What's new in IBM Software Hub

Back up and restore IBM Software Hub with NetApp Trident protect

If your IBM Software Hub deployment uses NetApp Trident persistent storage, you can now back up and restore your deployment with NetApp Trident protect. This backup and restore method replaces backup and restore with NetApp Astra Control Center.

For details, see:

• IBM Software Hub online backup and restore to the same cluster with NetApp Trident protect
• IBM Software Hub online backup and restore to a different cluster with NetApp Trident protect

Version 5.1.2 of IBM Software Hub includes various fixes.

For details, see What's new and changed in the platform.

Version 14.1.2 of the IBM Software Hub command-line interface includes various fixes.

Version 10.2.0 of the common core services includes various fixes.

For details, see What's new and changed in the common core services.

Version 1.42.0 of the scheduling service includes various fixes.

For details, see What's new and changed in the scheduling service.

Improved navigation for inventories

Inventories are now listed under AI governance in the main navigation, making it easier to find and work with inventories of AI use cases.

Version 5.1.2 of the AI Factsheets service includes various fixes.

For details, see What's new and changed in AI Factsheets.

Run applications on Spark 3.5

You can now use Spark version 3.5.4 to run your applications in Analytics Engine powered by Apache Spark. For more information, see Submitting Spark jobs via API.

Version 5.1.2 of the Analytics Engine powered by Apache Spark service includes various fixes.

For details, see What's new and changed in Analytics Engine powered by Apache Spark.

Change connection lifetime and timeout

You can now use a script to change the following configuration settings:

• The time for which data endures in the Cognos Analytics service instance.
• The duration for the persist.timeout parameter for the web container transport chains.

For details, see Managing configuration settings.

Configure search indexes to be persistent

You can now configure Content Manager to store search index files in a persistent data store. When configured, search indexes no longer need to be recreated after restarting the service instance. This enhancement means that users can start running searches and getting results immediately after the service is restarted. For details, see Setting up persistent data storage in Content Manager.

Version 27.2.0 of the Cognos Analytics service includes various fixes.

Version 5.1.2 of the Cognos Dashboards service includes various fixes.

Version 7.2.0 of the Data Gate service includes various fixes.

For details, see What's new and changed in Data Gate.

Version 5.1.2 of the Data Privacy service includes various fixes.

For details, see What's new and changed in Data Privacy.

Version 5.1.2 of the Data Product Hub service includes various fixes.

For details, see What's new and changed in Data Product Hub.

Version 10.2.0 of the Data Refinery service includes various fixes.

For details, see What's new and changed in Data Refinery.

To read more about these features, see What's new and changed in Data Replication in the IBM Cloud Pak for Data documentation.

Track and record modifications to source databases by using a change log when you replicate data

You can now configure your Data Replication asset to use the Change log business goal. With this business goal, you can replicate changes in the source database to the target database and simultaneously maintain a record of all modifications made to entries in the source database.

Version 5.1.2 of the Data Replication service includes various fixes.

For details, see What's new and changed in Data Replication.

Version 5.1.2 of the DataStage service includes various fixes.

For details, see What's new and changed in DataStage.

Monitor the health and status of your service by using two new service monitors

You can now use Data Virtualization service monitors to check that your Data Virtualization integration with IBM Knowledge Catalog is healthy, and that the last run of the integrated IBM Db2 audit logging process is successful.

For more information about service monitors, see Monitors in IBM Software Hub.

Migrate objects between Data Virtualization instances

You can now migrate Data Virtualization objects between instances that are on the same release version. For example, you might want to export the data from a version 5.1.2 development environment and import it to a version 5.1.2 production environment.

Use the IBM Software Hub export-import utility to import data to and export data from Data Virtualization instances.

You can migrate the following objects:

• Connections and connection details
• Schemas
• Nicknames
• Tables
• Views
• Authorization statements such as GRANTs
• Statistics
• Service instance users
• Service instance groups
• Caches
• Db2 audit policies
• IBM Knowledge Catalog objects:
  • Governance artifacts
  • Catalogs
  • Data protection rules

For more information, see Migrating Data Virtualization data.

Version 3.1.2 of the Data Virtualization service includes various fixes.

For details, see What's new and changed in Data Virtualization.

Version 5.1.2 of the Db2 service includes various fixes.

For details, see What's new and changed in Db2.

Version 7.8.2 of the Db2 Big SQL service includes various fixes.

For details, see What's new and changed in Db2 Big SQL.

Version 5.1.2 of the Db2 Data Management Console service includes various fixes.

For details, see What's new and changed in Db2 Data Management Console.

Version 5.1.2 of the Db2 Warehouse service includes various fixes.

For details, see What's new and changed in Db2 Warehouse.

Version 10.2.0 of the Decision Optimization service includes various fixes.

For details, see What's new and changed in Decision Optimization.

This release of the EDB Postgres service includes various fixes.

For details, see What's new and changed in EDB Postgres.

Version 5.1.2 of the Execution Engine for Apache Hadoop service includes various fixes.

For details, see What's new and changed in Execution Engine for Apache Hadoop.

Run the full set of enrichment and data quality capabilities on more data sources

You now have additional options when enriching or running data quality rules on the following data sources:

Amazon RDS for Oracle

Enrich metadata from Amazon RDS for Oracle databases, create query-based data assets, and also write analysis output to such databases.

Amazon RDS for PostgreSQL

Run data quality rules on assets from Amazon RDS for PostgreSQL databases, create query-based data assets, and also write analysis output to such databases.

Snowflake

Write analysis output to a Snowflake data storage.

Enhancements to output tables for data quality rules

You can now specify certain parameters to generate dynamic names for your rule output tables. Also, you can now select whether you want such tables to be added to your project. They are no longer automatically added.

For consistent setup of rule output tables, a project administrator can now configure default settings for a project. You can still overwrite these settings for individual rules.

View the real-time progress and metrics of metadata import jobs

You can now monitor the progress and metrics of your asset and lineage metadata import job runs, in real time, by navigating to the Run metrics tab. As your jobs run, the tab displays detailed visual views with charts, progress bars, and counts. You can also now see logs containing details of any asset import failures by clicking Download failed assets log.

Pause and resume metadata import jobs

You can now pause and resume your metadata import jobs by using the Pause job run and Resume job run buttons.

Restrict custom properties and custom relationship type by category

You can now restrict custom properties and custom relationship types to a primary category. When restricted, the item can only be applied to artifacts in the specified category and all of its subcategories.

Enhancements in reporting

You can now include SLA rules definitions, SLA rule assessments, job asset and job run asset details in the reporting data mart.

To read more about these features, see What's new and changed in IBM Knowledge Catalog in the IBM Cloud Pak for Data documentation.

Version 5.1.2 of the IBM Knowledge Catalog service includes various fixes.

For details, see What's new and changed in IBM Knowledge Catalog.

Extract metadata from on-premises data sources and analyze data lineage with Manta agents

You can now extract metadata from on-premises data sources and send them for data lineage analysis by using Manta agents. The agents are implemented for the IBM Cognos® Analytics data source.

New IBM Cognos Analytics data source for lineage metadata import

You can now import lineage metadata from the IBM Cognos Analytics data source. After the data is imported, you can visualize it on a lineage graph.

To read more about these features, see What's new and changed in IBM Manta Data Lineage in the IBM Cloud Pak for Data documentation.

Version 5.1.2 of the IBM Manta Data Lineage service includes various fixes.

For details, see What's new and changed in IBM Manta Data Lineage.

Version 4.5.2 of the IBM Match 360 service includes various fixes.

For details, see What's new and changed in IBM Match 360.

Upgrade IBM StreamSets by using the standard service upgrade method

You can now upgrade IBM StreamSets on IBM Software Hub version 5.1.1 in the same way that you upgrade any other service. Previously, IBM StreamSets required a separate upgrade process.

Version 6.0.0 of the IBM StreamSets service includes various fixes.

For details, see What's new and changed in IBM StreamSets.

Version 8.4.0 of the Informix service includes various fixes.

For details, see Informix v15.0.0.1 release notes.

Version 42.10.0 of the MANTA Automated Data Lineage service includes various fixes.

For details, see What's new and changed in MANTA Automated Data Lineage.

This release of the MongoDB service include various fixes.

For details, see What's new and changed in MongoDB.

Version 9.4.2 of the OpenPages service includes various fixes.

For details, see What's new and changed in OpenPages.

Version 5.1.2 of the Orchestration Pipelines service includes various fixes.

For details, see What's new and changed in Orchestration Pipelines.

Updated versions of Planning Analytics software

This release of the service provides the following software versions:

• Planning Analytics Workspace Version 2.0.102

  For details, see 2.0.102 - What's new in the Planning Analytics Workspace documentation.

• Planning Analytics Spreadsheet Services Version 2.0.102

  For details, see 2.0.102 - Feature updates in the TM1 Web documentation.

• Planning Analytics for Microsoft Excel Version 2.0.102

  For details, see 2.0.102 - Feature updates in the Planning Analytics for Microsoft Excel documentation.

• TM1 Database Version 12.4.8.

  For details, see What's new in TM1 Database Version 12 in the TM1 Database Version 12 documentation.

Version 5.1.2 of the Planning Analytics service includes various fixes.

Version 10.2.0 of the RStudio Server Runtimes service includes various fixes.

For details, see What's new and changed in RStudio Server Runtimes.

Use API key authentication with SSL for IBM Db2 Warehouse

You can now use an API key to authenticate to IBM Db2 Warehouse when you are using SQL Pushback and SSL. API keys offer better security, and you can also use an API key to automatically authenticate from a script or application.

Version 10.2.0 of the SPSS Modeler service includes various fixes.

For details, see What's new and changed in SPSS Modeler.

Version 10.2.0 of the Synthetic Data Generator service includes various fixes.

For details, see What's new and changed in Synthetic Data Generator.

Version 5.1.2 of the Watson Discovery service includes various fixes.

For details, see What's new and changed in Watson Discovery.

