The Amazon Bedrock integration enables leverage of enterprise foundation large language models directly into UAC workflows, creating intelligent automation solutions capable of processing, generating, and transforming content. Integrating AWS Bedrock's AI capabilities with the automation infrastructure allows the development of systems that merge UAC's robust orchestration framework with advanced natural language processing and reasoning.This integration opens up a wide range of automation possibilities, from analyzing logs and generating human-readable summaries of complex system states, to intelligently routing and classifying data, transforming unstructured information into structured formats, or even making context-aware decisions within workflows.The integration provides native support for AWS Bedrock foundation models, offering versatility in AI model deployment and management. Users can enhance prompts using file inputs or UAC Variables, while outputs are stored for potential subsequent reuse across tasks and workflows, allowing them to create chained AI operations or distribute intelligence throughout the automation environment.Key Features Sending prompts to foundation models through the AWS Bedrock Converse API. Offer for consistent interface across multiple Bedrock-supported models. Handling of prompt-based inputs and model-generated responses. Support for text responses and JSON outputs with schema enforcement via Tool Use. Storing the LLM response as a local file.Enriching prompts with data from files and UAC Variables.Configuring response behavior using Advanced Options (temperature, top‑p, and max tokens).

AWS Elastic Kubernetes Service (EKS) is used to deploy and manage containerized applications. Minimal container orchestration expertise is required to use. EKS is considered an ideal platform for the deployment and management of containerized applications that require high availability, scalability, and portability. It is also used for deploying applications across multiple regions, utilizing open-source tools, and integrating with existing DevOps tools.This integration provides the capability to trigger, control and manage Kubernetes Jobs and Cronjobs on a specified EKS cluster. Key FeaturesDeploy Job and Cronjob resources, either declaratively or imperatively, from local YAML or JSON files, remote URLs or UAC scripts.Create one-off Job resources based on existing Cronjob definitions in the cluster.Delete Job and Cronjob and resources via the delete action or manually through a Dynamic Command. Job resources can also be set to be deleted automatically after completion.Live stream real-time updates about Kubernetes pods as they are created, modified, or terminated.Option to automatically or manually retrieve information, including logs, for all containers pertaining to a specific job.Whats NewEnhancementsAdded: Improved compatibility with future agent releases.Added: Improved packaging logic which reduces extension file size.Fixes:Fixed: Issue where environment variables were not being sent during dynamic choice and dynamic command execution.

Apache Airflow is an open-source platform created to programmatically author, schedule, and monitor workflows. This Universal Extension provides the capability to integrate with an Apache Airflow Standalone Server or Google Cloud Composer Airflow and use it as part of your end-to-end UC workflow, allowing high-level visibility and orchestration of data-oriented jobs or pipelines. You gain many enterprise-level benefits by using the Universal Automation Center to control Airflow. Notable benefits include: Run your Airflow data pipelines in realtime by using event-based triggers from the UC Add Airflow tasks to broader business process workflows in the UC Gain observability of your Airflow workflows - with proactive alerts from the the UC Read this article to learn more about the benefits of integrating Airflow with the Universal Automation Center. Key-Features This Universal Extension provides the following key features:ActionsTrigger a DAG run and optionally wait until the DAG was reached "success" or "failure". Information retrieval of a specific DAG Run. Information retrieval for a task that is part of a specific DAG Run.AuthenticationBasic authentication for Stand Alone Airflow Server. Service Account Private Key for Google Cloud Composer.OtherCapability to use HTTP or HTTPS proxy instead of direct communication to Stand Alone Airflow Server. What's new v 2.0.0 The latest release provides the ability to integrate with Google Cloud Composer Airflow Server. Furthermore, provides the ability to configure Action "Trigger DAG Run", by passing JSON configuration parameters.

Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. A Kafka Event Monitor is a Universal Extension responsible for monitoring events (messages) from topics in Kafka, and consuming an event based on filtering criteria via consumer group subscription.Key FeaturesThis Universal Extension supports the following main features:Monitor messages and consume a Kafka message by consumer group topic subscription.Authenticate against Kafka using PLAINTEXT, SASL_SSL or SSL security protocolCapability to filter Kafka messages based on the value of the message. Number, String and JSON filter patterns are supported.Capability to fetch dynamically Kafka topics, during task creation.Capability to control the partition assignment strategy, as well as session-related timeout valuesWhat's New in Version 1.2.0EnhancementsUpgraded kafka-python to version 2.1.6, bringing major enhancements in stability, performance, and compatibility, along with numerous bug fixes.Added support for PLAIN mechanism under SASL authentication.Added environment variables for configuring Kafka connection and polling parameters.Added validation step after initializing a consumer, for improved stability and robustness.Improved consumer shutdown handling to ensure tasks terminate gracefully without leaving open connections.Improved log messages.Optimized the message retrieval logic for better efficiency.Optimized the pipeline stages flow for better efficiency.FixesFixed bug where the last message was not commited when consuming multiple messages.

Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. This integration is responsible for publishing events (messages) and topics in Kafka. Key Features: This Universal Extension supports the following main features: Perform authentication towards Kafka, using PLAINTEXT or SASL_SSL security protocol Send a message to Kafka with the capability to select the topic, the partition, the message key, the message value and message metadata Capability to control the transport of the messages by configuring the message acknowledgment strategy and the request timeout Capability to fetch dynamically topics and partitions from kafka for selection during task creation Capability to automatically select the serialization method depending on the key/value message data types What's New in V2.0.0 This release modernizes dependencies with native Confluent Python support, and introduces SASL authentication via PLAIN.

The AWS Command Line Interface (AWS CLI) is an open source tool that enables you to interact with AWS services using commands in your command-line shell. With minimal configuration, the AWS CLI enables you to start running commands that implement functionality equivalent to that provided by the browser-based AWS Management Console from the command prompt in your terminal program. This integration provides an interface to call the AWS CLI and enables users to execute AWS CLI tasks in UAC.Key FeaturesThis Universal Extension provides the following key features.Execute AWS CLI on UAC.Ability to provide configuration options through UAC scripts.

AWS Mainframe Modernization service is an AWS cloud-native platform to migrate, modernize, execute, and operate mainframe applications within a fully-managed runtime. Stonebranch Universal Automation Center (UAC) works with AWS Mainframe Modernization to run both batch schedules and event-based automation on AWS. Using the Stonebranch AWS Mainframe Modernization extension, enterprises may run automation in both the mainframe and on AWS simultaneously. Key Features: The ability to schedule, automate and execute mainframe batch jobs running in AWS Mainframe Modernization Service via the Stonebranch Universal Controller Monitor Application’s batch processes from the Stonebranch Universal Controller Start/Restart/Rerun/Cancel batch processes automatically or manually from the Stonebranch Universal Controller Synchronous and asynchronous batch execution from Stonebranch Universal Controller Retrieve the results of the AWS Mainframe Modernization Batch processes Capture the AWS Cloudwatch logs of the executed jobs in Stonebranch Universal Controller More information: Article: Mainframe Modernization - How Automation Makes it Possible What's New In 1.4.0: BluAge support added Log Fetching Methods updated

Step Functions is a serverless orchestration service that lets you combine AWS Lambda functions and other AWS services to build business-critical applications. Through Step Functions graphical console, you see your application’s workflow as a series of event-driven steps. This integration allows customers to execute AWS Step Functions from Universal Controller. Key Features: This Universal Extension provides the following key features.Actions Execute a AWS Step Function and wait until is reaches status "Success" or "Failed". Execute a AWS Step Function asynchronously without waiting for the execution to finish.  AuthenticationAuthentication using AWS CredentialsAuthorization via IAM Role-Based Access Control (RBAC) strategy.OtherCommunication through Proxy with use of HTTP or HTTPS. What's New V2.0.0Breaking Change: This version supports Universal Agent and Universal Controler from version 7.2 onwards. Customers that used older versions of this integration on Universal Controller and Universal Agent of versions <= 7.2 are required to upgrade to use the new functionality introduced by 2.0.0. Added: A suffix is added to the Execution Name before AWS Service is called. That suffix represents the execution count for a specific task instance, therefore providing uniqueness of the Execution Name. Fix: The Extension Output attribute “fields.invocation.execution_name” is corrected to represent the user input after the resolution of UC Functions and variables

