Azure Data Lake Analytics introduces capabilities to manage pipelines and recurring jobs
02 October 2017
The Azure Data Lake Analytics service can now help you easily organize, manage, and gain insights from Data Lake Analytics jobs that are running as part of pipelines or on a recurring basis. New capabilities enable you to:
- Quickly identify jobs in pipelines that might have failed or taken longer than expected.
- Get the aggregated statistics (job counts, successful and failed AU hours, and so on) for a pipeline or a recurring instance so you can better understand the resource consumption and cost trends.
For detailed use cases, technical documentation, and information on how to get started with these capabilities, see:
- Blog: Managing Pipeline & Recurring Jobs in Azure Data Lake Analytics Made Easy
- Documentation: Manage Azure Data Lake Analytics by using the Azure portal
Data Lake Analytics jobs submitted as part of Azure Data Factory V2 pipelines automatically benefit from the new capabilities with no additional work on your part. If you are using Azure Data Factory V1 to create pipelines composed of one or more Data Lake Analytics activities (or are looking for a data movement and job orchestration service on Azure), we recommend reviewing Data Factory V2.