Version 5.1.2 of the Watson Machine Learning service includes various fixes.

For details, see What's new and changed in Watson Machine Learning.

Version 5.1.2 of the Watson OpenScale service includes various fixes.

For details, see What's new and changed in Watson OpenScale.

Version 5.1.2 of the Watson Speech to Text service includes various fixes.

For details, see What's new and changed in Watson Speech to Text.

Use shared, custom, platform-level CA certificates in RStudio IDE in projects with default Git integration

You can now use shared, custom, platform-level CA certificates in RStudio IDE in projects with default Git integration. In earlier releases, Git certificates were related to user profiles. Custom platform-level certificates are installed by admins in a centralized location as Kubernetes secrets (in the cpd-custom-ca-certs secret) so that multiple users can use the same certificate while working with multiple services. The secret is automatically inserted when RStudio IDE starts so you automatically have access to these certificates. For more information, see Working in RStudio with default Git integration and Creating a secret to store shared custom certificates.

Version 10.2.0 of the Watson Studio service includes various fixes.

For details, see What's new and changed in Watson Studio.

Use Apache Spark 3.5 to run notebooks and scripts

You can now use environments that are based on Spark 3.5 to run your notebooks and scripts in Jupyter notebooks and JupyterLab. For more information, see Spark environments and JupyterLab environments.

Version 10.2.0 of the Watson Studio Runtimes service includes various fixes.

For details, see What's new and changed in Watson Studio Runtimes.

Work with new foundation models in Prompt Lab

You can now use the following foundation models for inferencing from the API and from the Prompt Lab in watsonx.ai:

• granite-3-2-8b-instruct
• granite-vision-3-2-2b
• mistral-small-24b-instruct-2501

For details, see Foundation models in watsonx.ai.

Deploy custom foundation models globally

Administrators can now deploy custom foundation models globally by using the watsonx.ai Inference Framework Manager (IFM) operator, which makes these models available for use in all projects and deployment spaces. Globally deployed models enable administrators to centrally manage and deploy custom models, while providing data scientists and AI builders with access to these models across multiple projects and spaces. These models can then be used for creating and deploying structured or freeform prompt templates in all projects and spaces, and can be used for text generation or inferencing.

To learn more about register custom foundation models for global deployment, see Registering custom foundation models for global deployment.

Enhanced interaction with deployed custom foundation models through chat API

You can now interact with your deployed custom foundation models through chat API. Chatting with a deployed custom foundation model provides you with a more natural and intuitive way to interact with the model, making it easier to ask questions, receive answers, and complete tasks. You can use the chat API to interact with your model for use cases such as customer service, language translation, and content generation to drive business value and improve user satisfaction.

Train, deploy, and inference custom foundation models that are fine-tuned with PEFT techniques

You can now train, deploy, inference, and manage your custom foundation models that are fine-tuned with Parameter-Efficient Fine-Tuning (PEFT) techniques in a more flexible way. By deploying non-quantized (LoRA) or quantized (QLoRA) models using PEFT techniques, you can save resources while taking advantage of the flexibility and customization options provided by using a custom foundation model.

Streamlined deployment for AutoAI for RAG experiments

From the AutoAI user interface, you can now save and deploy a RAG pattern as an AI service so that you can easily access the inferencing endpoint and test the pattern. This feature is available for RAG patterns developed using either the in-memory Chroma database or a Milvus database.

To read more about these features, see What's new and changed in watsonx.ai in the IBM watsonx documentation.

Version 10.2.0 of the watsonx.ai service includes various fixes.

For details, see What's new and changed in watsonx.ai.

Use LLMs for your watsonx Assistant

Large language models (LLMs) are now available for you to configure to improve the capabilities of your assistants. From the Base LLM section of the new Generative AI page, you can enable LLM features for the existing actions in your assistants to improve the conversation capabilities of your assistants. You can choose the foundation model that best interacts with your assistants for conversational search. For more information, see Supported foundation models for GPU features.

Version 5.1.2 of the watsonx Assistant service includes various security fixes.

For details, see What's new and changed in watsonx Assistant.

Version 5.1.2 of the watsonx Code Assistant service includes various fixes.

For details, see What's new and changed in watsonx Code Assistant.

Related documentation:

watsonx Code Assistant

Version 5.1.2 of the watsonx Code Assistant for Red Hat Ansible Lightspeed service includes various fixes.

For details, see What's new and changed in watsonx Code Assistant for Red Hat Ansible Lightspeed.

Version 5.1.2 of the watsonx Code Assistant for Z service includes various fixes.

For details, see What's new and changed in watsonx Code Assistant for Z.

Version 2.4.20 of the watsonx Code Assistant for Z Code Explanation service includes various fixes.

For details, see What's new and changed in watsonx Code Assistant for Z Code Explanation.

Integration enhancements

• You can now export configuration files for target Presto engine, based on their ODBC driver selection (Simba or CData), to more easily establish connections with watsonx.data. This enhancement saves you from having to manually configure Presto engine details manually by using PowerBI.
• You can now use your username and API key as an authentication mechanism to more easily integrate with IBM Knowledge Catalog compared to using the Zen API key.

Access management enhancements

This release of watsonx.data introduces the following access management enhancements:

• Administrators can now configure access for IBM Db2 and IBM Netezza. They can assign roles for watsonx.data users to view, edit, and administer the IBM Db2 and IBM Netezza engines.
• Administrators can now grant or revoke specific permissions to users or roles when creating and viewing their own schemas.
• DAS proxy flow, which was previously deprecated, is now removed and is no longer available in watsonx.data.

CPDCTL CLI enhancements

This release of watsonx.data introduces the following enhancements to IBM Cloud Pak for Data Command Line Interface (IBM cpdctl):

• Starting in version 2.1.2, the wx-data command is available by default, allowing you to perform operations like ingesting, managing engines, and more in watsonx.data.
• You can use the wx-data engine create and wx-data engine delete commands to provision and delete all available engines in watsonx.data.
• You can use the sparkjob command to submit, list, and get the details of a Spark application.
• INSTANCE_ID used in setting the instance environment is replaced with WX_DATA_INSTANCE_ID.

Engine and service enhancements

This release of watsonx.data introduces the following engine and service enhancements:

• You can now use the Azure Data Lake Storage Gen2 with Access Key Authmode with Spark engine to store your data while submitting Spark applications.

Query History Monitoring and Management (QHMM) enhancement

This release includes the following enhancement:

• You can now download Query History Monitoring and Management (QHMM) diagnostic data from the Get diagnostics page in IBM Software Hub.
• You can now select the Presto engine that is associated to a QHMM catalog when you configure query monitoring in watsonx.data.
• You can now use the migration script to transfer QHMM data from the source bucket to the destination bucket in watsonx.data.

Serviceability enhancement

You can now directly add and enable diagnostic telemetry tools, such as Instana and Prometheus, from the watsonx.data user interface (UI) to enhance telemetry data collection and processing.

Storage enhancements

watsonx.data now supports HashiCorp vault for credential and secret management. Users can now access and use the HashiCorp secrets as credentials for registering the following buckets in watsonx.data:

• IBM Cloud Object Storage
• Amazon S3
• IBM Storage Ceph
• MinIO
• Azure Data Lake Storage
• IBM Storage Scale
• Apache Ozone.

Version 2.1.2 of the watsonx.data service includes various fixes.

For details, see What's new and changed in watsonx.data.

Comparing AI assets with Evaluation Studio

With Evaluation Studio, you can evaluate and compare your generative AI assets with quantitative metrics and customizable criteria that fit your use cases. Evaluate the performance of multiple assets simultaneously and view comparative analyses of results to identify the best solutions.

Improved navigation for inventories

Inventories are now listed under AI governance in the main navigation, making it easier to find and work with inventories of AI use cases.

To read more about these features, see What's new and changed in watsonx.governance in the IBM watsonx documentation.

Version 2.1.2 of the watsonx.governance service includes various fixes.

For details, see What's new and changed in watsonx.governance.

Add the AI assistant to the chat and use

You can add the AI assistant from the AI assistant builder or from the IBM watsonx Assistant to the AI chat to help your users to resolve specific use-cases through the chat. The AI assistants that you add to the chat are available to all users in the tenant. See details in Managing AI assistants in the AI chat.

As a user, you can interact with AI-powered assistants, AI agents, and skills by using the AI chat. With this chat, you can complete various generative artificial intelligence (AI) tasks, or even use AI assistants to perform more complex tasks like sending emails or posting job requisitions. To use the AI assistant in chat, see Using the AI chat.

Save secret skill credentials in the external vault

You can now save your secrets skill credentials in the external vault where you want, instead saving in the default internal vault. As a watsonx Orchestrate tenant admin, you can configure the external vault to your watsonx Orchestrate instance for both imported skills or custom skills and pre-built skills.

Currently, you can use CyberArk vault, which is supported in watsonx Orchestrate.

Manage skill connections

As a builder you can now manage the connections of the Team skills, of skill sets from AI assistants, and of the watsonx Orchestrate Agent Skill set. You can:

• Configure an application credential as either Team credentials or Member credentials.
• Set connection parameters, such as client ID and client secret, as the required parameters for app integration.
• Connect to an app or configure the app connection either from the Skill catalog page or through the chat.

Version 5.3.0 of the watsonx Orchestrate service includes various fixes.

For details, see What's new and changed in watsonx Orchestrate.

Get gen AI recommendations to help resolve alerts from the privileged monitoring service

Tech preview If you purchased IBM watsonx.ai, you can now enable the generative AI chat to provide recommendations for resolving alerts that are generated by the following privileged monitors:

• Network status check (check-network-status)
• Volume usage status check (check-volume-status)
• EDB cluster status check (check-edb-cluster-status)

For example, if your environment produces a critical network status check event, you can use the gen AI chat to learn more about the tools and techniques that you can use to troubleshoot network problems.

For more information about enabling gen AI recommendations, contact IBM Support.

Audit logging configuration information is now stored in secrets

If you configured IBM Software Hub to forward audit records to a security information and event management (SIEM) solution, you might need to update your configuration.