Azure Kubernetes Service (AKS) is a managed Kubernetes service that you can use to deploy and manage containerized applications. You need minimal container orchestration expertise to use AKS. AKS reduces the complexity and operational overhead of managing Kubernetes by offloading much of that responsibility to Azure. AKS is an ideal platform for deploying and managing containerized applications that require high availability, scalability, and portability, and for deploying applications to multiple regions, using open-source tools, and integrating with existing DevOps tools.This integration provides the capability to define and deploy AKS Jobs, CronJobs, as well as delete them on a specified Azure-powered Kubernetes cluster.Key FeaturesDeploy Job and Cronjob resources, either declaratively or imperatively, from local YAML or JSON files, remote URLs or UAC scripts. Create one-off Job resources based on existing Cronjob definitions in the cluster. Delete Job and Cronjob resources via the delete action or manually through a Dynamic Command. Job resources can also be set to be deleted automatically after completion. Live stream real-time updates about Kubernetes pods as they are created, modified, or terminated. Option to automatically or manually retrieve information, including logs, for all containers pertaining to a specific job.Whats NewEnhancementsAdded: New environment variable UE_JOB_FINALIZATION_POLL_INTERVAL to configure the polling interval for job finalization checks.Added: New environment variable UE_RESOURCE_DEFINITION_LINK_GIT_PROVIDER to allow configuration of the Git provider used for retrieving Kubernetes job resource definitions.Added: Improved compatibility with future agent releases.FixesFixed: Improved handling of job finalization checks to ensure completion is detected accurately and prevent timeout errors.Fixed: Issue where environment variables were not being sent during dynamic choice and dynamic command execution.

The Azure OpenAI Integration brings the power of enterprise-grade large language models into your UAC workflows, enabling intelligent automation that can understand, generate, and transform content. By connecting your automation workflows to Azure-hosted AI models, you can build solutions that combine the reliability and orchestration capabilities of UAC with the reasoning and language capabilities of modern LLMs.This integration opens up a wide range of automation possibilities: from analyzing logs and generating human-readable summaries of complex system states, to intelligently routing and classifying data, transforming unstructured information into structured formats, or even making context-aware decisions within your workflows.The integration works with both Azure OpenAI Service and Azure AI Foundry platforms, giving you flexibility in how you deploy and manage your AI models. Prompts can be enriched by files or UAC Variables and outputs can be persisted for reuse across multiple tasks and workflows—enabling you to chain AI operations together or share insights across your automation ecosystem.Key FeaturesBuilt on the OpenAI V1 API standard for long-term compatibility and seamless model upgrades.Works with both Chat Completions API and the newer Responses API for comprehensive LLM interaction patterns.Platform-flexible design supporting models from Azure OpenAI Service and Azure AI Foundry.Enrich prompts with data from files and UAC Variables, and save outputs for use throughout your automation ecosystem.Configurable data retention controls via "Allow Conversation Data Retention parameter", with support for zero data retention when used with Modified Abuse Monitoring.Structured output support with JSON schemas for predictable, machine-consumable responses on models that support this capability.What's New 1.1.0:EnhancementsAdded: Improved support for upcoming agent releases.Added: Importable configuration examples.

