Vertica's native cloud support could be improved, and its installation could be made easier. It's possible to deploy the solution on different hyperscalers, but it's not an easy process. Vertica is an MPP database, and sometimes, some nodes may fail. It could have a better warning system to let us know if we use all the storage space.
In my opinion, nothing needs improvement in the solution as it is a great product. The documentation of Vertica is an area with shortcomings where improvements are required. Vertica needs to increase its sustainability in the future.
We faced some challenges when trying to use the temporary tables feature. First, we installed Vertica on the AWS cloud and tried to read the data from Bitbucket to Vertica. We were able to read the data, but when we were trying to transform the data and load it into Vertica physical tables, we experienced performance issues. Regarding additional features, we are unsure if Vertica has updated its features, but we've experienced difficulty in integrating with third-party tools. Clients use multiple technologies and value that integration. Sharing data with third parties should also be improved.
Arquitecto Delivery at a tech services company with 501-1,000 employees
Real User
2022-01-05T07:04:57Z
Jan 5, 2022
I think they need an easy client so that you can write queries easily, but it's not necessarily a weak point. I think some users would need them. In other versions of Vertica, there is a lack of integrations in some machine learning models. But today, the latest version has it all. I don't think they need too much improvement in that area. For me, it's great considering the use case I work with.
Director - Big Data, IoT and Analytics at a tech services company with 11-50 employees
Reseller
2021-10-29T18:01:02Z
Oct 29, 2021
Vertica offers a platform-as-a-service version, but their software-as-a-service solution is only available on AWS. They need to get a SaaS version on Azure and GCP as fast as possible. I know that's in their roadmap, but I can't wait.
Creator and Manager of Intelligent Water Loss Management Models at Qintess
Real User
2020-12-18T00:01:00Z
Dec 18, 2020
The product could be less expensive and could benefit from a better marketing strategy. In a future release, I would like to have one application to help create intelligent models.
Senior Manager, Systems and Network Engineering at a computer software company with 11-50 employees
Real User
2020-11-27T22:55:00Z
Nov 27, 2020
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.
Director - Big Data, IoT and Analytics at a tech services company with 11-50 employees
Reseller
2020-09-13T07:02:21Z
Sep 13, 2020
Some of our small to medium-sized customers would like to see containerization and flexibility from the deployment standpoint. They don't currently offer this as a platform as a service. Snowflake is offering this capability. They're available in the cloud. They're available on every cloud, but they're not available as a managed platform as a service offering.
Senior Database Architect at a real estate/law firm with 501-1,000 employees
Real User
2020-08-23T08:17:24Z
Aug 23, 2020
Every product has room for improvement and Vertica is no different in that way. I think the geospatial is quick, but could be quicker. Continue adding machine learning code to run directly on the database. I also think the ability to perhaps directly link to other databases rather than just data sources and files would be another one. For new functionality, I think the possibility of adding triggers or programmatic pieces of code might be helpful, depending on the data coming in. It is difficult to say if it would work or cause more issues than it solves. W
Sr. Business Intelligence Analyst / Developer at DXC
Real User
2019-07-09T13:52:00Z
Jul 9, 2019
There is serious performance degradation for large datasets. Fact-to-fact joins on multi-billion record tables perform poorly. Star schema joins also perform poorly if the fact tables reach more than one billion records and the dimension tables reach more than one million records.
You need to know what you are doing to get the most out of Vertica. If you do not utilize the tuning tools like projections, encoding, partitions, and statistics, then performance and scalability will suffer. It would be great if this were a managed service in AWS.
* 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.
* It should provide a GUI interface for data management and tuning. * Monitoring tools need to be lightweight. They should not take up heavy resources of the main server.
Vertica is a deploy-anywhere SQL database created for elasticity, speed, and advanced analytics. Vertica enables today’s busy teams to modernize their data warehouses, democratize data and analytics to enable increased access, and deploy analytics in a hybrid cloud environment. Additionally, Vertica merges how companies power their analytics by providing a scalable, open, and elastic database with numerous intuitive features.
In today’s marketplace, organizations are experiencing continued...
The product could improve by adding support for a wider variety of data types and enhancing features to better compete with other databases.
Pricing could be more competitive.
Vertica's native cloud support could be improved, and its installation could be made easier. It's possible to deploy the solution on different hyperscalers, but it's not an easy process. Vertica is an MPP database, and sometimes, some nodes may fail. It could have a better warning system to let us know if we use all the storage space.
In my opinion, nothing needs improvement in the solution as it is a great product. The documentation of Vertica is an area with shortcomings where improvements are required. Vertica needs to increase its sustainability in the future.
The integration with AI has room for improvement. There is room for better machine learning.
The biggest problem is the cost of cloud deployment.
Vertica can improve automation and documentation.
We faced some challenges when trying to use the temporary tables feature. First, we installed Vertica on the AWS cloud and tried to read the data from Bitbucket to Vertica. We were able to read the data, but when we were trying to transform the data and load it into Vertica physical tables, we experienced performance issues. Regarding additional features, we are unsure if Vertica has updated its features, but we've experienced difficulty in integrating with third-party tools. Clients use multiple technologies and value that integration. Sharing data with third parties should also be improved.
The integration of this solution with ODI could be improved.
I think they need an easy client so that you can write queries easily, but it's not necessarily a weak point. I think some users would need them. In other versions of Vertica, there is a lack of integrations in some machine learning models. But today, the latest version has it all. I don't think they need too much improvement in that area. For me, it's great considering the use case I work with.
Vertica offers a platform-as-a-service version, but their software-as-a-service solution is only available on AWS. They need to get a SaaS version on Azure and GCP as fast as possible. I know that's in their roadmap, but I can't wait.
They could improve the integration and some of the features in the cloud version.
I have found that coding support could be simplified.
The product could be less expensive and could benefit from a better marketing strategy. In a future release, I would like to have one application to help create intelligent models.
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.
Some of our small to medium-sized customers would like to see containerization and flexibility from the deployment standpoint. They don't currently offer this as a platform as a service. Snowflake is offering this capability. They're available in the cloud. They're available on every cloud, but they're not available as a managed platform as a service offering.
Every product has room for improvement and Vertica is no different in that way. I think the geospatial is quick, but could be quicker. Continue adding machine learning code to run directly on the database. I also think the ability to perhaps directly link to other databases rather than just data sources and files would be another one. For new functionality, I think the possibility of adding triggers or programmatic pieces of code might be helpful, depending on the data coming in. It is difficult to say if it would work or cause more issues than it solves. W
When it is about to reach the maximum storage capacity, it becomes slow.
There is serious performance degradation for large datasets. Fact-to-fact joins on multi-billion record tables perform poorly. Star schema joins also perform poorly if the fact tables reach more than one billion records and the dimension tables reach more than one million records.
It needs integration with multiple clouds.
You need to know what you are doing to get the most out of Vertica. If you do not utilize the tuning tools like projections, encoding, partitions, and statistics, then performance and scalability will suffer. It would be great if this were a managed service in AWS.
* 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.
* It should provide a GUI interface for data management and tuning. * Monitoring tools need to be lightweight. They should not take up heavy resources of the main server.