We performed a comparison between Apache Hadoop and VMware Tanzu Data Services based on real PeerSpot user reviews.
Find out in this report how the two Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."It is a file system for data collection. There are nodes in this cluster that contain all the information, directories, and other files. The nodes are based on the MySQL database."
"The most valuable features are the ability to process the machine data at a high speed, and to add structure to our data so that we can generate relevant analytics."
"The best thing about this solution is that it is very powerful and very cheap."
"Two valuable features are its scalability and parallel processing. There are jobs that cannot be done unless you have massively parallel processing."
"The solution is easy to expand. We haven't seen any issues with it in that sense. We've added 10 servers, and we've added two nodes. We've been expanding since we started using it since we started out so small. Companies that need to scale shouldn't have a problem doing so."
"What I like about Apache Hadoop is that it's for big data, in particular big data analysis, and it's the easier solution. I like the data processing feature for AI/ML use cases the most because some solutions allow me to collect data from relational databases, while Hadoop provides me with more options for newer technologies."
"What comes with the standard setup is what we mostly use, but Ambari is the most important."
"The most important feature is its ability to handle large volumes. Some of our customers have really large volumes, and it is capable of handling their data in terms of the core volume and daily incremental volume. So, its processing power and speed are most valuable."
"Very sophisticated routing control and priority messaging capabilities"
"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."
"We have been able to set up a messaging system that facilitates data integration between the software modules that we sell."
"It works very well with large database queries."
"The product's reliability is the most valuable feature."
"RabbitMQ will help to remove a lot of the complexities and create a loosely coupled codebase."
"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."
"The most valuable feature is asynchronous calls, which are easy to configure."
"It would be helpful to have more information on how to best apply this solution to smaller organizations, with less data, and grow the data lake."
"Real-time data processing is weak. This solution is very difficult to run and implement."
"The solution is not easy to use. The solution should be easy to use and suitable for almost any case connected with the use of big data or working with large amounts of data."
"From the Apache perspective or the open-source community, they need to add more capabilities to make life easier from a configuration and deployment perspective."
"The key shortcoming is its inability to handle queries when there is insufficient memory. This limitation can be bypassed by processing the data in chunks."
"The load optimization capabilities of the product are an area of concern where improvements are required."
"The stability of the solution needs improvement."
"The solution needs a better tutorial. There are only documents available currently. There's a lot of YouTube videos available. However, in terms of learning, we didn't have great success trying to learn that way. There needs to be better self-paced learning."
"The installation is difficult and should be made easier."
"We would like to see Greenplum maintain a closer relationship with and parity to features implemented in PostgreSQL."
"VMware RabbitMQ's configuration process could be easier to understand."
"There are some security concerns that have been raised with this product."
"Lacks sufficient inbuilt machine-learning functions for complex use cases."
"I saw some limitation with respect to the column store, and removing this would be an improvement."
"Their implementation is quite tricky. It's not that easy to implement RabbitMQ as a cluster."
"Maintenance is time-consuming."
Apache Hadoop is ranked 5th in Data Warehouse with 33 reviews while VMware Tanzu Data Services is ranked 6th in Data Warehouse with 81 reviews. Apache Hadoop is rated 7.8, while VMware Tanzu Data Services is rated 8.0. The top reviewer of Apache Hadoop writes "Handles huge data volumes and create your own workflows and tables but you need to have deeper knowledge". On the other hand, the top reviewer of VMware Tanzu Data Services writes "Reliable queueing functionality and versatile tool that can be used with any programming languages ". Apache Hadoop is most compared with Azure Data Factory, Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake and Amazon Redshift, whereas VMware Tanzu Data Services is most compared with IBM MQ, Apache Kafka, Anypoint MQ, ActiveMQ and Nutanix Database Service. See our Apache Hadoop vs. VMware Tanzu Data Services report.
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We monitor all Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
