hands-on lab

Submitting a U-SQL Job to Azure Data Lake Analytics

Up to 40m
This lab is currently under maintenance and unavailable. We are actively working to resolve this issue and we apologize for any inconvenience.

This lab will no be available after February 29th, 2024 due to the Azure Data Lake Analytics depreciation. Azure recommends Azure Synapse Analytics to be used in place of Azure Data Lake Analytics. This service is covered in the Using Azure Synapse Analytics to Query Data Lake lab. 

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Lab description

Big data and analytics in Azure can help you gain insights into your data and deliver better experiences. This Lab demonstrates using two of Azure's big data services:

  1. Azure Data Lake Store for storing massive amounts of structured, semi-structured, and unstructured data, and
  2. Azure Data Lake Analytics for massively parallel analysis of data stored in Data Lake Store.
    • Azure Data Lake Analytics will be retired on February 29th, 2024. Please see https://aka.ms/adlaqa for more details.

You will use Data Lake Analytics' U-SQL big data query language to transform website search logs stored in a Data Lake Store and discover which searches take longer than a threshold to complete.

Lab Objectives

Upon completion of this Lab you will be able to:

  • Store data in Azure Data Lake Stores
  • Submit jobs to Azure Data Lake Analytics
  • Understand the trade-off between cost and performance in Azure Data Lake Analytics
  • Explain basic U-SQL queries

Lab Prerequisites

You should be familiar with:

Lab Environment

Before completing the Lab instructions, the environment will look as follows:

After completing the Lab instructions, the environment should look similar to:


December 9th, 2022 - Updated the instructions and screenshots to reflect the latest UI and added validation check

September 16th, 2022 - Resolved Azure Data Lake Analytics issue

About the author
Logan Rakai, opens in a new tab
Lead Content Developer - Labs
Learning paths

Logan has been involved in software development and research since 2007 and has been in the cloud since 2012. He is an AWS Certified DevOps Engineer - Professional, AWS Certified Solutions Architect - Professional, Microsoft Certified Azure Solutions Architect Expert, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, Certified Kubernetes Security Specialist (CKS), Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), and Certified OpenStack Administrator (COA). He earned his Ph.D. studying design automation and enjoys all things tech.

LinkedIn, Twitter, GitHub

Covered topics
Lab steps
Logging in to the Microsoft Azure Portal
Uploading a File to an Azure Data Lake Storage
Submitting a U-SQL Job to Azure Data Lake Analytics
Inspecting the Results of the Data Lake Analytics Job