What’s New in OpenText Analytics Database

October 2025

OpenText Analytics Database CE 25.4: Accelerating AI and analytics at scale

Power your data with AI-ready performance, intelligent tuning, and modern cloud-native efficiency

AI and analytics have become the engines of competitive advantage—but legacy databases, manual tuning, and data sprawl across SaaS warehouses still slow progress.

The OpenText™ Analytics Database (Vertica) CE 25.4 release is designed for enterprises that demand speed, scale, and simplicity. This release delivers AI-ready analytics, self-optimizing performance, and seamless modernization paths to high-performance data infrastructure. 

Whether you’re modernizing from older versions or preparing for AI-driven workloads, CE 25.4 ensures your data platform keeps pace with your business ambitions. 

Why upgrade to OpenText Analytics Database CE 25.4?

Staying on earlier versions (CE 25.3 or older) risks maintaining status quo analytics—slower insights, higher operational overhead, and limited scalability—while competitors accelerate their AI innovation. 

Upgrading to CE 25.4 unlocks a modern, efficient, and intelligent database foundation that enables: 

• Faster, smarter insights – Achieve higher query throughput and lower latency across massive datasets. 
• Automated performance tuning – Leverage built-in auto-tuning to optimize workloads and free engineering time. 
• Accelerated AI productization – Bring models to production faster with in-database VectorOps and high-performance analytics. 
• Unified open data access – Analyze external datasets instantly with Apache Iceberg integration, without duplication or migration. 
• Modernized infrastructure – Deploy flexibly with Kubernetes and compute-only nodes for cloud-native scalability. 

CE 25.4 is not just an update—it’s your bridge to a modern, AI-driven data architecture. 

What’s new in OpenText Analytics Database CE 25.4

1. Accelerated performance and AI readiness 

Faster queries. Smarter computation. Ready for the AI era. 

• Vectorops for AI/ML: Execute high-performance in-database vector operations to accelerate AI and machine learning workloads. 
• Packed VL data optimization: Improve query performance and processing efficiency across massive, complex datasets. 

2. Intelligent auto-tuning 

Make your database smarter so your teams can focus on innovation, not maintenance. 

• Built-in auto-tuning: Automatically optimize performance based on workload characteristics. 
• Reduced manual effort: Eliminate tedious tuning tasks and empower engineers to focus on building value-driven data products. 

3. Modern, cloud-native infrastructure 

Evolve confidently toward hybrid or cloud-native environments. 

• Kubernetes support: Deploy and scale flexibly with full Kubernetes support for containerized, automated management. 
• Compute-only nodes: Separate compute and storage for greater elasticity and cost efficiency. 
• Smooth upgrade path: Modernized architecture ensures upgrades with minimal disruption. 

4. Seamless external data access 

Eliminate data silos and accelerate analytics without duplication. 

• Apache Iceberg integration: Query large-scale external data directly—no migration, no copies. 
• Unified analytics: Gain a single view across internal and external data sources for faster, more informed decisions. 

Building a smarter path to AI and analytics

Upgrading to OpenText Analytics Database (Vertica) CE 25.4 ensures your data environment is ready for what’s next—AI-driven intelligence, massive data growth, and continuous modernization. 

Customers currently on versions CE 25.3 or earlier can access the CE 25.4 Upgrade & Migration Kit to plan their modernization journey, whether moving to EON Mode or Kubernetes for cloud-native scalability. 

Upgrade to CE 25.4 and turn your data into your fastest competitive advantage.

January 2024

OpenText™ Vertica Data Platform is now OpenText™ Analytics Database.

What’s New in OpenText Vertica 24.1

The newest version of OpenText Vertica 24.1 (representing the first quarter of 2024) is all about saving operating costs while boosting value. The star in this release is an extraordinary new capability – workload routing. It makes each job more efficient and performant, decreasing spending and energy usage for each type of work by directing it to ideal hardware automatically. Read on to learn more or request a demo of the OpenText Analytics & AI platform today.

3x to 5x your ROI with workload routing

We estimate you will see an increase in Vertica analytics ROI between 3X and 5X by aligning workloads with ideally provisioned subclusters. If you do more than one thing with Vertica, such as power dashboards, do ad hoc business intelligence (BI) queries, run data ingestion and preparation (ELT) pipelines, train or infer with machine learning (ML) models, etc., you’ll see a significant advantage in this new version.

Current versions of Vertica allow you to assign ideal hardware to each subcluster. For example, large, more memory-rich nodes or instances for ML workloads, many small nodes to feed hundreds of concurrent users on dashboards, etc. The existing capability to spin up and down containers and assign compute resources as needed with Kubernetes also ties into this new feature. To use workload routing, grant certain types of workloads to roles such as ML, for example, and a user with that role sends the work to any designated Vertica node. The work is routed directly to the subcluster designated to have ideal hardware for ML workloads. If necessary, ideal instances for that job can even be spun up with Kubernetes then spun down again automatically when the job is complete.

Advantages of workload routing:

• Automation – no human intervention is needed once it is set up.
• Flexibility – do multiple different kinds of analytics work – ELT, ML/AI, ad hoc BI, drive applications, power dashboards – each on the ideal compute infrastructure for that workload.
• Efficiency – do far more work while spending far less on computing infrastructure, reducing energy usage, carbon footprint, and costs.

This isn’t the only great new feature in OpenText Vertica 24.1, but we’re excited to triple the ROI of our analytics customers!