The Universal Task for BatchMan provides an integration between Universal Automation Center (UAC) and Honico BatchMan, enabling unified scheduling, automated job and net execution, and centralized monitoring of Honico BatchMan jobs within Universal Automation Center.The Universal Task integrates with SAP BatchMan by invoking RFC-enabled BatchMan function modules exposed as SOAP web services. These function modules are published as Enterprise Services using SAP’s Web Service Framework (SOAMANAGER). The Universal Task securely calls these function modules to control and manage BatchMan-configured SAP jobs and nets.Note:This approach requires only the activation and configuration of the relevant Enterprise Services in SOAMANAGER on the customer’s SAP system; no transport is required.Key CapabilitiesThe integration exposes the following key capabilities:Select a BatchMan Job or Net – Dynamically select a BatchMan job or net at runtime.Start a Job or Net – Trigger BatchMan jobs and nets directly from UAC.Check Job Status – Monitor real-time execution status of jobs and nets.Retrieve Job Logs – Access execution logs for auditing and troubleshooting.Retrieve SAP Spool Files – Display the SAP spool output of each job step in the task STDOUT.Finish a running order - Finish a running order via a Universal Extension CommandStop and Start an order - Stop and Start an order via two Universal Extension Commands.This solution provides a secure and scalable interface for hybrid workflow automation, allowing customers to continue leveraging BatchMan for SAP job scheduling while orchestrating end-to-end workflows that include both SAP and non-SAP tasks within UAC. The use of SAP Enterprise Services ensures a standardized, maintainable, and vendor-supported integration with SAP systems.

Beta DocX and Beta LogX together provide a comprehensive, enterprise-wide solution for the management, storage, and processing of documents and system logs. They enable centralized access, secure storage, audit-proof archiving, and intelligent handling of both high-volume documents and cross-platform log data. Beta DocX focuses on document management and output, including integration with SAP and mainframe systems, mass printing, and workflow-based document processing. Beta LogX provides centralized log management, full-text search for forensics, and compliance monitoring across diverse systems such as mainframe, Windows, Linux/Unix, and SAP applications. The Beta UX Universal Task is a Beta UX integration that sends Task Instance logs into Beta LogX, enabling automated collection, processing, and enterprise-wide availability of job logs. The Collection of the Task Instance Log Information is triggered in real-time using the Universal Controller inbuild Web Hook. Currently, this Universal Task only supports Beta LogX, but in the future it will also support Beta DocX, allowing seamless processing and archiving of both log data and documents through the same integration framework. Key Features: The Beta UX Universal Task sends the Task Instance details including any generated log files to Beta LogX. Execute a Beta UX Integration Process in Real-time (Webhook triggered) Monitor a Beta UX Integration Process in Real-time Backlog Processing What's New Feature Backlog Processing added New Version of Current Integration

dbt Cloud is a scalable data transformation platform that helps data teams build, deploy, monitor, and discover data assets, providing a browser-based UI for developing, testing, scheduling, documenting, and investigating data models.This integration enables users to trigger and monitor their dbt Cloud Jobs directly from UAC. Key Features: Trigger and Monitor dbt Cloud Jobs with the capability to provide optional configuration run settings. Real‑Time Job Monitoring that provides continuous feedback displaying detailed Job and Step status updates throughout the execution lifecycle. What's New: Trigger and Monitor dbt Cloud Jobs with the capability to provide optional configuration run settings.

