We performed a comparison between Vertica and VMware Tanzu Greenplum 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."I appreciate the flexibility offered by Vertica's projections. It allows for modifying the primary projection without altering the tables, which helps to optimize queries without the need to modify the underlying data."
"It has improved my organization's functionality and performance."
"Vertica gives knowledgeable users and DBAs excellent tools for tuning."
"The Vertica architecture means it can process/ingest data in parallel to reporting and analyzing because of its in-memory Write-Optimized Storage sitting alongside the analytics optimized Read-Optimized Storage."
"For me, It's performance, scalability, low cost, and it's integrated into enterprise and big data environments."
"Speed and resiliency are probably the best parts of this product."
"The product's initial setup phase is extremely simple."
"Vertica has a few features that I like. From an architecture standpoint, they have separated compute and storage. So you have low-cost object storage for primary storage and the ability to have several sub-clusters working off the same ObjectStore. So it provides workload isolation."
"Very fast for query processing."
"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."
"It works very well with large database queries."
"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."
"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."
"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."
"Helps us to achieve large-scale analytics."
"Tanzu Greenplum's most valuable features include the integration of modern data science approaches across an MPP platform."
"Vertica seems to scale well, except for one use case where you are on a multi-node cluster. For example, if you had a nine-node cluster, one node goes down, then the eight nodes don't scale, because the absence of the node is very apparent, which is a problem. If you have nine nodes or multiple nodes, the whole idea is that if one of those nodes goes down, then you should not see an impact on the system if you have enough capacity. Even though we have enough capacity, you can still see the impact of the one node going down."
"Monitoring tools need to be lightweight. They should not take up heavy resources of the main server."
"The integration of this solution with ODI could be improved."
"They could improve on customer service."
"Suboptimal projection design causes queries to not scale linearly."
"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 documentation of Vertica is an area with shortcomings where improvements are required."
"There are a lot of limitations within this product and it makes things extremely hard for developers. It lacks Stored Procedure, packages, and triggers like other RDBMs."
"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."
"Initial setup is a little complex. It took around two weeks to deploy."
"We would like to see Greenplum maintain a closer relationship with and parity to features implemented in PostgreSQL."
"If you have a user consuming a huge load of resources, it takes down the entire system."
"VMware Tanzu Greenplum needs improvement in the memory area and improved methods for quick access to the disc. So, one of the quick goals of Greenplum must work on enhancing access to the disc by adding hints in the database."
"One of the disadvantages, not a disadvantage with the product itself, but overall, is the expertise in the marketplace. It's not easy to find a Greenplum administrator in the market, compared to other products such as Oracle."
"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."
"It will be very useful if we could communicate with other database types from Greenplum (using a database link)."
Vertica is ranked 4th in Data Warehouse with 82 reviews while VMware Tanzu Greenplum is ranked 9th in Data Warehouse with 36 reviews. Vertica is rated 8.4, while VMware Tanzu Greenplum is rated 7.8. The top reviewer of Vertica writes " A user-friendly tool that needs to improve its documentation part". On the other hand, the top reviewer of VMware Tanzu Greenplum writes "Very efficient at large scale analytics; lacks inbuilt machine-learning functions for complex use cases". Vertica is most compared with Snowflake, SQL Server, Amazon Redshift, Teradata and MySQL, whereas VMware Tanzu Greenplum is most compared with Oracle Exadata, Oracle Database Appliance, Apache Hadoop, Snowflake and Teradata. See our VMware Tanzu Greenplum vs. Vertica report.
See our list of best Data Warehouse vendors and best Cloud Data Warehouse vendors.
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.
