Sold by: OpenText
Deployed on AWS
OpenText Analytics Database (Vertica) is a blazingly fast advanced SQL analytics database, maximizing cloud economics for mission-critical big data analytical initiatives. OpenText Analytics Database (Vertica) for AWS is packed with best-in-class features -
4.3
Overview
OpenText Analytics Database (Vertica) is a blazingly fast advanced SQL analytics database, maximizing cloud economics for mission-critical big data analytical initiatives. OpenText Analytics Database (Vertica) for AWS is packed with best-in-class features - built-in machine learning, predictive analytics, elastic scalability, fine-tuning capabilities, integrated BI/reporting, data ingestion and more for a just-in time deployments on AWS, without breaking your budget.
Vertica for AWS offers the flexibility to start small and grow as your business grows as well as access to advanced analytics functionality that no other analytic platform offers. Vertica seamlessly integrates within existing data pipeline consisting of Kafka, Spark, and/or Hadoop for a comprehensive data warehouse solution.
Eon Mode, the separation of compute from storage, provides rapid elasticity for changing workloads and subclusters for workload isolation.
Vertica by the Hour includes full support for production deployments. Hourly pricing is cost-effective for workloads that come and go.
Vertica also runs on-premises, on industry-standard hardware as well as on Hadoop nodes. Visit Vertica.com to learn how Vertica is changing the way companies across every industry operate, grow, and stay competitive.
Highlights
- Gain insights into your data in real time with blazingly fast SQL analytics across Exabytes of data
- Maximize cloud economics with Eon Mode by scaling your cluster size to meet your variable workload demands and/or scale your S3 storage without limits (almost)
- Leverage Machine Learning and Predictive Analytics features to help you pre-process data, discover insights and predict outcomes
Details
Sold by
Categories
Delivery method
Delivery option
Deploy Management Console into new VPC
Deploy Management Console into existing VPC
Management Console with 3-node Eon Cluster
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Rhel 9
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Buyer guide
Gain valuable insights from real users who purchased this product, powered by PeerSpot.
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
r4.4xlarge Recommended | $2.00 |
d2.4xlarge | $2.00 |
i3.8xlarge | $4.00 |
m5d.12xlarge | $6.00 |
i2.8xlarge | $4.00 |
m5.8xlarge | $4.00 |
i4i.24xlarge | $12.00 |
r4.16xlarge | $8.00 |
r5n.4xlarge | $2.00 |
i4i.12xlarge | $6.00 |
Vendor refund policy
no refunds, cancel at any time
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Additional details
Usage instructions
Start with "Continue to Subscribe" then go to the "Manual Launch" tab. Select one of the "Management Console..." deployment options. Complete the CloudFormation template then find your new stack in the CloudFormation dashboard. Monitor progress in the CloudFormation stacks dashboard.
Resources
Support
Vendor support
Entitlement to Enterprise Support requires at least 180 hours of usage per month. Register for Enterprise Support here:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Yellowbrick Data
By Cloudera
Top
25
In Databases & Caching, Business Intelligence, Big Data
Top
50
In Data Warehouses
Top
10
In Data Analysis
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Advanced SQL Analytics Engine
Blazingly fast SQL analytics database capable of processing exabytes of data with real-time insights
Machine Learning and Predictive Analytics
Built-in machine learning and predictive analytics features for data pre-processing, insight discovery, and outcome prediction
Elastic Scalability with Eon Mode
Separation of compute from storage architecture enabling rapid elasticity for changing workloads with independent scaling of cluster size and S3 storage
Workload Isolation
Subclusters functionality for isolating and managing different workloads independently
Data Pipeline Integration
Seamless integration with Kafka, Spark, and Hadoop for comprehensive data warehouse solutions
Database Engine Architecture
Cloud-native, scale-out SQL database with Direct Data Accelerator technology for optimized query processing and efficiency
Elastic Cluster Infrastructure
Fully elastic clusters with separate storage and compute architecture enabling complex query execution at any scale with sub-second response times
Deployment Flexibility
Hybrid deployment capability supporting public cloud VPC, private cloud, on-premises, and edge deployments with identical data warehouse functionality across environments
PostgreSQL Compatibility
SQL database compatible with PostgreSQL, enabling developers to use familiar tools and interfaces for application development and integration
Built-in Disaster Recovery
Integrated replication functionality for disaster recovery and cross-geographic data sharing across hybrid deployments
Workload Auto-scaling
Intelligently autoscales workloads up and down across hybrid and public cloud environments for optimized cloud infrastructure utilization.