Starting in IBM Software Hub Version 5.1.1, the data required to connect to your SIEM solution must be stored in a secret named zen-audit-secret.

• If you stored your configuration in the zen-audit-config ConfigMap, your configuration is automatically migrated to the zen-audit-secret secret when you upgrade to IBM Software Hub Version 5.1.1.
• If you stored your configuration in a custom ConfigMap, you must manually migrate your configuration to the zen-audit-secret secret.

For more information, see Exporting IBM Software Hub audit records to a security information and event management solution.

Version 5.1.1 platform includes various fixes and updates.

For details, see What's new and changed in the platform.

Check the health of more services with the --services flag:

You can check five new services with the --services flag, such as:

• Data Product Hub
• IBM Orchestration Pipelines
• IBM Product Master
• RStudio Server Runtimes
• watsonx Code Assistant for Red Hat Ansible Lightspeed

For the full list of service keys, see the --services flag in the cpd-cli health service-functionality command reference.

Specify domain names that should not be routed through your proxy server

If you use the cpd-cli manage create-proxy-config command to apply your cluster-level HTTP proxy settings to IBM Software Hub, you can use the new --no_proxy option to specify a list of domain names that should not be routed to your proxy server.

For more information, see the cpd-cli manage create-proxy-config command reference.

New commands to back up and restore IBM Software Hub operators with the OADP utility

Now you can back up and restore the set of operators in an IBM Software Hub instance separately from the instance. In previous releases, operators are backed up and restored when you back up and restore an IBM Software Hub instance. For details, see the oadp operators command reference.

Version 14.1.1 of the IBM Software Hub command-line interface includes various fixes.

For details, see What's new and changed in the IBM Software Hub command-line interface.

Version 10.1.0 of the common core services includes various fixes and updates.

For details, see What's new and changed in the common core services.

Version 1.41.0 of the scheduling service includes various fixes.

For details, see What's new and changed in the scheduling service.

Version 5.1.1 of the AI Factsheets service includes various fixes.

For details, see What's new and changed in AI Factsheets.

Spark UI and Spark history server available on remote physical locations

You can view the status of the Spark workloads on remote clusters by using the history server of your Spark instance.

Run Spark workloads interactively on remote physical locations

You can run Spark applications interactively by using the Kernel API on remote clusters.

Version 5.1.1 of the Analytics Engine powered by Apache Spark service includes various fixes.

Create service instance more easily by using the new interface

By using the new, better organized, interface, you can now create an instance with more properties. For example, you can add an additional property to JDBC connections to configure Audit and Content Store during the instance creation. Also, you can review all the values that you set in a clear summary of the properties. For more information, see Creating a service instance for Cognos Analytics from the web client.

Synchronize session timeout between Cognos Analytics and platform

A Cognos Analytics service instance now automatically uses a session timeout that equals the session expiry time in the IBM Software Hub product-configmap ConfigMap. The default session expiry time in Cognos Analytics is 1 hour. For more information, see Setting the idle session timeout.

CronJob removes unavailable dispatchers from Content Manager

For each Cognos Analytics service instance, a new CronJob is created to remove, on an hourly basis, dispatchers that are no longer in service in Content Manager.

New access permissions of report output files and directories in a file system

When Cognos Analytics is configured to save report outputs to a file system, created files and directories have now new access permissions for groups. The groups can read and write files, and can read, write, and execute directories. For more information, see Saving report outputs to file system.

Update of the Cognos Analytics container time zone

The TZ environment variable is now added to the Cognos Analytics containers when you change the time zone in the Cognos Analytics service instance. This update affects timestamps in log files of the cm, reporting, rest, and smarts pods.

Updated software version for Cognos Analytics

This release of the service provides Version 12.0.4 IF1 of the Cognos Analytics software. For details, see Release 12.0.4 in the Cognos Analytics documentation.

Version 27.1.0 of the Cognos Analytics service includes various fixes.

Updated software version

This release of the service provides Version 12.0.4 IF2 of the Cognos Analytics dashboards software. For details, see Release 12.0.4 IF2 - Dashboards in the Cognos Analytics documentation.

To read more about these features, see What's new and changed in Cognos Dashboards in the IBM Cloud Pak for Data documentation.

Version 5.1.1 of the Cognos Dashboards service includes various fixes.

Version 7.1.0 of the Data Gate service includes various fixes.

For details, see What's new and changed in Data Gate.

Version 5.1.1 of the Data Privacy service includes various fixes.

For details, see What's new and changed in Data Privacy.

Version 5.1.1 of the Data Product Hub service includes various fixes.

For details, see What's new and changed in Data Product Hub.

Version 10.1.0 of the Data Refinery service includes various fixes.

For details, see What's new and changed in Data Refinery.

Version 5.1.1 of the Data Replication service includes various fixes.

For details, see What's new and changed in Data Replication.

Use Git integration to sync your projects with a GitLab or GitHub repository

You can now use Git integration to synchronize your DataStage project with a Git repository that uses either GitHub or GitLab platform.

Generate optimized pipeline runner jobs by using advanced import

When you import your flows, you can now use the advanced import option to generate optimized pipeline runner jobs.

Run stored procedures in Microsoft Azure PostgreSQL connector

You can now call an existing stored procedure or write your own procedure statement syntax in Microsoft Azure PostgreSQL connector.

Search within a canvas

You can now search for stages, links, columns, and parameters in your flow by using the Find in canvas option. You can specify case sensitivity, navigate through the search results, move to the next and previous matches, and view the path through which the columns flow in your canvas.

New transform functions for PostgreSQL and Teradata connectors

You can now use transform functions for pushdown in PostgreSQL and Teradata connectors.

Use DataStage on IBM Z and LinuxOne

You can now move and transform data between operational, transactional and analytical target systems using DataStage on IBM Z and LinuxONE systems.

Version 5.1.1 of the DataStage service includes various fixes.

For details, see What's new and changed in DataStage.

Version 10.1.0 of the Decision Optimization service includes various fixes.

For details, see What's new and changed in Decision Optimization.

This release of the EDB Postgres service includes various fixes.

For details, see What's new and changed in EDB Postgres.

Version 5.1.1 of the Execution Engine for Apache Hadoop service includes various fixes.

For details, see What's new and changed in Execution Engine for Apache Hadoop.

Edit custom properties directly in the artifact view

When viewing governance artifacts, you can now turn on editor mode for custom properties. In the Details section of the artifact view, the Edit values toggle is off by default, which means that you can only see custom properties that have values defined for the artifact. Switch editor mode on to see all of the available custom properties and edit their values.

Import, enrich, and assess data quality of data from additional data sources and in additional data formats

You can now import metadata from a Denodo platform, enrich that data, and assess its quality.

In addition, you can now work with tables in Delta Lake and Iceberg format that come from these connectors:

• Amazon S3
• Generic S3
• Google Cloud Storage
• Microsoft Azure Data Lake Storage

Access the relationship explorer project assets

You can now view the relationship explorer graph through your assets contained in different projects.

View indirect relationships on the relationship explorer graph

You can now see indirect relationships on the relationship explorer graph. When hiding items between two nodes, the edge change into a dashed line, indicating that one or more nodes were hidden.

To read more about these features, see What's new and changed in IBM Knowledge Catalog in the IBM Cloud Pak for Data documentation.

Version 5.1.1 of the IBM Knowledge Catalog service includes various fixes.

For details, see What's new and changed in IBM Knowledge Catalog.

Assign aliases for more complete lineage

In complex data environments that connect multiple systems and technologies, lineage might appear incomplete due to missing system connections. You can now assign aliases to systems to bridge these gaps and generate a more complete and accurate cross-system lineage.

View previous versions of the data lineage

You can now access earlier versions of lineage. When a system is added and rescanned, you can choose to view lineage from previous scans, allowing you to track changes and analyze how lineage has evolved over time.

Review SLA rule compliance for lineage assets

You can now see lineage asset SLA rule compliance information in the data lineage graph. If an asset has any violations, you can investigate further to resolve the underlying issue.

New data sources for lineage metadata import

You can now import lineage metadata from the following additional data sources. After the data is imported, you can visualize it on a lineage graph.

• Google BigQuery
• Microsoft Azure Databricks
• Microsoft SQL Server Integration Services (SSIS)

Use secrets and vaults for lineage import

You can now use secrets and vaults when you create connections for lineage metadata import.

To read more about these features, see What's new and changed in IBM Manta Data Lineage in the IBM Cloud Pak for Data documentation.

Version 5.1.1 of the IBM Manta Data Lineage service includes various fixes.

For details, see What's new and changed in IBM Manta Data Lineage.

Version 4.4.21 of the IBM Match 360 service includes various fixes.

For details, see What's new and changed in IBM Match 360.

Install IBM StreamSets by using the standard service installation method

You can now install IBM StreamSets on IBM Software Hub in the same way that you install any other service. Previously, IBM StreamSets required a separate installation process.

Deploy Data Collector version 6.1.0

You can now deploy a Data Collector engine version 6.1.0. With version 6.1.0, you can:

• Use a reusable connection for a Splunk destination rather than directly entering the details in the destination.
• Specify a single Kafka stage library for all supported Kafka versions when you configure any Kafka stage.

For a full list of the new features, see 6.1.x Release Notes in the IBM StreamSets documentation.

Create an engineless connection for easy access to the latest stage libraries

In addition to standard connections, which are based on a selected authoring Data Collector, you can now create engineless connections. An engineless connection can use any stage library that is associated with the latest Data Collector version.

Version 6.0.0 of the IBM StreamSets service includes various fixes.

For details, see What's new and changed in IBM StreamSets.

Version 8.3.0 of the Informix service includes various fixes.

Version 42.9.0 of the MANTA Automated Data Lineage service includes various fixes.

For details, see What's new and changed in MANTA Automated Data Lineage.

This release of the MongoDB service include various fixes.

For details, see What's new and changed in MongoDB.

Scan imported files with antivirus software

You can now use ClamAV to scan files for viruses and malware before importing them into OpenPages.

