We performed a comparison between Microsoft Parallel Data Warehouse, Oracle Exadata, and Vertica based on real PeerSpot user reviews.
Find out what your peers are saying about Snowflake Computing, Oracle, Teradata and others in Data Warehouse."We can store the data in a data lake for a very low cost."
"Tools like the BI and SAS are excellent."
"It is a very stable database."
"The most valuable feature for me is querying."
"The solution's integration is good."
"The UI is very simple and functional for my clients, most of the clients that use the solution are not experts."
"Microsoft Parallel Data Warehouse provides good firewall processing in terms of response time."
"Data collection and reporting are valuable features of the solution."
"It is the best solution for OLTP and data warehousing."
"Oracle Exadata's performance is one of its best features. We very satisfied with it."
"Compression is a great feature, where one can really save a lot of storage."
"Before using this machine, we took no less than two days to run a report. Now, we can do it within five hours. So, there is a lot of improvement."
"Exadata with the In-Memory option is several levels about SAP HANA."
"The most valuable feature is that you have the same familiar environment of an Oracle database but with the additional performance you get from this architecture."
"Complete management occurs from one single address instead of different servers."
"We have used this solution for a long period of time so it has become easy for us to query any kind of data from Oracle Exadata which has been valuable."
"Allows us to take volumes and process them at a very high speed."
"It maximize cloud economics for mission-critical big data analytical initiatives."
"Vertica is a columnar database, this support our developments in analytics, advanced analytics, and ETL process with large sets of data."
"Vertica's most outstanding features are the compression rates achieved and the speed of access of high volume data."
"The solution has great capabilities. The tool that instructs the internal database forward is easy to use and is very powerful."
"Bulk loads, batch loads, and micro-batch loads have made it possible for our organization to process near real-time ingestions and faster analytics."
"Speed and resiliency are probably the best parts of this product."
"The feature I like best is performance. We use Red Tool and Red Job for the data warehouse and reporting. It's perfect. Performance is good, and it can return ad hoc queries very quickly. Of course, it's a cluster, so it's easy to scale."
"Some compatibility issues occur during deployment, so we need to build the product from scratch for some features."
"The query is slow if we don't optimize it."
"The solution is expensive and has room for improvement."
"I would like the tool to support different operating systems."
"It needs more compatibility with common BI tools."
"The only issue with the product is that the process is very slow when we have a huge amount of data."
"It could be made more user-friendly for business users which would increase the user base."
"We find the cost of the solution to be a little high."
"The scalability can be improved as it is not a parallel execution."
"The management monitoring tools are quite important and an area that needs some improvement."
"There is room for improvement with the handling of the Temp IO, which is often used for JOIN statements."
"The improvement could be made on the hardware level as the habit in the industry is to go better and faster and larger with every iteration."
"The solution lacks a visualized console."
"License or upgrade management can be difficult and time consuming because it requires login to a separate console."
"The initial setup process is very difficult and extremely complex."
"It would be good if Exadata made some new features available regarding data retrieval and speed capacity functions."
"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."
"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."
"Metadata for database files scale okay, but metadata related to tables/columns/sequences must be stored on all nodes."
"I have found that coding support could be simplified."
"Very bad support, I would rate it two out of 10."
"In a future release, we would like to have artificial intelligence capabilities like neural networks. Customers are demanding this type of analytics."
"One feature, which has really benefited us, is the scalability offered by Vertica as it has enabled Pythian's clients to manage data with agility."
"They could improve on customer service."
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