Multi-function Analytics Platform
Provides integrated data warehouse, machine learning, and custom analytics capabilities with unified analytic functions to eliminate data silos.
Shared Data Experience (SDX)
Implements security and governance policies that are set once and applied consistently across all data and workloads, with portability across supported infrastructures.
Data Lifecycle Management
Manages complete data lifecycle functions including ingestion, transformation, querying, optimization, and predictive analytics across multiple cloud environments.
Unified Security and Governance
Ensures all workloads share common security, governance, and metadata with capabilities for data discovery, curation, and self-service access controls.
Standard contract
No
No
Customer reviews
Vasant Pramod Diwane
Data analytics has accelerated decisioning and now delivers faster insights for telecom use cases
Reviewed on Feb 17, 2026
Review provided by PeerSpot
What is our primary use case?
My main use case for OpenText Analytics Database (Vertica) is mostly data sourcing and analyzing the data in OpenText Analytics Database (Vertica) for a telecom client.
For example, whatever telecom data we have stored in the OpenText Analytics Database (Vertica) database, we have to analyze as per our use case. For offer decisioning-related use cases, we had to gather some data from OpenText Analytics Database (Vertica) regarding customer analytics, usage analytics, or the different plans. We used to analyze that data based on the requirement.
We also use OpenText Analytics Database (Vertica) to generate SQL queries, and we analyze the data there.
What is most valuable?
The best features that OpenText Analytics Database (Vertica) offers are mainly the parallel processing, ETL capabilities, and the multi-cloud features which are very handy to use.
The parallel processing and ETL capabilities have helped us significantly. GCP cloud is a very useful feature to get the data from Azure or GCP. The ETL capabilities include querying the databases from different systems. Also, parallel processing has helped us generate the data source analysis earlier.
OpenText Analytics Database (Vertica) has impacted our organization positively because earlier we used to use Teradata , and it was very slow, creating issues with processing as well. OpenText Analytics Database (Vertica) has helped us a lot in terms of processing capabilities and the speed of querying.
What needs improvement?
OpenText Analytics Database (Vertica) can be improved by adding some more features in analytics.
OpenText Analytics Database (Vertica) does not have a cloud-based UI that Snowflake has, which features a very good comprehensive GUI for querying and analyzing data. This is something OpenText Analytics Database (Vertica) can improve. Otherwise, the database is very strong.
For how long have I used the solution?
I have been using OpenText Analytics Database (Vertica) for almost five to six years.
What do I think about the stability of the solution?
OpenText Analytics Database (Vertica) is stable, and there have been no issues till now.
What do I think about the scalability of the solution?
OpenText Analytics Database (Vertica) has very good scalability. Whenever we need to scale the database, the database configuration is easy to modify.
How are customer service and support?
Regarding customer support, I actually never had to contact customer support, so I do not see any issues there.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
We previously used a different solution and also used Snowflake database in place. While Snowflake is a better option, we are pretty much familiar with OpenText Analytics Database (Vertica), so we use OpenText Analytics Database (Vertica) more than Snowflake.
How was the initial setup?
Before choosing OpenText Analytics Database (Vertica), I did not evaluate other options. We directly started with OpenText Analytics Database (Vertica) only.
What about the implementation team?
My company has no business relationship with this vendor other than being a customer of OpenText Analytics Database (Vertica).
