature of my use case, performance and security were of the highest priority and were the main reason I chose RabbitMQ messaging frameworks.

Over time, the most useful feature became the ability to create durable queues, which increased reliability and also boosted performance.

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.

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.

Message queue, because it is easy to use, reliable, not a big load.

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

Strong integration with Greenplum Servers.

I've found that the data compression and ETL are the most valuable features for us.

In 4.3.8.1 Pivotal confirmed that even restoring schema level backup is possible from a DB backup.

- restoring schema from a DB level backup has been tested and working fine .

ORCA - the Pivotal Optimizer does a good query plan but does not works with all business logics. This needs to be tested based on your requirement.

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

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

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

The most valuable features are MPP and that it's polymorphic.

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

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

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

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

• Parallel processing
• Takes minutes to process millions of records

MPP (Massive Parallel Processing); processing large amount of data.

The MPP – fast aggregation and that I still reuse the PL/SQL code.

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

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

The performance is the most valuable.

External tables to load the data from flat files.

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.

It's super easy to deploy and it also supports different languages and analytics.

The most valuable feature for me is that it is open source. The licensing costs are really low and they are transparent. You know how much you are going to pay if you want the commercial version. If you want the open-source version, you do not have to pay anything and that is just as reliable as the commercial one.