For details, see Configure anti-virus scanning for imported files in the IBM OpenPages documentation.

Version 9.4.1 of the OpenPages service includes various fixes.

Customize how you allocate and use hardware resources by using remote physical locations

Tech preview You can now set up remote physical locations for pipelines. You can customize default hardware specifications and environment variables on the remote physical locations and you can allocate CPU and memory to custom, pipeline-specific environments where you can build your pipeline flows and deployments.

For more information, see Installing Orchestration Pipelines runtime on a remote physical location.

Use Bash script output as node input in a single node

You can now assign your Bash script output as input directly in a node and then run the routine in a single node. With this simplified process, you no longer need to use an extra node to save the Bash script output as a parameter before you use the output as Pipelines node input.

Automatically delete associated jobs of a Pipelines asset

You can choose to automatically delete associated jobs when you delete a Pipelines asset in the settings. Any associated jobs for the Pipelines asset are automatically deleted when you delete the asset.

Version 5.1.1 of the Orchestration Pipelines service includes various fixes.

Updated versions of Planning Analytics software

This release of the service provides the following software versions:

• Planning Analytics Workspace Version 2.0.101

  For details, see 2.0.101 - What's new in the Planning Analytics Workspace documentation.

• Planning Analytics Spreadsheet Services Version 2.0.101

  For details, see 2.0.101 - Feature updates in the TM1 Web documentation.

• Planning Analytics for Microsoft Excel Version 2.0.101

  For details, see 2.0.101 - Feature updates in the Planning Analytics for Microsoft Excel documentation.

• TM1 Database Version 12.4.7.

  For details, see What's new in TM1 Database Version 12 in the TM1 Database Version 12 documentation.

Version 5.1.1 of the Planning Analytics service includes various fixes.

Easy-access Product Master application URLs - Now you can find the URLs for the Admin UI and Persona-based UI of the Product Master application on the Product Master service instance page. You can use the icons either to directly open the application or to copy the URL for the application so that you can open it yourself from a web browser.

Version 7.1.0 of the Product Master service includes various fixes.

For details, see What's new and changed in Product Master.

Version 10.1.0 of the RStudio Server Runtimes service includes various fixes.

Use trusted sessions with your Teradata connector

You can now use trusted sessions for authenticating users with your Teradata connector. When you use a trusted session to create a connection for Teradata, a proxy user is set up and logged in as a trusted user. Other users can then access the connection through the logged-in proxy user. This approach can offer improved performance and scalability by eliminating the need to authenticate user identities and roles through individual logon sessions. User queries through the proxy user can also be logged, which increases traceability.

Version 10.1.0 of the SPSS Modeler service includes various fixes.

For details, see What's new and changed in SPSS Modeler.

Version 10.1.0 of the Synthetic Data Generator service includes various fixes.

For details, see What's new and changed in Synthetic Data Generator.

Version 1.7.0 of the Voice Gateway service includes various fixes.

Version 5.1.1 of the Watson Discovery service includes various fixes.

Version 5.1.1 of the Watson Machine Learning service includes various fixes.

Version 5.1.1 of the Watson OpenScale service includes various fixes.

For details, see What's new and changed in Watson OpenScale.

Version 5.1.1 of the Watson Speech to Text service includes various fixes.

Version 10.1.0 of the Watson Studio service includes various fixes.

For details, see What's new and changed in Watson Studio.

Version 10.1.0 of the Watson Studio Runtimes service includes various fixes.

Save resources by training, deploying, and inferencing fine-tuned models with LoRA and QLoRA adapters

Fine tuning is the process of training a model on additional training data by updating the weights of the model, which results in a completely new model. Fine tuning is resource intensive process and normally requires you to have multiple GPUs due to memory requirements. To save resources, you can now use parameter-efficient fine tuning (PEFT) techniques such as low-rank adaptation (LoRA) and quantized low-rank adaptation (QLoRA) to train, deploy, and inference your fine-tuned models programmatically.

The following foundation models are now available for you to customize by using fine tuning techniques:

• granite-3-1-8b-base
• llama-3-1-8b
• llama-3-1-70b
• llama-3-1-70b-gptq

Deploy generative AI applications with AI services

You can now use AI services in watsonx.ai to deploy your applications. An AI service is a deployable unit of code that you can use to capture the logic of your generative AI use cases. While Python functions are the traditional way to deploy machine learning assets, AI services offer a more flexible option to deploy code for generative AI applications, such as streaming. When your AI services are successfully deployed, you can use the endpoint for inferencing from your application.

Deploy and inference DeepSeek-R1 distilled models with watsonx.ai for accelerated development

You can now use the distilled variants of DeepSeek-R1, a powerful open-sourced reasoning model, to securely deploy and inference DeepSeek-R1 models with watsonx.ai, which enabled you to accelerate the development of AI-powered solutions. The DeepSeek-R1 model can be deployed as a custom foundation model with watsonx.ai.

Enhancements for AutoAI for RAG experiments

These features for AutoAI RAG experiments are new with this release:

• You can now add documents in for Markdown format for your document collections.
• You can exercise greater control over chunking settings for creating RAG patterns.
• Use either a standalone Milvus vector database or a Milvus database created with watsonx.ai for storing your indexed documents.

Work with new foundation models in Prompt Lab

You can now use the following foundation models for inferencing from the API and from the Prompt Lab in watsonx.ai:

• codestral-2501
• llama-3-3-70b-instruct
• mistral-large-instruct-2411
• pixtral-large-instruct-2411

For details, see Foundation models in watsonx.ai.

Work with new Granite embedding models for text matching and retrieval tasks

You can now use the following embedding models to vectorize text in multiple languages:

• granite-embedding-107m-multilingual-rtrvr
• granite-embedding-278m-multilingual-rtrvr

For details, see Foundation models in watsonx.ai.

Use IBM Granite time series foundation models and the watsonx.ai API to forecast future values

Use the new time series API to pass historical data observations to an IBM Granite time series foundation model that can forecast future values with zero-shot inferencing. You can now work with the following time series models provided by IBM:

• granite-ttm-512-96-r2
• granite-ttm-1024-96-r2
• granite-ttm-1536-96-r2

To read more about these features, see What's new and changed in watsonx.ai in the IBM watsonx documentation.

Version 10.1.0 of the watsonx.ai service includes various fixes.

For details, see What's new and changed in watsonx.ai.

Hide or display the skill output

You can now choose to hide or display the skill output that is stored as an assistant variable.

Integrate your watsonx Assistant with Slack

You can integrate your watsonx Assistant with Slack. Then you can configure the assistant to support certain events so that your assistant can respond to questions that are asked in Slack direct messages or in Slack channels where the assistant is directly mentioned.

Evaluate assistant's performance

You can now evaluate the performance of your watsonx Assistant by uploading a comprehensive, relevant collection of utterances and sending the utterances to your assistant in a test run.

Post-process the conversational search responses

You can now save your conversational search responses in action variables of your watsonx Assistant for post-processing.

Version 5.1.1 of the watsonx Assistant service includes various security fixes.

For details, see What's new and changed in watsonx Assistant.

Version 5.1.1 of the watsonx Code Assistant service includes various fixes.

Version 5.1.1 of the watsonx Code Assistant for Red Hat Ansible Lightspeed service includes various fixes.

Version 5.1.1 of the watsonx Code Assistant for Z service includes various fixes.

Version 2.4.10 of the watsonx Code Assistant for Z Code Explanation service includes various fixes.

Data sources, catalogs, and storage enhancements

This release includes the following data sources, catalogs, and storage enhancements:

• Now, you can test connections for the following data sources and storage:
  • IBM Db2 for i
  • Apache Phoenix
  • IBM Data Virtualization Manager
  • BigQuery
  • MySQL
  • Google Cloud Storage
• You can now register and load external pre-existing Hudi and Delta tables on an object storage using watsonx.data APIs.

Integration enhancements

This release of watsonx.data introduces the following enhanced integrations with other services:

• Connection Information page now includes:
  • Presto configuration details for DBT integration. You can copy the Presto configuration details that are required for DBT integration from this page.
  • Option to export the TDS file, which includes Presto engine configuration details required for Tableau integration.
• Now you can define IBM Knowledge Catalog governance policies for Presto (C++) engine when you integrate with watsonx.data.

Engine and service enhancements

This release of watsonx.data introduces the following engine and service enhancements:

• You can now create a Spark application from the Applications tab of the Spark engine details page.
• You can now use Spark version, 3.5.4 to run the applications in watsonx.data.
• In Milvus you can now do a hybrid GroupBy search based on multiple vector columns and also customize the group size when you run search queries.
• Starting from watsonx.data 2.1.1 version, Milvus 2.5.0 is supported.

CPDCTL CLI enhancements

IBM CPDCTL CLI is now used to configure and manage different operations in watsonx.data. Using the CPDCTL CLI, you can manage configuration settings, run ingestion jobs, manage engines, data sources, and storages. The following two plugins are currently used to execute these operations:

• config- To configure watsonx.data service environment and users.
• wx-data - To perform other operations such as, ingesting, managing engines, etc in watsonx.data.

Access management enhancements

This release of watsonx.data introduces the following access management enhancements:

• Access Management Service (AMS) in watsonx.data platform is enhanced to support JSON Web Token (JWT) authentication for incoming requests from Presto. This capability allows AMS to authenticate users based on JWTs, ensuring secure and efficient access control.
• You can now assign users and roles to infrastructure components in batches of Twenty.
• IBM watsonx.data now supports Apache Ranger HadoopSQL policies to govern data with Spark engines. It allows you to define Ranger policies when the Spark engine access data from Hadoop clusters. Enabling Ranger policy ensures robust data security and governance. With the Ranger policy, you can configure table authorization (L3), row-level filtering, and column masking for data.

Serviceability enhancements

You can now enable the OpenTelemetry sidecar collector to enhance telemetry data collection and processing.