What was our ROI?
I have seen a return on investment from using OpenText Analytics Database (Vertica), specifically in terms of time saved. The time we used to take with our earlier databases has reduced to one-tenth of what was there earlier, which is a positive outcome that can be converted to financial metrics in terms of return on investment.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup cost, and licensing is limited because the organization handled the licensing and pricing as well as the cost setup.
What other advice do I have?
My advice for others looking into using OpenText Analytics Database (Vertica) is to proceed with it. We found no issues, and it can definitely help organizations in database analytics. I believe we covered everything regarding OpenText Analytics Database (Vertica). I gave this review a rating of ten.
Prassingh Singh
Reporting has become faster and more cost-effective but data cleanup and writes still need work
Reviewed on Feb 13, 2026
Review from a verified AWS customer
What is our primary use case?
I used OpenText Analytics Database (Vertica) in my previous company for three years. The main use case was reporting. I worked for a fintech company that had a massive amount of data, and we had this use case for reporting, so we used OpenText Analytics Database (Vertica) .
A specific example of how I used OpenText Analytics Database (Vertica) for reporting in my fintech company is that we had an ETL pipeline in which we processed the data and removed all PII and PCI data as part of the pipeline, then dumped all the data in S3 . We used OpenText Analytics Database (Vertica) in Eon Mode, so the data was in S3 and we had compute nodes in AWS . We had a license from AWS Marketplace and were using the enterprise version. The consumers were the data analytics team, and our job was to make all the data available in OpenText Analytics Database (Vertica). In some use cases, we were creating projections to make queries faster because we had predictable reports to generate.
OpenText Analytics Database (Vertica) does not support good write, update, or insert queries, but from a read perspective, which was ideal for reporting, it has a strong use case.
What is most valuable?
The best features OpenText Analytics Database (Vertica) offers are that it is parallel, with Massively Parallel Processing (MPP), and it is a columnar database. It works in append-only mode, which is ideal for analytics and read queries. Additionally, I can use it in Eon Mode in which I can store the data in cheaper storage such as Amazon S3 and have different compute nodes. That helped me save costs. Beyond that, it is massively scalable, which was quite useful for our fintech use case. The read speed was excellent, and the data compression is good.
OpenText Analytics Database (Vertica) has positively impacted our organization by helping us reduce storage costs and improve reporting efficiency in our fintech company. The analytics team could run reports much faster, and we saved significant costs on compute and storage, especially with Eon mode and compression.
What needs improvement?
OpenText Analytics Database (Vertica) does not support hard delete, and they perform soft delete, which is the case with all columnar databases. If they could support or periodically clean up the data so that the data volume does not grow as much, that would be one suggestion. Beyond that, I think it is solid overall. Projections could be made more dynamic, and if they could find a faster way to update, insert, and delete data, that would also be helpful.
For how long have I used the solution?
I have been working in my current field for seven years.
What do I think about the stability of the solution?
OpenText Analytics Database (Vertica) is very stable. The migrations were smooth, we did not lose any data, and we only had to pause the pipeline.
What do I think about the scalability of the solution?
The scalability of OpenText Analytics Database (Vertica) is very strong. Being a columnar database, it scales quite well.
How are customer service and support?
Customer support for OpenText Analytics Database (Vertica) is excellent. We had multiple use cases because we moved from proof of concept to enterprise mode. We had to scale up OpenText Analytics Database (Vertica) and make some changes. Throughout this process, customer support was outstanding, and we had a person actively supporting us from the OpenText Analytics Database (Vertica) team for our use case.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
I did not use any other solution before using OpenText Analytics Database (Vertica). Initially, this was the use case we had. We analyzed all the present tools and started with OpenText Analytics Database (Vertica) itself.
How was the initial setup?
For migration or upgrade to OpenText Analytics Database (Vertica), we were using three nodes and then shifted to five nodes based on the load. The migration was very smooth. We can take a backup in S3 and update it, so the compute part was stateless. The upgrade became very straightforward. Regarding metrics, the latency of the reports was very low, and we had many consumers because it was a B2B and B2C fintech company.
