We performed a comparison between TIBCO Live Datamart, Vertica, and VMware Tanzu Greenplum based on real PeerSpot user reviews.
Find out what your peers are saying about Snowflake Computing, Oracle, Teradata and others in Data Warehouse."You can create your own rules that include mathematic calculations."
"The solution has a powerful aggregating feature"
"Vertica is a columnar database where the query performance is extremely fast and it can be used for real-time integrations for API and other applications. The solution requires zero maintenance which is helpful."
"The most valuable feature of Vertica is the unmatchable database performance."
"Any novice user can tune vertical queries with minimal training (or no training at all)."
"Integrated R and geospatial functions are helping us improve efficiency and explore new revenue streams. "
"Speed and resiliency are probably the best parts of this product."
"Initiate on one node, and the RPM propagates automatically to all other nodes. "
"The fast columnar store database structure allows our query times to be at least 10x faster than on any other database."
"I have found the solution to be scalable."
"Scalability is simple because it's an MPP database. If you need more processing power or you need more storage, you just add a few more nodes in the cluster. It works on common commodity hardware. You can use any type of server. You don't need to have proprietary hardware. It's fairly flexible."
"A very good, open-source platform."
"Helps us to achieve large-scale analytics."
"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."
"Tanzu Greenplum's most valuable features include the integration of modern data science approaches across an MPP platform."
"The most valuable feature for us is horizontal scaling."
"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."
"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."
"Improvements need to be made on the load balancing side."
"The solution's setup could be quicker and easier."
"When it is about to reach the maximum storage capacity, it becomes slow."
"It needs integration with multiple clouds."
"Promotion/marketing must be improved, even though it is a very useful product at very good price, it is not as "popular" as it should be."
"The integration of this solution with ODI could be improved."
"Fact-to-fact joins on multi-billion record tables perform poorly."
"Metadata for database files scale okay, but metadata related to tables/columns/sequences must be stored on all nodes."
"Support is an area where it could get better."
"Documentation has become much better, but can always use some improvement."
"We would like to see Greenplum maintain a closer relationship with and parity to features implemented in PostgreSQL."
"Implementation takes a long time."
"They need to enhance integration with other Big Data products... to integrate with Big Data platforms, and to open a bi-directional connection between Greenplum and Big Data."
"Tanzu Greenplum's compression for GPText could be made more efficient."
"Initial setup is a little complex. It took around two weeks to deploy."
"The initial setup is somewhat complex and the out-of-the-box configuration requires optimization."
"Lacks sufficient inbuilt machine-learning functions for complex use cases."
"They should add more analytics. Their documentation could also be improved so that I don't have to bother my co-workers and tech support so often."