OverviewDownload the IntegrationThis extension collects UAC metrics via Dynatrace ActiveGate and turns them into a UC-aware view inside Dynatrace. It discovers Universal Controllers, OMS servers, Universal Agents, workflows, and business services from metric labels and maps them into a topology with dedicated Unified Analysis Screens and example dashboards. IT Ops, SREs, and platform teams can use this extension to:Track task execution and workflow outcomes.Monitor controller, JVM, and process health.Watch UAC license consumption and capacity limits.Receive UAC-specific alerts when OMS, agents, or licenses are in trouble.All of this is done through metric scraping---no custom scripting is required.Use casesOverview of your entire UAC environment: Get an overview of one or more Universal Controllers with an all-in-one view inside of Dynatrace.Controller and OMS health monitoring: Detect controller node or OMS issues early using status and connection metrics.Automation workload visibility: Monitor execution counts, active instances, and outcome distributions across workflows and task types.License and capacity management: Track usage of agents, nodes, executions, and tasks against license quotas to avoid overages.Workflow- and agent-centric views: Use workflow and Universal Agent entity screens to understand where tasks run, how they perform, and which business services they belong to.Integrated troubleshooting inside Dynatrace: Use dashboards, entity screens, and alerts to quickly isolate whether a problem is in the automation logic, the controller, the agents, or the underlying infrastructure.

This task has been replaced by UAC Utility: Email Monitor (IMAP) & UAC Utility: Email (SMTP) The Universal Task for E-Mails allows sending and retrieving E-Mails and E-Mail attachments. It also provides the functionality to download mail attachments to a mail folder.This Universal Task is especially useful for Stonebranch SaaS customers, which access Universal Controller in the Stonebranch AWS Cloud and have their Universal Agents deployed in their own data center. Since the Universal Task is triggered from the Universal Agent, no additional firewall ports need to be opened. Key Features Send an E-Mail with or without attachments. Use Universal Controller Variables. Retrieve an E-Mail based on filter criteria like From, To, sender, subject, body content. Move an E-Mail to a Mail folder after downloading it to a configured folder. Delete an E-Mail after downloading it to a configured folder.

Fivetran is a cloud-based data integration platform that helps businesses automate the process of extracting data from various sources, transforming it, and loading it into a data warehouse for analysis. It offers a wide range of pre-built connectors to popular data sources such as databases, marketing platforms, CRMs, and more. Fivetran's connectors are designed to be easy to set up and use, allowing businesses to focus on data analysis rather than data integration. The platform also offers features such as data transformation, scheduling, monitoring, and access control to help organizations manage their data pipelines more efficiently. Fivetran supports a variety of data warehouses, including Amazon Redshift, Google BigQuery, Snowflake, and Microsoft Azure. This integration allows to create and execute Fivetran Tasks in Universal Controller.   Key Features: Trigger a Sync action on a Connector. Optionally, wait until Sync is completed. Trigger a Re-sync (Historical Sync) action on a Connector. Optionally, wait until Re-sync is completed. What's New in V1.0.1:A fix is provided to execute the integration with a Python installation, other than the one that is bundled with the Universal Agent.

Google Kubernetes Engine (GKE) is a service for deploying, managing, and scaling Kubernetes containers on Google Cloud. Among other capabilities, GKE supports Jobs and CronJobs for task execution. Jobs are used for one-time tasks that run to completion, such as batch processing, while CronJobs enable scheduled, recurring task execution. To deploy a Job or CronJob, a Kubernetes resource definition is used. This file is typically defined inside of a YAML or JSON file and specifies the task details, container image, and execution configuration. Once applied to a GKE cluster, the Job runs to completion and terminates automatically, while CronJobs create new Job instances according to their defined schedule.This integration provides the capability to define and deploy GKE Jobs and CronJobs on a specified cluster or delete them from a namespace.Key FeaturesDeploy Job and Cronjob resources, either declaratively or imperatively, from local YAML or JSON files, remote URLs or UAC scripts. Create one-off Job resources based on existing Cronjob definitions in the cluster. Delete Job and Cronjob resources via the delete action or manually through a Dynamic Command. Job resources can also be set to be deleted automatically after completion. Live stream real-time updates about Kubernetes pods as they are created, modified, or terminated. Option to automatically or manually retrieve information, including logs, for all containers pertaining to a specific job.What's newEnhancementsAdded: New environment variable to configure the polling interval for job finalization checks.Added: New environment variable to allow configuration of the Git provider used for retrieving Kubernetes job resource definitions.Added: Improved compatibility with future agent releases.FixesFixed: Improved handling of job finalization checks to ensure completion is detected accurately and prevent timeout errors.Fixed: Issue where environment variables were not being sent during dynamic choice and dynamic command execution.

