MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Difficulty: Beginner | Genre: eLearning | Language: English | Duration: 8 Lectures (37m) | Size: 572 MB
Azure Data Lake Store (ADLS) is a cloud-based repository for both structured and unstructured data.
Get data into and out of ADL Store
Use the five layers of security to protect data in ADL Store
Use ADL Analytics to process data in a data lake
Troubleshoot errors in ADL Analytics jobs
Database experience
SQL experience (recommended)
Microsoft Azure account recommended (sign up for free trial at
if you don't have an account)
For example, you could use it to store everything from documents to images to social media streams.
ADLS is designed for big data analytics in a Hadoop environment. It is compatible with Hadoop Distributed File System (HDFS), so you can run your existing Hadoop jobs by simply telling them to use your Azure data lake as the filesystem.
Alternatively, you can use Azure Data Lake Analytics (ADLA) to do your big data processing tasks. It's a service that automatically provisions resources to run processing jobs. You don't have to figure out how big to make a cluster or remember to tear down the cluster when a job is finished. ADLA will take care of all of that for you. It is also simpler to use than Hadoop MapReduce, since it includes a language called U-SQL that brings together the benefits of SQL and C#.
In this course, you will follow hands-on examples to import data into ADLS, then secure, process, and export it. Finally, you will learn how to troubleshoot processing jobs and optimize I/O.
Anyone interested in Azure's big data analytics services
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