Two other exciting new features in this release

• Vertica data loader – Get new data in minutes automatically. Import data exactly once in configurable micro-batches as it’s dropped in an S3-compatible staging area. No need to manually execute copy commands or design a script to do that. Data pipelines become first-class citizens, saved in the database like tables or machine learning models.
• API-based control and monitoring – When using Vertica embedded in another application, fully control the database with no dependency on Vertica’s admin tools utility or SSH. Use any software, not just Kubernetes, to programmatically control Vertica from an API with improved feedback and performance. Also, manage infrastructure closer to workload demands to save costs.

There are a ton of other new features. If you want to learn more, check out the latest Vertica documentation.  

July 2023

What’s New in OpenText Vertica 23.3

This release contains several months of work and a lot of changes. The first change you’ll notice is the versioning system. The last Vertica release was 12.0.4, so you might have expected this to be version 13. However, OpenText releases are timed to one per quarter every year, and they’re numbered according to the year and quarter — hence this is OpenText™ Vertica™ release is 23.3, re[resemtomg the 3rd quarter of 2023. You’ll also notice Management Console and other visual aspects of Vertica have changed color and logo to reflect our new company and brand. 

Beyond the cosmetics, and even beyond all the improvements you expect in performance, security, and the rest provided in every release of Vertica, major new features now allow you to: 

• Re-shard your Vertica database whenever you need to as data and workloads change.  
• Save snapshots of the database at moments in time that you can revert to as needed, without overburdening your storage budget with multiple copies of the same data.  
• Automate routing of workloads to the node or sub-cluster that makes the most sense for that type of work.  
• Start doing machine learning (ML) workflows with Vertica easily with new VerticaPyLab with all dependencies, examples, and lessons in a single installation with an easy-to-use JupyterLab interface. 

Most notably, with the addition of read and analysis capabilities on external data using Apache Iceberg as the semantic layer, Vertica is now a fully functional data lakehouse. In past versions, Vertica unified business intelligence, machine learning, and other types of advanced analytics like geospatial, event pattern, and time series data analysis into a single point of contact for any analytics. Vertica also gave you the ability to analyze any data, from structured data in our own ROS format, to semi-structured and complex data in external data lake formats like Parquet, JSON, and ORC. 

OpenText Vertica data lakehouse with Apache Iceberg Integration.

Analyzing this data with OpenText Vertica, through the Apache Iceberg metadata layer, gives you the advantage of ACID compliance and rapid findability of that data in the lake. Vertica lets you analyze even complex data through Iceberg quickly, even if another application altered it since the last analysis, even adding or removing columns or changing data types. Vertica’s focus on performance at scale has provided several ways to optimize queries on data lake data. Each release will bring that performance closer and closer to the equivalent blazing speed you expect from querying internal Vertica ROS data.  

Vertica’s smarter data lakehouse removes the limits from analytics 

It lets you analyze your data lake at the speed and concurrency you’re accustomed to in a data warehouse. Here are some things you can now do with OpenText Vertica 23.3: 

Start using Vertica machine learning easily with new VerticaPyLab

New fast install of VerticaPy with all dependencies at once, and an easy JupyterLab interface for choosing applications, examples, data science lessons, etc. 

Authenticate new users with just-in-time provisioning via OAuth2.0

The organization’s OAuth2 identity verification will add a new Vertica user with specified role(s) on the fly saving dbadmins a great deal of time. When a person logs in to Vertica via their preconfigured SSO OAuth token, there’s no need to create user accounts or grant roles manually.  Also, OAuth users who have not used Vertica in a while are automatically removed. 

Use less memory and reduce resource queues with multi-party query plans

OpenText Vertica now breaks long-running queries into parts and allocates only the memory and compute resources needed to execute the largest part of a multi-part plan. Any unused resources within the allocated block are used to optimize the query further, so the query executes faster overall. 

Start large clusters faster using HTTPS instead of SSH

Use thin Golang clients, Cluster Operations Library (vclusterops) and vcluster.exe, which decouple the Kubernetes operator from the details of the cluster operations, and creates databases, especially large databases, faster than admin tools. Many functionalities including administration operations that were formerly only available in admintools via SSH are now in the Vertica server itself, so you can use them via HTTPS instead of SSH. No special client needs to be installed. It is all handled via the NMA (Node Management Agent.) 

Automate multi-step database maintenance and ML pipelines

Automate multi-step database maintenance or machine learning pipelines or ML model retraining when a threshold of declined accuracy is reached. Stored procedures can now call meta-functions and nested stored procedures up to a call depth of 100, and session parameter changes made by stored procedure now persist after the procedure has completed. 

Run Management Console (MC) in Linux with ADO.net Core support

Use less expensive Linux cloud instances, rather than being required to use a Windows instance for MC. 

Dynamically control hardware/nodes/instances for different purposes with workload routing

Admins can create rules that route the execution of queries coming from clients with a particular workload to a separate subcluster, de-coupling connection from execution, client can connect to one node, execute on a different set of nodes. Clients can set a workload name by adding a workload parameter to their connection string or with SQL syntax post connection.  

Revive to a previous state by saving “restore points,” a snapshot of the database at a point in time

(Eon Mode only) – Store a copy of the catalog and any changed data, not a full extra copy of data direct from the database server. VBR (Vertica Backup and Restoration tool) is not required. 

And this just scratches the surface of the many improvements in this latest version of Vertica.  

Read the OpenText Vertica Release Notes to learn more.