The Google Vertex AI Integration brings the power of enterprise-grade large language models into your UAC workflows, enabling intelligent automation that can understand, generate, and transform content. By connecting your automation workflows to Google-hosted AI models, you can build solutions that combine the reliability and orchestration capabilities of UAC with the reasoning and language capabilities of modern LLMs.This integration opens up a wide range of automation possibilities: from analyzing logs and generating human-readable summaries of complex system states, to intelligently routing and classifying data, transforming unstructured information into structured formats, or even making context-aware decisions within your workflows. Prompts can be enriched by files or UAC Variables and outputs can be persisted for reuse across multiple tasks and workflows—enabling you to chain AI operations together or share insights across your automation ecosystem.Key FeaturesWorks with Chat Completions API for comprehensive LLM interaction patterns.Platform-flexible design supporting MaaS models from different providers.Enrich prompts with data from files and UAC Variables, and save outputs for use throughout your automation ecosystem.Structured output support with JSON schemas for predictable, machine-consumable responses on models that support this capability.Provides "Advanced Options" to tinker the behavior of the selected model, like increasing the determinism of the response, penalizing repetition and others.

Guidewire is a core insurance software platform used by insurers to manage policy, claims, and billing operations. Guidewire Batch refers to its background processing framework that executes scheduled or high-volume tasks automatically without user interaction. The integration enables execution and management of Guidewire batch processes directly from Stonebranch Universal Automation Center (UAC) via the SOAP protocol.Functionality Implemented for Guidewire IntegrationThe following capabilities are implemented:Fetch a batch process nameRun a batch job by batch process name Terminate a batch process by name The integration also supports dynamic fetching of available batch processes from Guidewire into UAC, as well as dynamic command execution to terminate Guidewire batch processes. Key Features This Universal Task provides the following key features: Actions Fetch a batch process name Run a batch job by batch process name Terminate a batch process by name AuthenticationBasic Authentication Input / Output Capability to print the XML request body to STDERR Capability to output the run process ID and batch status

Note: This integration replaces Informatica Cloud: Schedule, Control, and Manage. Informatica Cloud is a cloud-native data management platform that enables organizations to integrate, transform and orchestrate data across on-premises and multi-cloud environments. This integration allows users to trigger and control Informatica Cloud Taskflows, Processes, and Jobs from UAC. Key Features: Trigger and Monitor an Informatica Cloud Taskflows with capability to optionally provide required input and fault action strategy. Trigger Processes in a synchronous way with capability to provide optionally input for the executed process. Trigger and Monitor Linear Taskflow Job, Synchronization Task, PowerCenter Task, Replication Task and Mapping Task.  Flexibility in providing input as JSON Text or JSON Script Flexibility to provide Activity Log and Session Log on STDOUT and Activity Log on EXTENSION Output. It is also possible for Users to retrieve Activity Logs on demand when required.

This Universal Extension provides the capability to monitor objects (files/directories) on various major Cloud Providers, including AWS, Google Cloud, Microsoft Azure and trigger Universal Events, which can be used for execution of tasks and workflows. It can be executed as a standalone UAC Task, however in order to benefit from the full functionality and scalability of UAC, it is suggested to be used in conjunction with Universal Monitor Triggers and Universal Monitor Tasks. Multiple storage systems are supported (an overview can be found here). Integrations within this solution package include: Amazon S3 Google Cloud Storage Microsoft OneDrive Business, including Sharepoint Microsoft Azure Blob Storage Hadoop Distributed File Storage (HDFS) Local file system (Linux, Windows) HTTP(S) URL Key Features: This Universal Extension supports the following key features: Features Monitor Object Creation, Change (based on modification time) and Deletion using advanced filtering capabilities Generates Universal Events for each entity the monitor has detected, which can be used to trigger other tasks and workflows. Large set of possible parameterization and coverage of remote storages. Plug & Play, easy installation without the need to install external dependencies. Dynamic token updates for OneDrive Business cloud storage, observing the OneDrive business refresh token flow. Enhanced observability with event metrics, providing users with detailed statistical insights into events processed across all task instances. Shared configuration with Cloud Data Transfer. What's New v 2.4.0 EnhancementsUpdated bundled binaries, bringing various improvements and fixesEnable the use of FTP with TLS/SSL certificates