In addition to the support for Instana for serviceability, Prometheus and Grafana are now integrated with watsonx.data to collect and visualize OpenTelemetry metrics..

Version 2.1.1 of the watsonx.data service includes various fixes.

Comparing AI assets with Evaluation Studio

With Evaluation Studio, you can evaluate and compare your generative AI assets with quantitative metrics and customizable criteria that fit your use cases. Evaluate the performance of multiple assets simultaneously and view comparative analyses of results to identify the best solutions.

Improved navigation for inventories

Inventories are now listed under AI governance in the main navigation, making it easier to find and work with inventories of AI use cases.

To read more about these features, see What's new and changed in watsonx.governance in the IBM watsonx documentation.

Version 2.1.1 of the watsonx.governance service includes various fixes.

Enhanced skill flows can now include conditional branches

When creating business automation, builders can now include conditional branches that enable users to adapt their skill flows depending on a particular condition or the output of a given action. Builders can now use one path from the multiple paths options for users to follow, depending on certain conditions. By using conditional branches in your skill flows, you can achieve seamless end-to-end flows.

For example, in an accident insurance scenario, the insurance operator needs to contact the customer to inform them about policy changes. If the customer's preferred communication method fails, then the operator needs to switch to an alternative means of communication.

Integrate your AI assistant builder with Slack

You can now integrate your AI assistant builder with Slack. When integrated, depending on the events that you configure the AI assistant builder to support, your AI assistant builder can respond to questions that are asked in direct messages or in channels where the AI assistant builder is directly mentioned. For more information, see Integrating with Slack.

Integrated Serviceability

If you face any issues while opening a watsonx Orchestrate instance, experience functionality problems, or notice that it is not in a healthy state, you can download the specific watsonx Orchestrate logs that are needed for the support team to troubleshoot the issues offline. To download go to Support > Diagnosis > Select watsonx Orchestrate and download the issue logs and send them to IBM support.

Custom signed certificates

watsonx Orchestrate supports usage of custom signed certificates from 5.1.1. When you require to use custom CA certificates or self-signed certificates in watsonx Orchestrate, you can now follow this documentation Creating a secret to store shared custom certificates.

Version 5.2.0 of the watsonx Orchestrate service includes various fixes.

IBM Software Hub Version 5.1 is the new foundation for IBM Cloud Pak for Data, IBM Data Product Hub, IBM watsonx, and IBM watsonx Code Assistant.

If you are running IBM Cloud Pak for Data Version 4.8 or Version 5.0, you can upgrade to IBM Software Hub Version 5.1. For more information, see Upgrading IBM Software Hub.

When you install IBM Software Hub, you can access the IBM Software Hub Administration Console, which offers a dedicated and streamlined experience for administrators.

IBM Software Hub Control Center (Control Center) is an optional component that helps you manage resource use across multiple instances of IBM Software Hub that are deployed on the same cluster.

For more information on IBM Software Hub Control Center, see Installing IBM Software Hub Control Center.

It is now easier to back up and restore an IBM Software Hub instance when you use the OADP utility. The improved process includes the following changes:

• You need to run only a single command to create an online or offline backup. The single command does a backup precheck, runs backup prehooks, backs up volumes and Kubernetes resources, runs backup posthooks, and validates the backup.
• You need to run only a single command to restore a backup. The single command does a restore precheck, restores volumes and Kubernetes resources, runs restore posthooks, and validates the restore.
• Some tasks that you previously had to do to prepare IBM Software Hub and some services before creating a backup are now automated.
• Some tasks that you previously had to do to after a restore to make IBM Software Hub and some services fully functional are now automated.
• You no longer need to manually download the backup script cpd-operators.sh.
• A new script was added to clean up a cluster before you restore a backup.

Access more data with new connectors

• Denodo
• IBM watsonx.data Milvus
• Microsoft Azure PostgreSQL

Integrated service connections

You can now add new connections using the information from existing service instances. This means that parameter values for the new connection can be automatically filled in from the existing instance.

Multi-node writing from the watsonx.data Presto connector

You can now write data from the watsonx.data Presto connector to DataStage using multiple nodes for parallel writes.

New engine connections for the watsonx.data Presto data source

You can now use the Presto (C++) engine with the watsonx.data Presto data source, giving you more options for querying your data.

To read more about these features, see What's new and changed in Common core services in the IBM Cloud Pak for Data documentation.

Version 10.0.0 of the common core services includes various fixes.

For details, see What's new and changed in the common core services.

Version 1.40.0 of the scheduling service includes various fixes.

For details, see What's new and changed in the scheduling service.

AI Factsheets on IBM Z and LinuxONE

Now you can use AI Factsheets to track machine learning models from request to production in AI use cases on IBM Z and LinuxONE. Use the detailed factsheets to meet your governance and compliance goals.

For details, see Planning for IBM Software Hub on IBM Z and LinuxONE.

Version 5.1.0 of the AI Factsheets service includes various fixes.

For details, see What's new and changed in AI Factsheets.

Automatic daily database snapshot backups

IBM Analytics Engine now automatically backs up the metastore database each day. Administrators can restore the database from the snapshots.

For more information, see Automated Backup and Restore.

Improved flexibility when managing Spark environment variables

When configuring your Spark environment variables, you can now decide whether your changes apply to:

• All Spark instances and jobs
• A single instance of the Analytics Engine
• An individual Spark job

IBM Power (ppc64le) supports Spark with R4.3

Spark with R4.3 is supported on IBM Power (ppc64le) starting in 5.1.

Schedule Spark workloads on remote physical locations

You can now install Analytics Engine powered by Apache Spark on a remote physical location so that you can run Spark workloads on remote clusters. This capability is not enabled by default.

For information about how to enable it, see Setting up a remote physical location for IBM Software Hub and Installing Analytics Engine powered by Apache Spark on a remote physical location.

Version 5.1.0 of the Analytics Engine powered by Apache Spark service includes various fixes.

For details, see What's new and changed in Analytics Engine powered by Apache Spark.

Optimize individual pod memory and ephemeral storage

You can now use a script to fine-tune memory and ephemeral storage in pods to improve service performance. For details, see Fine-tuning memory and ephemeral storage in pods.

Save report outputs to file system

You can now use scripts to configure Cognos Analytics to save report outputs to a file system. Report outputs are saved on a persistent volume in the project where the Cognos Analytics service instance is provisioned. For details, see Saving report outputs to file system.

Updated software version

This release of the service provides Version 12.0.4 of the Cognos Analytics software. For details, see Release 12.0.4 in the Cognos Analytics documentation.

Version 27.0.0 of the Cognos Analytics service includes various fixes.

For details, see What's new and changed in Cognos Analytics.

Updated software version

This release of the service provides Version 12.0.4 of the Cognos Analytics dashboards software. For details, see Release 12.0.4 - Dashboards in the Cognos Analytics documentation.

To read more about these features, see What's new and changed in Cognos Dashboards in the IBM Cloud Pak for Data documentation.

Version 5.1.0 of the Cognos Dashboards service includes various fixes.

For details, see What's new and changed in Cognos Dashboards.

Version 7.0.0 of the Data Gate service includes various fixes.

For details, see What's new and changed in Data Gate.

Version 5.1.0 of the Data Privacy service includes various fixes.

For details, see What's new and changed in Data Privacy.

Monitor your insights with dashboards

Both administrators and data producers can now use the Insights dashboard to monitor their data products and community activity. The Insights dashboard provides a comprehensive, centralized overview of open tasks, delivered data products, and more. By delivering real-time metrics, the Insights dashboard provides detailed data insights at scale and increases workflow efficiency across the data community.

Create custom business domains to organize your data products

Improve your data community's organization and optimize your data products' searchability by creating custom business domains. With new, custom business domains, you can easily organize your data products into intuitive categories and curate your community for your business needs.

Add custom properties to data products

You can now create and add custom properties to data products to optimize searchability, classification, and organization. By adding custom properties, you can structure information and curate your data product to meet specific business needs.

Enhance and update your published data products

To further enhance and improve data products, you can now create new versions of your published data products. When creating a new version, you can edit data assets, change delivery methods, and manage the access level. By continuously creating new versions of published data products, you can help ensure data accuracy and currency.

Pre-approve data consumers for data products requiring approval

For data consumers who frequently access data products requiring approval, you can now create a list of pre-approved users or user groups. By defining a pre-approved list, you streamline the subscription process for both data consumers and producers and improve efficiency in delivering data products.

Send notifying comments for requests for new data products

To ensure that comments do not go unnoticed, you can now send notifying comments for requests for new data products from your task inbox. The approver for a data product request can enter comments or questions for the requester. The requester receives notifications directly in the user interface or by email. The approver is also notified when the requester responds.

Expedite data product approvals with custom approval workflows

Administrators can now create custom approval workflows for data products that require approval. With custom approvals, multiple levels for approving a data product are controlled by a single workflow. Each user in the workflow receives a task and a notification at the appropriate time to ensure all approval levels are met before the data product is delivered. The approval process is based upon a workflow configuration, which is in turn based upon an imported workflow template file.

To read more about these features, see What's new and changed in Data Product Hub in the IBM Data Product Hub documentation.

Version 5.1.0 of the Data Product Hub service includes various fixes.

For details, see What's new and changed in Data Product Hub.

Schedule Data Refinery jobs in Git-based projects

You can now schedule jobs for Data Refinery jobs in Git-based projects. You can set up scheduling when you create the job.

Version 10.0.0 of the Data Refinery service includes various fixes.

For details, see What's new and changed in Data Refinery.

Audit logging

Data Replication now integrates with the IBM Software Hub audit logging service. Auditable events for Data Replication are forwarded to the security information and event management (SIEM) solution that you integrate with.

For more information, see:

• Audit events for Data Replication
• Exporting IBM Software Hub audit records to a security information and event management solution

Add multiple licenses to your Data Replication service installation

If you have more than one replication license and want to gain access to all of the capabilities provided by your licenses within the same IBM Software Hub instance, you can now add these licenses to your Data Replication service installation as replication extensions. For details, see Extending Data Replication capabilities.

