11 products found for "Azure"

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Azure Data Factory: Schedule, Trigger, and Monitor
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.  
Free  
Azure Blob: Manage File Transfers
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.
Free  
Azure Virtual Machines: Start, Stop, and Terminate Instances
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.
Free  
Azure Logic Apps: Schedule, Trigger, and Monitor Workflows
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. 
Free  
Databricks: Automate Jobs and Clusters
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.
Free  
Azure Blob: Check for Blob in Container
This integration allows users to check for the existence of a blob in a container in Azure Blob Storage and supports multi-level security when moving to the cloud. Key Features: All credentials for Azure Blob Storage are stored in an encrypted form in the database. Encrypted connections towards the Azure Blob Storage via a proxy server are supported. Calls the Microsoft Azure Storage SDK for Python. Supports encrypted connections via a proxy server. All credentials for Azure (account_name, account_blob) are stored in an encrypted form in the database. Select different log-levels, e.g., info and debug.  Additional Info: The Python azure-storage-blob module is called by a Universal Agent running on a Linux server or Windows server.
Free  
Azure Blob: Upload Local Directory
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).
Free  
Azure Blob: Monitor Blob in Container
This integration allows users to monitor a blob in a container in Azure Blob Storage and provides multi-level security when moving to the cloud.  Key Features: Supports encrypted connections via a proxy server. All credentials for Azure (account_name, account_blob) are stored in an encrypted form in the database.Calls the Microsoft Azure Storage SDK for Python. Configure all connection parameters for the proxy and Azure via the integration. Select different log-levels, e.g., info and debug. Additional Info: The Python azure-storage-blob module is called by a Universal Agent running on a Linux server or Windows server. 
Free  
Azure Blob: Copy Files to a Container and Multi-Level Security
This integration allows users to copy a file to a container in Azure Blob Storage and provides multi-level security when moving to the cloud.  Key Features: All credentials for Azure (account_name, account_blob) are stored in an encrypted form in the database. Calls the Microsoft Azure Storage SDK for Python.Configure all connection parameters for the proxy and Azure via the integration. Select different log-levels, e.g., info and debug. Additional Info: The Python azure-storage-blob module is called by a Universal Agent running on a Linux server or Windows server.
Free Video  
Snowflake: Schedule, Trigger, Monitor, and Orchestrate Operations
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.
Free  
Microsoft Teams: Send and Receive Notifications
This integration enables users to receive task-related notifications and send task-related approvals through a Teams channel. It uses incoming webhooks to integrate Microsoft Teams with the Stonebranch Universal Automation Center (UAC).  Key Features: Using this task, UAC task-related information can be sent directly to an operational Teams channel. Send interactive messages to a Teams channel to trigger an approval process for manual tasks whenever user intervention is needed. End-users can request notifications within Teams alerting them about UAC task results. Task results can include items like completed workflows and task failures. Additional Info:  Requires serverless infrastructure (e.g., AWS Lambda, Microsoft Azure Functions, or Google Cloud Functions).