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.

This integration allows users to create an AWS EC2 instance with parameters, either in task form or by simply creating an EC2 instance from the existing AWS launch template. This task also offers the option to install a Linux/Unix Universal Agent in the newly provisioned EC2 instance. Key Features: The task interacts with the AWS platform via a Python Boto3 module. All AWS credentials remain encrypted. Users can also install/configure a Linux Universal Agent for each EC2 instance, enabling the Universal Controller to communicate with the newly created instance instantly.  This task also lets users create multiple EC2 instances with the same configuration. New instances can also be tagged. It allows customers to create a new key pair or use an existing one for the new EC2 instance. This task also enables options for additional EBS volume and encryption, as well as detailed monitoring. Additional Info:Only Linux Universal Agent is supported at the moment.

This integration allows users to spin up, terminate, and manage AWS EC2 instances on demand simply by providing one or more instance IDs as input.  Key Features: This task uses Python Boto3 to interact with the AWS platform using the credentials supplied within the task. It supports multiple EC2 instances at once. This task goes to the success state in Universal Controller until the EC2 instance is completely spun up or terminated. Scheduling this task using Universal Controller workflow spins up and tears down EC2 instances based on the business needs, complete with the correct setup and dependencies.  It dynamically manages EC2 operations, offering the potential to reduce EC2 operations costs in the cloud. 

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 upload a local Windows or Linux directory to an Azure Blob Storage container. As a result, you can integrate uploads of an entire local directory into your existing or new scheduling workflows, providing a true hybrid cloud (on-prem and cloud computing) file transfer solution. This integration makes it possible to automate your uploads in a way that's not available in the standard Azure SDK. 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, or any other cloud file transfer and integrate them into your existing scheduling flows. This integration offers multiple levels of security: All credentials for Azure Blob Storage are stored in an encrypted form in the database. Key Features: Calls the Python blobxfr module. The Python blobxfr module is called by a Universal Agent running on a Linux server or Windows server. The server running the Universal Agent needs to have Python 2.7.x or 3.6.x installed. All credentials for Azure are stored in an encrypted form in the database. Select different log-levels, e.g., info and debug. A proxy connection towards Azure is currently not implemented for this integration (however, it's possible with minor adjustments).

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 can trigger and monitor the execution of Azure Logic workflows and retrieve Azure Logic workflow output execution. The Stonebranch Universal Controller (UC) integrates with Logic apps through REST APIs securely through the Azure Oauth 2.0 authentication mechanism.  Key Features: Passes dynamic input parameters (JSON format) to each Azure Logic app workflow. Triggers a workflow, monitors it until the process is completed, and then delivers the results to UC. Customers can manage and control Logic app workflow execution from UC, with the capability to employ other dependencies like time triggers or event-based jobs/workflows. This task offers ITSM integration capability, enabling the auto-creation of incidents in Logic apps workflow execution failure. 

This integration allows users to utilize Azure Virtual Machine (VM) name, resource group, subscription ID, and access token as inputs to a start, stop, terminate, list, and check the status of Azure VMs. Key Features: Uses a Python request module to interact with the Azure cloud platform.  Expands user ability to start/stop/terminate/check/list Azure VMs that belong to a subscription and resource group. In the Stonebranch Universal Controller (UC), this task reaches and stays in the success state until the Azure instance is completely started, stopped, or terminated.  Scheduling this task in UC with the right dependencies set up would start and stop EC2 instances based on business needs using a UC workflow.  This task helps to dynamically manage VM operations. It could potentially reduce the Azure VM running cost in the cloud.  Important: This integration uses Azure Oauth 2.0 access token for Azure API authentication. Users may need to use the UC web services task to refresh the access token periodically.

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.

Stream data from one object store to another without intermediate storage.The Inter-Cloud Data Transfer integration allows users to transfer data to, from, and between any of the major private and public cloud providers like AWS, Google Cloud, and Microsoft Azure. It also supports the transfer of data to and from a Hadoop Distributed File System (HDFS) and to major cloud applications like OneDrive and SharePoint. Integrations within this solution package include: AWS S3. Google Cloud. SharePoint. Dropbox. OneDrive. Hadoop HDFS. Key Features: Transfer data to, from, and between any cloud provider. Transfer between any major storage applications like SharePoint or Dropbox. Transfer data to and from a Hadoop HDFS. Stream data from one object store to another – no intermediate storage. Executes quickly if the object stores are in the same region. Preserves always timestamps and verifies checksums. Supports encryption, caching, compression, and chunking. Supports regular expressions based on include/exclude filter rules. Supported actions are: List objects, list directory. Copy/move. Remove object/object store. Perform dry-runs. Monitor object. Copy URL.