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Actian ParAccel vs OpenText Analytics Database (Vertica) comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 28, 2025

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Actian ParAccel
Ranking in Data Warehouse
27th
Average Rating
7.0
Reviews Sentiment
6.6
Number of Reviews
1
Ranking in other categories
No ranking in other categories
OpenText Analytics Database...
Ranking in Data Warehouse
6th
Average Rating
8.2
Reviews Sentiment
6.9
Number of Reviews
90
Ranking in other categories
Cloud Data Warehouse (11th)
 

Mindshare comparison

As of March 2026, in the Data Warehouse category, the mindshare of Actian ParAccel is 1.5%, up from 0.2% compared to the previous year. The mindshare of OpenText Analytics Database (Vertica) is 5.0%, down from 8.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Warehouse Mindshare Distribution
ProductMindshare (%)
OpenText Analytics Database (Vertica)5.0%
Actian ParAccel1.5%
Other93.5%
Data Warehouse
 

Featured Reviews

it_user263409 - PeerSpot reviewer
Business Intelligence Consultant at a computer software company with 501-1,000 employees
​The stability needs work and the leader node should be removed, but we can deliver results faster.
The speed of the application It has delivered results to our customers faster than we were able to previously. The stability needs work. I've used it for two years. It wasn't stable. There are issues. No issues yet. Customer Service: It's good. Technical Support: It's good. SQL Server…
JN
consultant at tcs
Data warehousing has transformed reporting performance and now delivers near real-time insights
OpenText Analytics Database (Vertica) is a very powerful analytic database, but like any platform, there are areas where it can improve to make daily work even smoother. Better cloud-native experience is one area for improvement. OpenText Analytics Database (Vertica) was originally designed as an on-premises analytic database and later moved to cloud. Improvement opportunities include more seamless cloud-native features such as auto-scaling, serverless options, and easier cluster management. Competitors such as Snowflake and BigQuery provide more fully managed experiences. Easier UI is another area for improvement. Most administration is currently done by SQL and command line tools. An improvement opportunity would be a more modern web UI for monitoring, workload management, and troubleshooting. Faster ecosystem and community growth is needed. In short, OpenText Analytics Database (Vertica) could improve in areas such as cloud-native capability, modern UI for administration, stronger real-time streaming integration, and growing its ecosystem and community. These enhancements would make it easier to manage and adopt compared to newer cloud-first analytic platforms. From a day-to-day operational perspective, there are a few areas where OpenText Analytics Database (Vertica) could improve to make our work smoother. Smarter automatic projection management is needed with more intelligence, auto projection creation, automatic optimization, and reduced manual testing with better workload management. Right now, monitoring queries often requires system tables and manual analysis. Troubleshooting slow queries takes time. A modern real-time dashboard showing query bottlenecks and resource users would enable quick detection. The impact could be faster issue resolution and less time spent debugging performance. Storage native interaction with modern data tools is also important. In short, from a day-to-day perspective, improvements in automatic projection optimization, better workload monitoring dashboard, easier schema evolution, and stronger modern tool integration would significantly reduce manual tuning effort and improve developer productivity. While OpenText Analytics Database (Vertica) is very powerful, these enhancements would make it more efficient for the analytics team.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"It has delivered results to our customers faster than we were able to previously."
"We use Vertica as our primary data warehouse."
"We are also opening new areas of business and potential new revenue streams using Vertica's analytic functions, most notably geospatial, where we are able to run billions of comparisons of lat/long point locations against polygon and point/radius locations in seconds. ​"
"Customer Service: Excellent! Technical Support: Excellent!"
"We were able to implement new algorithms without having to move data out of Vertica into a compute cluster."
"I enjoy the cybersecurity and backup features."
"I would name HPE Vertica as the most mature columnar database with a best of class data storage and query engine."
"Bulk loads, batch loads, and micro-batch loads have made it possible for our organization to process near real-time ingestions and faster analytics."
"The most valuable feature is Vertica's performance and the ease of using the database."
 

Cons

"The stability needs work."
"I would love to see direct connections to other DMSs."
"Pricing could be more competitive."
"The support was slow and didn’t provide a solution in most cases."
"They could improve the integration and some of the features in the cloud version."
"Even with some optimization (adding projections for merge joins and grouped by pipelined), it's still taking a longer time than a Spark job in some cases."
"Limitations in group by projections is where I would like to see an improvement."
"Metadata for database files scale okay, but metadata related to tables/columns/sequences must be stored on all nodes."
"vbr.py needs to be improve to support diff no of nodes source to target."
 

Pricing and Cost Advice

Information not available
"The pricing and licensing depend on the size of your environment and the zone where you want to implement."
"I am aware that we have licensed it, but I have no knowledge of its cost."
"The price is reasonable. We use a pay per license model. Firstly, you need to buy a license. After that, you mainly pay the annual support fee of around 20% or 25%. I think their prices are quite reasonable."
"The pricing depends on the license model because there are several. It depends on the client, but it's cheaper than other solutions. I think it's cheap for all the functionality and robustness. It's not very expensive to deploy."
"The solution is free and we pay for the storage."
"It's an expensive product"
"Vertica has a perpetual license, but they are currently trying to convert all those licenses to subscription-based licenses on a yearly basis."
"The solution is relatively cost-effective."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
16%
Computer Software Company
15%
Manufacturing Company
8%
Marketing Services Firm
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business29
Midsize Enterprise23
Large Enterprise42
 

Questions from the Community

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What do you like most about Vertica?
Vertica is easy to use and provides really high performance, stability, and scalability.
What is your experience regarding pricing and costs for Vertica?
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 needs improvement with Vertica?
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 featu...
 

Also Known As

ParAccel
Micro Focus Vertica, HPE Vertica, HPE Vertica on Demand
 

Overview

 

Sample Customers

Amazon, The Royal Bank of Scotland, OfficeMax, MicroStrategy
Cerner, Game Show Network Game, Guess by Marciano, Supercell, Etsy, Nascar, Empirix, adMarketplace, and Cardlytics.
Find out what your peers are saying about Snowflake Computing, Oracle, Teradata and others in Data Warehouse. Updated: February 2026.
884,873 professionals have used our research since 2012.