Use an IBM Data Replication Access Server connection for replicating data between remote source and target data stores in your project

You can now replicate data between remote source and target data stores with the Data Replication service by using an IBM Data Replication Access Server connection to a remote Change Data Capture (CDC) replication engines deployment.

For details, see Setting up remote CDC replication.

Use IBM Db2 for z/OS as a source data store for replicating data in your project

You can now replicate data from IBM Db2 for z/OS with the Data Replication service.

Use IBM Db2 Database as a source data store for replicating data in your project

You can now replicate data from IBM Db2 Database with the Data Replication service.

Use watsonx.data as a target data store for replicating data in your project

You can now replicate data to watsonx.data with the Data Replication service.

To read more about these features, see What's new and changed in Data Replication in the IBM Cloud Pak for Data documentation.

Version 5.1.0 of the Data Replication service includes various fixes.

For details, see What's new and changed in Data Replication.

Implement version control in your projects by enabling Git integration

Enable Git integration to sync your project with a Git repository. You can clone changes into your project or commit selected changes to the specified repository and branch.

Test your flows by using MettleCI unit testing

Create a test case component to run tests on a DataStage flow. Generate, upload, or intercept data to run tests and compare the expected and actual results.

Run jobs on remote engines by using DataStage Anywhere

Use DataStage Anywhere to run jobs on a remote engine that is deployed in a location of your choice. You can deploy an engine within your own environment, in an on-premises location, or in any cloud or data center.

New user interface for the Transformer stage

You can now enable the new Transformer stage UI in your flow settings to see the stage's visual enhancements and usability improvements. In the new tile-based interface, you can view all elements on one screen, with mapping links between input and output columns. The new interface includes the following enhancements:

• Zoom in and out
• Drag and drop columns into position
• Undo and redo actions
• Search all elements with find
• Prompt to save on exit

Manage workloads for your DataStage instance

You can now specify workload management settings, including job limits and memory usage, in the settings of your DataStage instance.

Run the Watsonx.data connector in ELT run mode

You can now run the Watsonx.data connector in ELT mode with SQL Pushdown. Running in ELT mode transforms data directly on the target system and can increase the efficiency of your flows.

Assign the Super operator role to users

You can now assign the Super operator role to users. Users with this role can create and run jobs, but cannot view or edit DataStage assets.

To read more about these features, see What's new and changed in DataStage in the IBM Cloud Pak for Data documentation.

Version 5.1.0 of the DataStage service includes various fixes.

For details, see What's new and changed in DataStage.

Speed up schema listings and table counts by configuring your source setup

You can now improve the performance of operations such as listing schemas in the Explore view and counting tables on the Data sources page. Configure whether the service uses a custom query or an API method to list schemas and count tables at the source type level or the Connection Identifier (CID) level.

To read more about these features, see What's new and changed in Data Virtualization in the IBM Cloud Pak for Data documentation.

Version 3.1.0 of the Data Virtualization service includes various fixes.

For details, see What's new and changed in Data Virtualization.

Version 5.1.0 of the Db2 service includes various fixes.

For details, see What's new and changed in Db2.

Version 7.8.0 of the Db2 Big SQL service includes various fixes.

For details, see What's new and changed in Db2 Big SQL.

Version 5.1.0 of the Db2 Data Management Console service includes various fixes.

For details, see What's new and changed in Db2 Data Management Console.

Version 5.1.0 of the Db2 Warehouse service includes various fixes.

For details, see What's new and changed in Db2 Warehouse.

Compare tables in Decision Optimization experiments to see differences between scenarios

You can now compare tables in a Decision Optimization experiment in both the Prepare data or Explore solution view. This comparison shows data value differences between scenarios displayed next to each other.

Version 10.0.0 of the Decision Optimization service includes various fixes and updates.

For details, see What's new and changed in Decision Optimization.

This release of the EDB Postgres service includes various fixes.

For details, see What's new and changed in EDB Postgres.

Execution Engine for Apache Hadoop now uses Java 11

The Hadoop Execution Engine RPM is now upgraded to use Java 11 instead of Java 8. All of the edge nodes must also be upgraded to Java 11 before you can install the RPM.

For more information, see Installing Execution Engine for Apache Hadoop on Apache Hadoop clusters.

Version 5.1.0 of the Execution Engine for Apache Hadoop service includes various fixes.

For details, see What's new and changed in Execution Engine for Apache Hadoop.

Enhanced gen AI based enrichment (IBM Knowledge Catalog Premium and IBM Knowledge Catalog Standard)

• The granite-8b-code-instruct model replaces the previously used granite 13b model for generating asset and column descriptions. The new model provides more accurate results and needs less memory and storage.
• Business term abbreviations are now taken into account when display names are generated during metadata enrichment. If a source asset or column name matches any defined business term abbreviation, this abbreviation is used to expand the name.
• In the metadata enrichment results, you can now remove suggested display names or descriptions in bulk.

Enhanced management and scheduling of metadata enrichment jobs

• You can now configure execution windows for your metadata enrichment jobs to balance workloads. Jobs then run only within the configured time frames.
• On the new run metrics dashboard, you can monitor the progress of the individual enrichment tasks for an active metadata enrichment job run. In addition, you can explore run information for completed job runs to identify if and where issues occurred.

Enhanced data quality monitoring (IBM Knowledge Catalog and IBM Knowledge Catalog Premium)

You can now better target the data elements for monitoring of data quality:

• You can now configure data quality SLA rules without asset-level filters. The rules can be applied to any number of columns that have the same name or the same terms assigned, regardless of the containing data asset.
• You can now select and run data quality SLA rules as part of metadata enrichment. The rules are no longer enabled in the enrichment settings for the project.

Segment data assets by column values to focus on the information you need

You can now chunk data assets into smaller data assets based on selected column values to help you access only the data that you’re interested in. You can work with connected data assets in your project or directly select a data asset and column from a connection in your project without creating a connected data asset first.

Import, enrich, and assess data quality of data from additional data sources

You can now import metadata from Dremio data lakes, enrich that data, and assess its quality.

Simplify the importing of metadata to better understand your data

You can now import metadata by using a new experience that is integrated with IBM Manta Data Lineage service. The metadata import experience process is simplified and provides more lineage import configuration options, which can help you to understand how data flows in more detail.

IBM Knowledge Catalog, IBM Knowledge Catalog Premium, and IBM Knowledge Catalog Standard now store data in a Neo4j graph database

All editions of IBM Knowledge Catalog now use a Neo4j graph database to store lineage and relationship information. Neo4j provides greater data consistency while improving scaling and performance.

For new installations of IBM Knowledge Catalog, IBM Knowledge Catalog Premium, or IBM Knowledge Catalog Standard, the Neo4j graph database is installed automatically with the service.

Neo4j is the graph database that is used with the IBM Manta Data Lineage service. If you want to use the MANTA Automated Data Lineage service as your lineage service or if you want to enable the relationship explorer feature, you can enable the use of FoundationDB instead of Neo4j during installation or upgrade.

For details, see Determining the optional features to enable.

To read more about these features, see What's new and changed in IBM Knowledge Catalog in the IBM Cloud Pak for Data documentation.

Version 5.1.0 of the IBM Knowledge Catalog service includes various fixes.

For details, see What's new and changed in IBM Knowledge Catalog.

Navigate the service in a new way

You now use a new navigation menu to move between different tools and capabilities within IBM Match 360. The navigation options are improved and reorganized into a single menu. You can minimize the navigation menu to get more screen space to configure, view, and work with your master data. You can also now switch between different data types more easily while configuring or working with your master data.

Additionally, record and entity profiles are now enhanced to give you a clearer view of their associated attribute and relationship details.

IBM Match 360 now stores data in a Neo4j graph database

IBM Match 360 now uses a Neo4j graph database to store your master data records, entities, and relationships. Neo4j provides greater data consistency while improving scaling and performance.

For new installations of IBM Match 360 on Version 5.1, the Neo4j graph database is installed automatically with the service. For upgrades to Version 5.1 from previous releases, IBM Match 360 will continue to use FoundationDB and OpenSearch by default.

Master data entities can now be stored in the database

Data engineers can now configure IBM Match 360 to store and persist entity composite views in the graph database instead of composing them on demand. When an entity type is configured to persist, the composited attributes of each entity get stored in the database similar to the way that record attributes are stored, meaning that entity data is now more stable and resilient.

When entities are configured to persist, data stewards and business users can search directly on entity data, including supplementary attributes, audit attributes, and system properties such as record count and entity ID. Users can search for persisted entities by using the simple or advanced search mechanisms in the master data explorer interface.

As an additional benefit, searches and exports of persisted entity data are faster than was previously possible.

To read more about these features, see What's new and changed in IBM Match 360 in the IBM Cloud Pak for Data documentation.

Version 4.3.49 of the IBM Match 360 service includes various fixes.

For details, see What's new and changed in IBM Match 360.

Informix now uses Informix Dynamic Server 15.0

The Informix service has been upgraded to use Informix Dynamic Server 15.0, which is a major release that includes several enhancements in areas such as administration, ease of use, and storage.

Version 8.2.0 of the Informix service includes various fixes.

For details, see Fix list for Informix Server 14.10.xC6 release.

Version 42.8.0 of the MANTA Automated Data Lineage service includes various fixes.

For details, see What's new and changed in MANTA Automated Data Lineage.

This release of the MongoDB service include various fixes.

For details, see What's new and changed in MongoDB.

Use global search to find OpenPages records

You can now use global search in the search panel on the dashboard to find more records relevant to your search terms, and not only exact text matches.

For example, if you search for “management”, the search now finds records that contain all variations of the root word “manage”, such as “management”, “managements”, “manager”, and so on. Search results are ranked in order from most to least relevant.

Version 9.4.0 of the OpenPages service includes various fixes.

For details, see What's new and changed in OpenPages.

Streamline pipeline configuration by specifying settings at the project level

You can now configure pipeline settings at the project level to specify how all assets in the project are created. You can still configure settings, such as autosave, cache, and error policy settings, at the asset level. Any settings that you specify for an individual asset override the project settings.

