Compare Oracle Database In-Memory vs. Vertica
Quotes From Members
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
Pros | |
"Normally, every database server uses hard disks. In-Memory has a feature, apart from their database, which is very good. When we start our server, all your data needs loading memory. We can use that. It's a very good feature. I think they added this feature in 2019. We can mount memory in the partition, create partitions in there, and create table space from that spot to share. It's a really good feature. We use it a lot." "The solution is very fast." "The scalability of the solution is very good. It's able to support large amounts of data." "The most valuable aspects of this solution are the fast caching and improved performance to the database" "The application development is very user-friendly." | "Eighty percent of the ETL operations have improved since implementing this solution." "The performance is very good and the aggregate records are fast." "Vertica's most outstanding features are the compression rates achieved and the speed of access of high volume data." "Allows us to take volumes and process them at a very high speed." "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." "It has improved my organization's functionality and performance." "The solution has great capabilities. The tool that instructs the internal database forward is easy to use and is very powerful." |
Cons | |
"We use some partitions in In-Memory. We have a very large table and a low dose. It is very expensive in data to load all of them into In-Memory. It takes up more memory slots in the server, as well as a lot of RAM. We use last partitions on the table. We always need to create a script and make a schedule that can load a last partition in In-Memory. Oracle doesn't have features to do this automatically. I would like them to allow us to load last partitions, as well as other table partitions, in In-Memory. I think a good feature would do that automatically, letting you see a table, load a large partition, and monitor loading memory. It's quite a good feature." "The solution is quite expensive." "Technical support is below our expectations currently. It could be improved." "They should lower the price. My customers think that it's too expensive." "The pricing could be improved. It would ideal if it was more reasonable." | "Fact-to-fact joins on multi-billion record tables perform poorly." "When it is about to reach the maximum storage capacity, it becomes slow." "Support is an area where it could get better." "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." "Some of our small to medium-sized customers would like to see containerization and flexibility from the deployment standpoint." "The geospatial functionality could be designed better." "We are looking for a cheaper deployment for the solution. Although we did a lot of benchmarks, like Redshift. We tried Redshift, it didn't work. It didn't work out for us as well." "They could improve on customer service." |
Pricing and Cost Advice | |
Information Not Available | "From a cost perspective, the software is less than most of its competitors." "It's difficult today to compete with open-source solutions. In these areas, there is a lot of competition and the price of this solution is a bit pricy." |
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Questions from the Community | |
Top Answer: The most valuable aspects of this solution are the fast caching and improved performance to the database Top Answer: They should lower the price. My customers think that it's too expensive. Top Answer: I would recommend In-Memory because it is a robust and scalable RDBMS. We are able to achieve high availability. I would rate it a nine out of ten. They should improve the licensing cost. | Top Answer: SQreamDB is a GPU DB. It is not suitable for real-time oltp of course.
Cassandra is best suited for OLTP database use cases, when you need a scalable database (instead of SQL server, Postgres)… more » Top Answer: The performance is very good and the aggregate records are fast.
Top Answer: When it is about to reach the maximum storage capacity, it becomes slow. |
Ranking  | |
Views 2,546 Comparisons 2,008 Reviews 6 Average Words per Review 439 Rating 8.3 | Views 14,490 Comparisons 8,334 Reviews 7 Average Words per Review 478 Rating 9.1 |
Popular Comparisons | |
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Also Known As | |
| Micro Focus Vertica, HPE Vertica, HPE Vertica on Demand | |
Learn | |
| Oracle | Micro Focus |
Overview | |
Oracle Database In-Memory transparently accelerates analytics by orders of magnitude while simultaneously speeding up mixed-workload OLTP. With Oracle Database In-Memory, users get immediate answers to business questions that previously took hours. Oracle Database In-Memory delivers leading-edge in-memory performance without the need to restrict functionality, or accept compromises, complexity and risk. Deploying Oracle Database In-Memory with any existing Oracle Database compatible application is as easy as flipping a switch - no application changes are required. Oracle Database In-Memory is fully integrated with the Oracle Database’s renowned scale-up, scale-out, storage tiering, availability, and security technologies making it the most industrialstrength offering on the market. The ability to easily perform real-time data analysis together with real-time transaction processing on all existing applications enables organizations to transform into Real-Time Enterprises that quickly make data-driven decisions, respond instantly to customer demands, and continuously optimize all key processes. For more information on Oracle Database In-Memory, visit Oracle.com | Micro Focus Vertica is the most advanced SQL database analytics portfolio built from the very first line of code to address the most demanding Big Data analytics initiatives. Micro Focus Vertica delivers speed without compromise, scale without limits, and the broadest range of consumption models. Choose Vertica on premise, on demand, in the cloud, or on Hadoop. With support for all leading BI and visualization tools, open source technologies like Hadoop and R, and built-in analytical functions, Vertica helps you derive more value from your Enterprise Data Warehouse and data lakes and get to market faster with your analytics initiatives. To learn more about Micro Focus Vertica Advanced Analytics, visit our website. |
Offer | |
Learn more about Oracle Database In-Memory | Learn more about Vertica |
Sample Customers | |
| Shanghai Customs | Cerner, Game Show Network Game, Guess by Marciano, Supercell, Etsy, Nascar, Empirix, adMarketplace, and Cardlytics. |
Top Industries | |
Financial Services Firm11% Local Government11% Retailer11% Insurance Company11% Computer Software Company39% Comms Service Provider23% Financial Services Firm7% Government5% | Computer Software Company23% Media Company19% Marketing Services Firm16% Comms Service Provider13% Computer Software Company36% Comms Service Provider16% Financial Services Firm7% Media Company7% |
Company Size | |
Small Business29% Midsize Enterprise14% Large Enterprise57% | Small Business30% Midsize Enterprise25% Large Enterprise45% |
Find out what your peers are saying about Oracle Database In-Memory vs. Vertica and other solutions. Updated: January 2021.
457,209 professionals have used our research since 2012.
Oracle Database In-Memory is ranked 2nd in Embedded Database Software with 6 reviews while Vertica is ranked 4th in Data Warehouse with 7 reviews. Oracle Database In-Memory is rated 8.4, while Vertica is rated 9.0. The top reviewer of Oracle Database In-Memory writes "Easy to deploy and has good performance". On the other hand, the top reviewer of Vertica writes "Superior performance in speed and resilience makes this a very good warehousing solution". Oracle Database In-Memory is most compared with SAP HANA, SQL Server, Progress OpenEdge RDBMS, Exasol and MySQL, whereas Vertica is most compared with Snowflake, Apache Hadoop, Teradata, Amazon Redshift and Citus Data.
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