What was our ROI?
I have seen a return on investment with OpenText Analytics Database (Vertica). I saved a lot of money because the storage was on a cheaper alternative and was not directly on OpenText Analytics Database (Vertica), but on S3. I also saved time because setting up OpenText Analytics Database (Vertica) was a one-time effort, and the ETL pipeline was configured. We had different use cases for different reports, some of which were daily, some monthly, and some hourly. There I saved a lot of time, and the entire pipeline became automated. We benefited in terms of money, time, and employee resources.
What's my experience with pricing, setup cost, and licensing?
The pricing for OpenText Analytics Database (Vertica) is somewhat on the higher side for the license. Beyond that, the support from the OpenText Analytics Database (Vertica) team was very streamlined and excellent. We upgraded the OpenText Analytics Database (Vertica) cluster multiple times, and we always had a person supporting us from the OpenText Analytics Database (Vertica) team, so my experience was really strong.
Which other solutions did I evaluate?
Before choosing OpenText Analytics Database (Vertica), we evaluated other options such as Snowflake and others, but OpenText Analytics Database (Vertica) was the best for our use case because we wanted our data to be in our system. The support for S3 was also strong, and we had some expertise in OpenText Analytics Database (Vertica), as some team members were experts in it, so we started using it. We analyzed BigQuery and Snowflake .
What other advice do I have?
My advice to others looking into using OpenText Analytics Database (Vertica) is that if you want a read-heavy database and do not want it to be transactional in terms of write, delete, or insert operations, and if your use case is reporting, then OpenText Analytics Database (Vertica) is excellent for that use case. However, it is not ideal if you want to use it as a regular database, such as in e-commerce. If you want to use it in e-commerce, use it primarily for reporting. I would rate this product a 7 out of 10.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
T Venkatesh
Processes query faster through multiple systems simultaneously, but it could support different data types
Reviewed on Aug 15, 2024
Review provided by PeerSpot
What is our primary use case?
We use the solution for various tasks, including preparing data marts and generating offers. It helps extract data based on rules from the policy team and provides insights to enhance business operations. We also analyze transactions to target customers and improve business performance.
How has it helped my organization?
The platform has improved our organization by providing faster data retrieval and cost-effective solutions compared to other databases like Oracle. The columnar storage format allows for quicker data processing and reduced costs.
What is most valuable?
The most valuable feature is the speed of data retrieval. Compared to Sybase, Vertica processes queries faster by executing them across multiple systems simultaneously.
What needs improvement?
The product could improve by adding support for a wider variety of data types and enhancing features to better compete with other databases.
For how long have I used the solution?
I have been working with Vertica for five years.
What do I think about the stability of the solution?
The product is stable. I rate the stability an eight.
What do I think about the scalability of the solution?
The product is scalable. However, there is room for improvement. I rate the scalability a seven.
How are customer service and support?
In my organization, I frequently reach out to the support team for assistance. They provide guidance when we encounter issues related to Vertica, such as problems with heavy queries or permission issues.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
I previously used Sybase but switched to Vertica for its superior speed and cost-effectiveness.
How was the initial setup?
The initial setup was straightforward. I rate the process a seven.
What about the implementation team?
We implemented the solution with the help of our in-house team.
What's my experience with pricing, setup cost, and licensing?
The solution is relatively cost-effective. Pricing and licensing are reasonable compared to other solutions.
What other advice do I have?
I rate Vertica a seven out of ten.
ERICK RAMIREZ
Allows for a large amount of data to be stored with minimal physical space
Reviewed on Jun 03, 2024
Review provided by PeerSpot
What is our primary use case?
We use the solution for the warehouse. We implement machine-learning solutions such as clustering or classification models.
How has it helped my organization?
We can implement advanced solutions with very interesting capabilities to review whether the customer returns the tool and the licensing cost.