Simplify job selection by adding multiple jobs to your canvas at the same time

You can now add jobs to pipelines in batch. You can drag the node Run multiple jobs from the node panel onto the canvas, then select one or more job assets, such as the Run DataStage job or the Run Pipelines job. All of the selected assets are added to the canvas with one click.

Version 5.1.0 of the Orchestration Pipelines service includes various fixes.

For details, see What's new and changed in Orchestration Pipelines.

Back up the Planning Analytics service online

You can now create online backups of the Planning Analytics service by using the backup and restore utilities on IBM Software Hub. Previously, you could create online backups only by using the Planning Analytics administration console.

For more information about how to create online backups of the IBM Software Hub cluster and how to restore from an online backup, see Backing up and restoring IBM Software Hub.

Updated versions of Planning Analytics software

This release of the service provides the following software versions:

• Planning Analytics Workspace Version 2.0.99

  For details, see 2.0.99 - What's new in the Planning Analytics Workspace documentation.

• Planning Analytics Spreadsheet Services Version 2.0.99

  For details, see 2.0.99 - Feature updates in the TM1 Web documentation.

• Planning Analytics for Microsoft Excel Version 2.0.99

  For details, see 2.0.99 - Feature updates in the Planning Analytics for Microsoft Excel documentation.

• TM1 Database Version 12.4.2 (formerly Planning Analytics Engine).

  For details, see What's new in TM1 Database Version 12 in the TM1 Database Version 12 documentation.

Version 5.1.0 of the Planning Analytics service includes various fixes.

For details, see What's new and changed in Planning Analytics.

Version 7.0.0 of the Product Master service includes various fixes.

For details, see What's new and changed in Product Master.

Version 10.0.0 of the RStudio Server Runtimes service includes various fixes.

For details, see What's new and changed in RStudio Server Runtimes.

Promote flows to deployment spaces

You can now directly promote SPSS Modeler flows from projects to deployment spaces without having to export the project and then import it into the deployment space.

Analyze Japanese text data in SPSS Modeler with Text Analytics

You can now use the Text Analytics nodes in SPSS Modeler to analyze text data that is written in Japanese. Text Analytics nodes use advanced linguistic technologies and text mining techniques to analyze text data and extract concepts, patterns, and categories.

Connect to new data sources with SPSS Modeler

You can now connect SPSS Modeler to the following new data sources for read and write access:

• Microsoft Azure Databricks
• Microsoft Azure Synapse Analytics

Use Kerberos for Apache Impala

You can now use Kerberos for authentication with an Apache Impala connector. However, when you use Kerberos authentication, you cannot use SQL Pushback.

Version 10.0.0 of the SPSS Modeler service includes various fixes.

For details, see What's new and changed in SPSS Modeler.

Connect to new data sources with Synthetic Data Generator

You can now connect Synthetic Data Generator to the following new data sources for read and write access:

• Microsoft Azure Databricks
• Microsoft Azure Synapse Analytics

Use Kerberos for Apache Impala

You can now use Kerberos for authentication with an Apache Impala connector. However, when using Kerberos authentication, you cannot use SQL Pushback.

For more information, see Creating Synthetic data in the IBM watsonx documentation.

To read more about these features, see What's new and changed in Synthetic Data Generator in the IBM watsonx documentation.

Version 10.0.0 of the Synthetic Data Generator service includes various fixes.

For details, see What's new and changed in Synthetic Data Generator.

Version 1.6.0 of the Voice Gateway service includes various fixes.

For details, see What's new and changed in Voice Gateway.

Version 5.1.0 of the Watson Discovery service includes various fixes.

For details, see What's new and changed in Watson Discovery.

Deploy multi-source SPSS Modeler flows

You can now create deployments for SPSS Modeler flows that use multiple input streams to provide data to the model.

Use GPU inferencing and create deep learning experiments with Watson Machine Learning

You no longer need Watson Machine Learning Accelerator to train deep learning experiments or use GPU inferencing. If you have the IBM Software Hub scheduling service and Watson Machine Learning installed, you can now train a neural network using the Deep learning experiment builder in Watson Studio and use the power of GPU inferencing. Train deeper neural networks and explore more complicated hyperparameter spaces.

New runtime available to deploy AI asset on s390x hardware

You can now use Runtime 24.1 to deploy AI assets on s390x hardware.

Version 5.1.0 of the Watson Machine Learning service includes various fixes.

For details, see What's new and changed in Watson Machine Learning.

Import model deployment configuration settings

When you’re adding deployments to configure evaluations for production models, you can now import the settings from your preproduction model deployment to provide model details.

Configure global explanations with LIME

You can now use the LIME (Local Interpretable Model-Agnostic explanations) algorithm to configure global explanations. To use LIME to configure global explanations, you must enable the global explanation parameter when you configure explainability.

Run quality evaluations with historical data

You can now use an API to evaluate historical feedback data for online deployments and prompt templates. By running quality evaluations with historical data, you can analyze your model performance over time with a wider scope.

Version 5.1.0 of the Watson OpenScale service includes various fixes.

For details, see What's new and changed in Watson OpenScale.

Version 5.1.0 of the Watson Speech to Text service includes various fixes.

For details, see What's new and changed in Watson Speech to Text.

Schedule jobs in Git-based projects

You can now schedule jobs within Git-based projects. You can set up scheduling when you create the job.

Use your IBM watsonx.data Spark engine with Jupyter notebooks in Watson Studio

If you have IBM watsonx.data and Watson Studio provisioned, you can now create new Python environment templates that are based on the Spark engine that runs on your IBM watsonx.data instance. Then you can run your code in a Jupyter notebook directly from the Watson Studio user interface. For more information, see watsonx.data documentation.

Version 10.0.0 of the Watson Studio service includes various fixes.

For details, see What's new and changed in Watson Studio.

Version 10.0.0 of the Watson Studio Runtimes service includes various fixes.

For details, see What's new and changed in Watson Studio Runtimes.

Install watsonx.ai on a single node OpenShift® (SNO) cluster

You can now install watsonx.ai in the full service or lightweight engine mode on a single node OpenShift (SNO) cluster if high availability and scalability is not a requirement. For details, see IBM Software Hub platform hardware requirements and Choosing an installation mode in IBM watsonx.ai.

New software specification for deploying custom foundation models

You can now deploy custom foundation models by using the new watsonx-cfm-caikit-1.1 software specification. This software specification is not available with every model architecture.

New model architectures for deploying custom foundation models

You can now deploy custom foundation models from the following model architectures with the vLLM runtime:

• Bloom
• Databricks
• exaone
• Falcon
• GPTJ
• Gemma
• Gemma2
• GPT_BigCode
• GPT_Neox
• GPTJ
• GPT2
• Granite
• Jais
• Llama
• Marlin
• Mistral
• Mixtral
• MPT
• Nemotron
• Olmo
• Persimmon
• Phi
• Phi3
• Qwen2

Deploy custom foundation models on MIG-enabled clusters

You can now deploy custom foundation models on a cluster with Multi-Instance GPU (MIG) enablement. MIG is useful when you want to deploy an application that does not require the full power of an entire GPU.

Review hardware requirements and configure MIG support to deploy your custom foundation models. For more information, see Requirements for deploying custom foundation models on MIG-enabled clusters.

Deploy custom foundation models on specific GPU nodes

You can now deploy custom foundation models on specific GPU nodes when you have multiple GPU nodes available for deployment. Review the process of creating a customized hardware specification to use a specific GPU node for deploying your custom foundation model.

Automate the building of RAG patterns with AutoAI

Use AutoAI to automate the retrieval-augmented generation (RAG) process for a generative AI solution. Upload a collection of documents and transform them into vectors that can be used to improve the output from a large language model. Compare optimized pipelines to select the best RAG pattern for your application.

Simplify complex documents by using the text extraction API

Simplify your complex documents so that they can be processed by foundation models as part of a generative AI workflow. The text extraction API uses document understanding processing to extract text from document structures such as images, diagrams, and tables that foundation models often cannot interpret correctly.

Chat with multimodal foundation models about images

Add an image to your prompt and chat about the content of the image with a multimodal foundation model that supports image-to-text tasks. You can chat about images from the Prompt Lab in chat mode or by using the Chat API.

Build conversational workflows with the watsonx.ai chat API

Use the watsonx.ai chat API to add generative AI capabilities, including agent-driven calls to third-party tools and services, into your applications.

Add contextual information to foundation model prompts in Prompt Lab

Help a foundation model generate factual and up-to-date answers in RAG use cases by adding relevant contextual information to your prompt as grounding data. You can upload relevant documents or connect to a third-party vector store that has relevant data. When a new question is submitted, the question is used to query the grounding data for relevant facts. The top search results plus the original question are submitted as model input to help the foundation model incorporate relevant facts in its output.

Work with new foundation models in Prompt Lab

You can now use the following foundation models for inferencing from the API and from the Prompt Lab in watsonx.ai:

• Granite Guardian 3.0 models in 2 billion and 8 billion parameter sizes
• Granite Instruct 3.0 models in 2 billion and 8 billion parameter sizes
• granite-20b-code-base-sql-gen
• granite-20b-code-base-schema-linking
• codestral-22b
• Llama 3.2 Instruct models in 1 billion and 3 billion parameter sizes
• Llama 3.2 Vision Instruct models in 11 billion and 90 billion parameter sizes
• llama-guard-3-11b-vision
• mistral-small
• ministral-8b
• pixtral-12b

For details, see Foundation models in watsonx.ai.

Work with new embedding models for text matching and retrieval tasks

You can now use the following embedding models to vectorize text in watsonx.ai:

• all-minilm-l12-v2

For details, see Foundation models in watsonx.ai.

Enhance search and retrieval tasks with the text rerank API

Use the text rerank method in the watsonx.ai REST API together with the ms-marco-minilm-l-12-v2 reranker model to reorder a set of document passages based on their similarity to a specified query. Reranking adds precision to your answer retrieval workflows.

