The Amazon S3 Cloud Storage Bucket File Transfer integration allows you to securely automate file transfers from, to, and between Amazon S3 cloud storage buckets and third-party application folders.Storing data in the cloud becomes an integral part of most modern IT landscapes. With Universal Automation Center (UAC), you can securely automate your AWS tasks and integrate them into existing scheduling workflows. Key Features: Automate file transfers in real-time. Drag-and-drop as a task into any existing scheduling workflow within the UAC. File Transfers can be triggered by a third-party application using the UAC RESTfull web service API: REST API. The following file transfer commands are supported: Upload file(s) to an S3 bucket. Download file(s) from an S3 bucket. Transfer files between S3 buckets. List objects in an S3 bucket. Delete object(s) in an S3 bucket. List S3 bucket names. Create an S3 bucket.  Additional Info: Security is ensured by using the HTTPS protocol with support for an optional proxy server. Supports AWS IAM Role-Based Access (RBCA). No Universal Agent needs to be installed on the AWS Cloud – the communication goes via HTTPS.AWS canned ACLs are supported, e.g., to grant full access to the bucket owner.

The Amazon SQS integration allows you to create, send, and monitor Amazon SQS messages and automatically trigger a task or workflow in Universal Controller each time a message has been received.Amazon Simple Queue Service (SQS) is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. Using SQS, enterprises can send, store, and receive messages between software components.  Key Features: Allows you to monitor for, create, and send Amazon SQS messages. Trigger a task in Universal Controller upon the arrival of a new SQS message. Amazon SQS tasks can be integrated into any existing or new automation workflow. Create and send a SQS message out of any modern third-party application by calling the Universal Controller remote web service API. Set different log-levels for the Amazon SQS task to provide additional information when root-causing potential issues. Additional Information:  This integration uses the Python Boto3 module. This enables new Amazon AWS services and the ability to update the current SQS task when new requirements occur. Credentials for Amazon S3 are stored in an encrypted format in the database. IAM Role-Based Access Control (RBAC) is supported. Communication to Amazon AWS is done via the HTTPS protocol. A proxy server connection to Amazon AWS with basic authentication is supported. Amazon AWS with basic authentication is supported.

The integration for Azure Blob Storage allows secure transfer of files from Azure Blob Storage containers and folders.Storing data in the cloud becomes an integral part of most modern IT landscapes. With the Stonebranch Universal Automation Center, you can securely automate your AWS, Azure, Google, and MinIO file transfers and integrate them into your existing scheduling flows. Key Features: The following file transfer commands are supported: Upload file(s) to an Azure Blob Storage container. Download file(s) from an Azure Blob Storage container. Transfer files between Azure Blob Storage containers. List objects in an Azure Blob Storage container. Delete object(s) in an Azure Blob Storage container. List Azure Blob Storage container names. Create an Azure Blob Storage container. File transfer can be triggered by a third-party application using the Universal Automation Center RESTfull web service API: REST API. The integration for Azure Blob Storage can be integrated into any existing scheduling workflow in the same way as any standard Linux or Windows task type. Security is ensured by using the HTTPS protocol with support for an optional proxy server. Supports Azure token-based Shared Access Signature (SAS). No Universal Agent needs to be installed on the Azure cloud – the communication goes via HTTPS.

This integration allows users to schedule, trigger, and monitor the Azure Data Factory pipeline process directly from the Universal Controller.   Key Features:  Uses Python modules azure-mgmt-resource and azure-mgmt-datafactory to make REST API calls to Azure Data Factory. Use the Azure tenant ID, subscription ID, client ID, client secret, resource group, and location for authenticating the REST API calls to Azure Data Factory.  Perform the following Azure Data Factory operations: Run a pipeline. Get a pipeline info. List all pipelines. Cancel pipeline run. List factory by resource group. Azure Data Factory triggers user can perform the  following operations from UAC: Start trigger. Stop trigger. List trigger by factory. UAC also can restart a failed pipeline either from the failed step or from any activity name in the failed pipeline.  

This integration allows users to perform end-to-end orchestration and automation of jobs and clusters in Databricks environment either in AWS or Azure.   Key Features:  Uses Python module requests to make REST API calls to the Databricks environment. Uses the Databricks URL and the user bearer token to connect with the Databricks environment. With respect to Databricks jobs, this integration can perform the below operations: Create and list jobs. Get job details. Run new jobs. Run submit jobs. Cancel run jobs. With respect to the Databricks cluster, this integration can perform the below operations: Create, start, and restart a cluster. Terminate a cluster. Get a cluster-info. List clusters. With respect to Databricks DBFS, this integration also provides a feature to upload files larger files.