What is most valuable?
Vertica uses advanced Azure technologies to compress raw data using indexing, allowing a large amount of data to be stored with minimal physical space. Advanced algorithms are employed in data compression.
What needs improvement?
Pricing could be more competitive.
For how long have I used the solution?
I have been using Vertica for three years. We are using the V23 of the solution.
What do I think about the stability of the solution?
I rate the solution’s stability an eight out of ten.
What do I think about the scalability of the solution?
100-200 users are using this solution.
I rate the solution’s scalability a nine out of ten.
How are customer service and support?
Support is good.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial setup is straightforward. The deployment process includes creating a solar package to deploy this package on-premise or in a cloud environment. The solar package has all the configurations and components we need to implement as part of customer solutions. There are various software components requiring a specific configuration. So, we package this solar component and deploy it in the customer environment.
I rate the initial setup an eight out of ten, where one is difficult and ten is easy.
What's my experience with pricing, setup cost, and licensing?
I rate the product’s pricing a seven out of ten, where one is cheap and ten is expensive.
What other advice do I have?
You can implement a cluster of servers, and we should guarantee high availability in a disaster recovery scenario. You can use Vertica in a production environment with distributed workflows and workloads. Vertica is available and has parallel processing and other capabilities.
You can implement a cluster of servers to guarantee high availability and massive parallel processing. It's a very sophisticated solution.
Vertica can be used to implement machine learning models such as classification, clustering, and aggregation models to support various use cases depending on customer needs. We have already implemented some machine learning models to detect anomalies. Some employees have distinct patterns in their working behaviors.
It is another feature-intelligent solution from OpenTex. It can implement or process structural data such as images, videos, text documents, and semi-structured data.
I recommend using this kind of solution because you can index your data and use a balancing algorithm to manage and retrieve data efficiently. Customers don't need a very large infrastructure to implement this type of solution. You can use it to implement advanced machine learning models, classification, and clustering. It also supports advanced artificial intelligence solutions.
Overall, I rate the solution a nine out of ten.
Marko Stajcer
Used for different business analytics, but its native cloud support could be improved
Reviewed on Apr 24, 2024
Review provided by PeerSpot
What is our primary use case?
We use Vertica for different business analytics, like IPTV and viewership analytics.
What is most valuable?
Vertica is easy to use and provides really high performance, stability, and scalability.
What needs improvement?
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.
For how long have I used the solution?
I have been using Vertica for more than five years.
What do I think about the stability of the solution?
Vertica provides good stability.
I rate the solution a nine out of ten for stability.
What do I think about the scalability of the solution?
There are different options for scaling the solution through physical or virtual nodes or Kubernetes containers. Scaling is easy, but once we add more nodes, some actions have to be performed on the database. More than 200 users are using the solution in our organization.
How are customer service and support?
The solution's technical support is great.
Which solution did I use previously and why did I switch?
We usually work with other vendors like Netezza and Oracle, some open-source databases, big data systems, and cloud-native tools like Azure, GCP, or BigQuery. We decided to go with Vertica because we had everything on-premises, and we preferred to have a database on-premise.
What about the implementation team?
The solution's deployment takes a week or even more. We implemented the solution with the help of its support. The deployment can be done in-house, but expertise is needed.
What's my experience with pricing, setup cost, and licensing?
Vertica has a perpetual license, but they are currently trying to convert all those licenses to subscription-based licenses on a yearly basis.
What other advice do I have?
Vertica improved real-time data ingestion from sources and reporting to business users. The solution's native functions were usable for some simple use cases. However, developers prefer something else, like Python, for some complex projects. Vertica has features very similar to those of other databases like Netezza or Snowflake. The solution provides great value for its price.
Vertica's integration with third-party systems is very easy because it supports standard integrations like ODBC and JDBC. The solution's price-performance ratio is great, and it is used as a group data warehouse.
Overall, I rate the solution a seven out of ten.