For details about the ms-marco-minilm-l-12-v2 model, see Foundation models in watsonx.ai.

Review benchmarks that show how foundation models perform on common tasks

Review foundation model benchmarks to learn about the capabilities of foundation models deployed in watsonx.ai before you try them out. Compare how various foundation models perform on the tasks that matter most for your use case.

Configure AI guardrails for user input and foundation model output separately in Prompt Lab

Adjust the sensitivity of the AI guardrails that find and remove harmful content when you experiment with foundation model prompts in Prompt Lab. You can set different filter sensitivity levels for user input and model output text, and you can save effective AI guardrails settings in prompt templates.

To read more about these features, see What's new and changed in watsonx.ai in the IBM watsonx documentation.

Version 10.0.0 of the watsonx.ai service includes various fixes.

For details, see What's new and changed in watsonx.ai.

Conversational search analytics

You can now analyze the performance of your conversational search by using conversational search analytics. For more information, see Conversational search analytics.

Debug conversational search with Inspector

You can now debug conversational search issues using the Inspector in your watsonx Assistant. For more information, see Debugging failures for Conversational search or skill based actions.

Respond using a table format

The watsonx Assistant web chat can now provide responses in table format. The table response type is only available in the web chat. For more information, see Response types reference.

Pass values to skill-based actions

watsonx Assistant users can now pass values to skill-based actions within the conversation flow. To enable this feature, external skill providers must implement the new endpoint for skill-based actions. For more information, see Passing values to a subaction.

Version 5.1.0 of the watsonx Assistant service includes various security fixes.

For details, see What's new and changed in watsonx Assistant.

Version 5.1.0 of the watsonx Code Assistant for Red Hat Ansible Lightspeed service includes various fixes.

For details, see What's new and changed in watsonx Code Assistant for Red Hat Ansible Lightspeed.

Version 5.1.0 of the watsonx Code Assistant for Z service includes various fixes.

For details, see What's new and changed in watsonx Code Assistant for Z.

Data sources, catalogs, and storage enhancements

This release includes the following data sources, catalogs, and storage enhancements:

• Now, you can connect to Apache Phoenix data sources. For more information, see Apache Phoenix.
• You can create or add a new data source to the engine without attaching a catalog to it. You can attach a catalog to the data source at a later stage.

Version 2.1.0 of the watsonx.data service includes various fixes.

For details, see What's new and changed in watsonx.data.

Enhancements for governance management

These features extend the way you can extend and manage governance activity.

Distribute governance activities across multiple clusters

You can now distribute governance activities across multiple servers and sync data between the remote servers to a primary governance cluster. Use this capability to isolate production assets on one server and control access to them for greater security.

For details, see Managing multiple clusters for watsonx.governance.

Track governance activity for custom models and tuned models

You can now extend governance to include tracking prompt templates for custom foundation models and tuned models. You can capture the data for the prompt templates, including evaluation results, in a factsheet as part of an AI use case.

Enhancements for asset evaluation

Use these features to improve the quality of your asset evaluations and drive decision-making.

Run quality evaluations with historical data

You can now use an API to evaluate historical feedback data records for online deployments and prompt templates to analyze your model performance over time with a wider scope.

Configure generative AI quality evaluations with LLM-as-a-judge

When you configure generative AI quality evaluations, you can now configure settings to calculate metrics with LLM-as-a-judge models. LLM-as-a-judge models are LLM models that you can use to evaluate the performance of other models.

Import model deployment configuration settings

When you’re adding deployments to configure evaluations for machine learning models in production, you can import settings from your pre-production model deployment to provide model details for evaluations.

Configure global explanations with LIME

You can now use the LIME (Local Interpretable Model-Agnostic explanations) algorithm to configure global explanations for machine learning models. To use LIME to configure global explanations, you must enable the global explanation parameter when you configure explainability.

Enable GPU metrics computation

You can use detectors on GPUs to increase the speed of data safety, answer quality, and retrieval quality metric computations.

For details, see Enabling GPU metrics computation.

Enhancements for the governance console

Use these features to better manage your use cases in the Governance console.

Add custom tabs to the governance console dashboard

You can now add up to three custom tabs to the dashboard. For example, you might want to use custom tabs to group related panels and widgets in separate places on the Home page.

Create stacked bar charts in the governance console

You can now configure a stacked bar chart on the dashboard and in the View Designer panel. Use a stacked bar chart to compare the proportional contributions of each item to the total within a category. For example, for the Model object type, you might create a stacked bar chart that shows risks grouped by Model Category and stacked by Computed Tier.

Use expressions to set field values based on a respondent's questionnaire answers

When you create response actions in governance console, you can now enter an expression for the value of a field. For example, you can enter the following values:

• [$TODAY$] for the current date
• [$END_USER$] for the name of the signed on user
• [$System Fields:Description$] to set the field to the value of the Description field of the object

Enhancements to the watsonx.governance Model Risk Governance solution

This release includes the following enhancements:

New Model Group object type

Groups similar models together. For example, versions of a model that use a similar approach to solve a business problem might be grouped under a Model Group object.

New Use Case Risk Scoring calculation

Aggregates metrics by breach status into risk scores to give an overall view into how the underlying models of a use case are performing.

New Discovered AI library business entity

Provides a default place to store any AI deployments that are not following sanctioned governance practices within an organization (also known as “shadow AI”).

To read more about these features, see What's new and changed in watsonx.governance in the IBM watsonx documentation.

Version 2.1.0 of the watsonx.governance service includes various fixes.

For details, see What's new and changed in watsonx.governance.

Create projects in skill studio to automate complex tasks and processes

As a builder, you can create projects in Skill studio and publish them as skills to the Skills and apps page, where you enhance the skills and make them available in the skill catalog. You can use the skill by entering a phrase in the watsonx Orchestrate chat or by adding these skill as actions on AI assistants. You can create skills from types like workflows, decision models, and generative AI responses. For details, see https://www.ibm.com/docs/en/watsonx/watson-orchestrate/current?topic=building-projects.

Use formatting for skill descriptions

As a builder, you can create skills that have skill input descriptions in plain text. You can format any of the descriptions by using bold, italics, underline, and you can add hyperlinks.

Use slot logic for validations

As a builder, when you enhance a conversational skill, you can specify validations on the inputs (slot filling) that are obtained from the nonlinear multi-turn conversation interaction.

Version 5.1.0 of the watsonx Orchestrate service includes various fixes.

For details, see What's new and changed in watsonx Orchestrate.

IBM Manta Data Lineage is a new service that is available with IBM Software Hub 5.1.0.

IBM Manta Data Lineage helps you understand how data moves across your systems, revealing data origins, dependencies, and transformations. With deep visibility into complex data flows, you can demonstrate compliance, improve change management practices, and make informed decisions with confidence.

You can access your imported lineages in the Data lineage workspace, or you can view the lineage for a specific asset on the Catalogs or Projects pages.

IBM StreamSets is a new service available with IBM Software Hub 5.1.0.

With the IBM StreamSets service, you can build streaming data pipelines to act on time-sensitive data and enhance real-time decision making.

Data engineers use IBM StreamSets to build, run, and monitor streaming data pipelines that access and connect data across various types of data sources.

IBM watsonx Code Assistant is a new service available with IBM watsonx Code Assistant 5.1.0.

watsonx Code Assistant is a generative AI coding companion that provides contextually aware assistance for programming languages such as Go, C, C++, Java, JavaScript, Python, and TypeScript. You integrate watsonx Code Assistant into your IDE, and it helps simplify your coding tasks.

Use watsonx Code Assistant to:

Generate code suggestions

Enter a prompt that describes the code you need, and watsonx Code Assistant generates something you can choose to use.

Modernize Java applications

You can modernize your WebSphere Application Server traditional application to Liberty.

Document code

Generate comment lines that document what your code does.

Generate unit tests

Generate a test for a class, file, function, or method.

Explain code

Analyze and summarize your code to understand what the code does.

To read more about these features, see What's new and changed in watsonx Code Assistant in the IBM watsonx documentation.

• Version 4.12 or later fixes
• Version 4.14 or later fixes
• Version 4.15 or later fixes
• Version 4.16 or later fixes
• Version 4.17 or later fixes

For more information, see:

• Storage considerations
• Cloud deployment environments

For more information, see:

• Storage considerations
• Cloud deployment environments

Previously, you had to run multiple commands to set up an instance of IBM Cloud Pak for Data. Starting in IBM Software Hub Version 5.1.0, you can use the new cpd-cli manage setup-instance command to install:

• IBM Cloud Pak foundational services operators and custom resources
• IBM Software Hub operators
• IBM Software Hub custom resources

For more information, see Installing the required components for an instance of IBM Software Hub.

The new cpd-cli manage setup-instance command includes an option named --run_storage_tests. You can use the --run_storage_tests option when you install or upgrade IBM Software Hub.

Storage performance is critical to the functionality of and performance of IBM Software Hub. For more information, see:

• Installing the required components for an instance of IBM Software Hub
• Upgrading to IBM Software Hub (Upgrading from Version 4.8 to Version 5.1
• Upgrading to IBM Software Hub (Upgrading from Version 5.0 to Version 5.1)

• cpd-cli health cluster
• cpd-cli health nodes
• cpd-cli health network-performance

The installation and upgrade documentation include guidance on running these commands.

The installation and upgrade documentation include guidance on running these commands.

Starting in IBM Software Hub Version 5.1, new installations use only the cpd route, which means you only need to manage certificates for a single route. All calls that were previously passed to the cp-route are now directed to the cpd route.

If you upgrade to IBM Software Hub Version 5.1, you can optionally unify the cpd and cp-console routes. For more information, see Unifying the cpd and cp-console routes.

If you are upgrading to IBM Software Hub Version 5.1, you can continue to use the IBM Certificate manager, provided by IBM Cloud Pak foundational services.

Use the new cpd-cli manage setup-instance command to install and upgrade an instance of IBM Software Hub.