This integration allows users to schedule, trigger, monitor, and orchestrate the Google BigQuery process directly from the Universal Controller. Key Features:  Users can perform the below Google BigQuery operations: BigQuery SQL. List dataset. List tables in a dataset. View job information. Create a dataset. Load local file to a table. Load cloud storage data to a table. Export table data. Additional Info: This task uses Python google-cloud-bigquery and google-auth modules to make REST API calls to Google BigQuery. This task will use the GCP project ID, BigQuery SQL or schema, dataset ID, job ID, location, table ID, cloud storage URI, source file format as parameters of BigQuery function and GCP KeyFile (API KEY) of service account for authenticating the REST API calls to Google BigQuery.

This integration allows users to monitor a file on the Hadoop Distributed File System (HDFS), then have the option to trigger an action. If the file is found, the file monitor can either go to success or launch a task in the Stonebranch Universal Automation Center. It's also possible to run the task monitor in auto-restart mode, which means it can restart itself automatically after finding a file. Key Features: Uses the Python HDFS module, which calls the Hadoop WebHDFS REST API. Supports Universal agent for both Linux/Unix and Windows. However, it has been currently only tested against a Linux agent.  Select different log-levels, e.g., info and debug. All passwords are encrypted using Universal Controller credentials. Currently only the InsecureClient (the default) is implemented. The TokenClient can be implemented upon request. Currently only direct file matches are implemented, as this is the standard functionality supported by the Python HDFS module. A scan for files using wildcards (*) or regular expressions can be implemented upon request.

Pentaho Data Integration provides powerful ETL (Extract, Transform, and Load) capabilities and Universal Controller using its universal extension capabilities integrated with Pentaho Data Integration tool to orchestrate the below functionalities.Key Features: Run a Pentaho Job from its Carte configured repository Run a Pentaho Job from its Repository Run a Pentaho Job from a remote job definition file Define & Run a Pentaho Job from Universal Controller Script Library Execute a Pentaho Transformation from a Repository Execute a Pentaho Transformation from a remote transformation definition file(XML)Define & Run a Pentaho Transformation from Universal Controller Script Library

This integration allows users to schedule any data integration task, linear taskflow, or taskflow in the Informatica Cloud. Key Features: Schedule data integration tasks, including linear taskflow, in the Informatica Cloud.All communication is web-service based, using the latest Informatica REST API (version 2 and 3) with support for folders.Log-files including activity-, session-, and error-log are available from the Universal Controller web UI in the same way as the Informatica monitoring console.

This integration allows users to schedule Informatica PowerCenter workflows and tasks, including retrieving the workflow and session log. It's also possible to start a workflow from a certain task onwards. Key Features: Schedules Informatica PowerCenter via its web services hub; therefore, no installation on any Informatica system is required. Based on the standard PowerCenter web services hub using the SOAP protocol. The PowerCenter web services hub interface is called by a Universal Agent running on a Linux server or Windows server. The following actions are supported: Start a task in an Informatica PowerCenter workflow. Start an Informatica PowerCenter workflow. Start an Informatica PowerCenter workflow from a given task onwards.

This integration allows users to refresh datasets and dataflows in the Microsoft Power BI business analytics service. Key Features: Refresh a dataset in a group-workspace or in my workspace. Refresh a dataflow in a group-workspace. Lookup datasets in a selected group. Lookup dataflows in a selected group. Connection to the Power BI REST API is done via the Python MSAL library. Supports Windows and Linux Universal Agents in order to connect to the Power BI REST API.

This integration allows users to execute SQL scripts and functions against a MySQL, PostgreSQL, Microsoft SQL Server, Oracle, and SAP HANA database. It uses an agentless connection via ODBC towards SQL Server, MySQL, and PostgreSQL, and the Oracle basic instant client to connect to an Oracle database. Key Features: Supports execution of SQL scripts for Oracle, MySQL, PostgreSQL, Microsoft SQL Server, and SAP HANA: For SQL Server, MySQL, and PostgreSQL: all connections are agentless via ODBC.For SQL Server: Windows Authentication and SQL Server Authentication is supported. For SAP HANA: database connections are performed agentless using the SAP HANA client for Python. For Oracle: the execution of SQL scripts and Oracle PLSQL blocks are supported. Oracle connections are performed agentless using the Oracle basic instant client. Supports Universal Agent for both Linux/Unix and Windows. Select different log-levels, e.g., info and debug. Decide if the SQL-output is provided in the standard out or not. All passwords are encrypted using Stonebranch Universal Controller credentials.