This Universal Extension provides the capability to perform data transfers between cloud based storage services, as well as local or distributed file systems. Transfers are fast and secure since data are streamed from one storage to another with no intermediate storage taking place.Multiple storage systems are supported (an overview can be found here). Integrations within this solution package include: Amazon S3 Google Cloud Storage Microsoft OneDrive Business, including Sharepoint Microsoft Azure Blob Storage Hadoop Distributed File Storage (HDFS) Local file system (Linux, Windows) HTTP(S) URL Key Features This Universal Extension supports the following key features: Actions Copy, move, synchronize data between two storages. Copy a URL's content and to cloud or local destination without saving it in temporary storage. List data on a storage, including listing with details or in JSON format for machine parsing. Create objects on a storage. Delete objects from a storage. Features: Fast transfers for objects stored in the same region. Preserves always timestamps and verifies checksums. Supports encryption, caching, compression, chunking. Dynamic token updates for OneDrive Business cloud storage, observing the OneDrive business refresh token flow. Support for dry runs. Allows users to execute a Universal Task without making any permanent changes on the target storage. Advanced filtering capability for files or objects to be listed or transferred. Option to mark the Universal Task as Failed when no files have been transferred. List of overwrite options for existing data. Additional customized options. Observability capabilities, providing users with detailed statistical insights for data transfers across all task instances. Output Progress of the selected Action is visible, during Universal Task Instance execution. Text or JSON formatted output. What's New V 4.5.0 Enhancements Updated bundled binaries, bringing various improvements and fixesEnable the use of FTP with TLS/SSL certificatesarious improvements. Fixes Time parsing issues on systems using different date formats.

Microsoft Power BI is a Business Intelligence Tool. Power BI users can connect to and combine data from multiple data sources through a secure interface, creating Power BI datasets. Additionally, users may combine and transform collections of tables into a data pipeline, creating Power BI dataflows. This integration provides the capability to perform refresh actions on Microsoft PowerBI Datasets and Dataflows. A typical Use Case is when UAC is used as a Data Pipeline orchestrator, where the last step of a workflow could be the refresh of a Microsoft Dataset or Dataflow.Key FeaturesCapability to execute refresh commands for Datasets or Dataflows and monitor their status What's New 1.3.0: Enhancements Added: Detection of dataflow refreshes that result in 'Unknown' status now immediately reported as failures.Added: Improved support for upcoming agent releases.

SQL Server Integration Services (SSIS) is a platform for building data integrations and data transformation solutions.This Universal Extension task interactively allows users to list and select the SSIS Folder, Project, Environment Reference, and SSIS Package while creating the job. Furthermore, it can trigger the SSIS package execution in the Microsoft SQL server, monitor the SSIS Package execution, and fetch SSIS logs to Universal Controller when the SSIS package execution has been completed. Key Features: This Universal Extension provides the following key features:Dynamic Choice Fields to select the SSIS Folder/Project/Package/Environment Reference ID.Launch SSIS Package execution. Monitor SSIS Package execution.Fetch SSIS Package execution logs.SSIS Execution ID and Execution status captured for every execution in the Task Instance.Connection to MS SQL Server is done via the Python PYMSSQL module.Supports Windows and Linux Universal Agents in order to connect to the MSSQL server. What's in V1.0.4This new release involves a minor bug fix.