This integration allows users to execute a Microsoft SSIS Package using the 'dtexec' utility, which comes with the Microsoft SQL Server installation. In addition, it's also possible to execute an SSIS package via a SQL job, which is executed by a procedure task stored by Stonebranch Universal Controller.    

This integration can complete various administrative tasks, including publishing reports and moving reports from one server to another server. It's based on the SQL Server Reporting Services 'rs.exe' command-line utility, which can perform many scripted operations related to SQL Server Reporting Services (SSRS). The rs.exe utility requires an input file to tell rs.exe what to do.

This integration for SAP batch input allows users to schedule and execute batch input sessions in SAP. Batch input sessions enter data non-interactively into an SAP system. It's typically used to transfer data from non-SAP systems to SAP systems or to transfer data between SAP systems. Key Features: Runs a batch input session. You only need to provide the batch input session name in the task variable. It's possible to use wildcards (*) to run multiple batch input sessions. There's no need to create a variant manually for the batch input session in SAP. The SAP task uses the feature inline variants of USAP to create a temporary variant for the ABAP RSBDCSUB with the batch input session name.

Schedule any SAP BusinessObjects Data Services ETL job from the Stonebranch Universal Automation Center, using the 'AL_RWJobLauncher.exe' utility that comes with SAP Data Services.Using analytics tools to collect massive amounts of big data from your organization is one thing. Extracting meaning from that data and using it to drive real growth is another. BusinessObjects analytics from SAP can help you unleash the power of collective insight by delivering enterprise business intelligence, agile visualizations, and advanced predictive analytics to all users.Key Features:The integration provides the same error and trace information as the SAP Data Services Management ConsoleYou can select different log-levels ; for example, Info and DebugYou can configure all connection parameters Exception handling has been implemented for all parametersAdditional Info: Based on the “AL_RWJobLauncher.exe”. which is part of the Data Services InstallThe Task runs on Data Services for Windows and Linux

This integration schedules any SAP BusinessObjects schedulable resource, including Crystal or Web Intelligence reports, from the Stonebranch Universal Automation Center.Using analytics tools to collect massive amounts of big data from your organization is one thing. Extracting meaning from that data and using it to drive real growth is another. BusinessObjects analytics from SAP can help you unleash the power of collective insight by delivering enterprise business intelligence, agile visualizations, and advanced predictive analytics to all users.

This Integration allows users to execute an SAP Data Services “ETL” Job using the “AL_RWJobLauncher.exe” utility. Using analytics tools to collect massive amounts of Big Data from your organization is one thing. Extracting meaning from that data and using it to drive real growth is another. Business Objects analytics from SAP can help you unleash the power of collective insight by delivering enterprise business intelligence, agile visualizations, and advanced predictive analytics to all users. Leverage the capabilities of SAP® Business Objects and schedule any SAP Business Object Data Services ETL job in Stonebranch's Universal Automation Center by using the “AL_RWJobLauncher.exe” utility, which comes with the SAP Data Services installation. Key Features: It is based on the “AL_RWJobLauncher.exe”, which is part of the Data Services Install. The Task runs on Data Services for Windows and Linux. The Universal Task provides the same error and trace information as the SAP Data Services Mgt. Console. You can select different log levels, for example, Info and Debug. You can configure all connection Parameters via the Universal Task. For all Parameters, exception handling has been implemented.

This integration allows Stonebranch users to orchestrate, schedule, trigger, and monitor the Snowflake load and unload processes from different data sources (including cloud storage or local virtual machines), directly from the Stonebranch Universal Automation Center.   Key Features:  Users can orchestrate the following Snowflake functionalities: Snowflake loading processes: Load data from AWS S3 to Snowflake. Load data from Azure storage to Snowflake. Load data from Google storage to Snowflake. Load internal stage file to Snowflake table. Copy from local server to internal staging. Snowflake unloading processes: Unload Snowflake data to AWS S3. Unload Snowflake data to Azure storage. Unload Snowflake data to Google storage. Unload Snowflake data to internal staging. Unload from internal stage to local server. Snowflake execute commands: Execute a Snowflake command.