Azure Data Lake Analytics, is a powerful on-demand analytics job service on Microsoft Azure. By reading this article, you can learn Azure Data Analytics by Example.
Since this is an introductory article on Azure Data Lake Analytics, the example that will be presented, will be a simple one just to showcase the technology. The rest is left to your imagination and creativity!
More specifically, in this simple example, I will create and submit for execution 2 jobs:
- Job 1
- Create a sample database and table
- Populate the table with sample data
- Job 2
- Query the sample table and extract the data into a .csv file
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Creating an Azure Data Lake Analytics Account
Step 1: Create a new resource
The first step, is to create an Azure Data Lake Analytics account, in case you don’t already have one.
You can easily do this, by creating a new resource – Data Lake Analytics:
Then, you will need to specify some details, as shown in the below screenshot:
Right after the above steps, within a few minutes, the Azure Data Lake Analytics account is ready for use:
Step 2: Configure firewall
The next step is to configure the Firewall rules for the newly created Azure Data Lake Analytics account. In my example, I have just added the IP of my client.
Creating and Submitting Jobs on Azure Data Lake Analytics
Now that the Azure Data Lake Analytics account is ready to accept jobs for execution, let’s create and submit a new job.
Define First Job
As mentioned earlier, in this example I will create and submit for execution 2 simple jobs.
The first job is to create a sample database and table, and then populate it with sample data.
So, in order to create the job, within the Azure Data Lake Analytics account, you click on the link “New job”.
Then, in this example, I used U-SQL, in order to define the job:
Submit First Job for Execution
After submitting the job for execution, I can see that it was successfully executed.
Below, you can see the relevant job graph that illustrates the job’s execution steps, as created by the powerful engine behind Azure Data Lake Analytics.
Moreover, in the “AU analysis” tab, you can get more statistics and shortcuts to further optimize your job (i.e. for performance, balanced execution, etc.).
Now, let’s check also via Data Explorer, to see our table:
Define Second Job
Now, let’s define the second which will extract the data from the sample table into a .csv file.
This time, I have created the job right from within Data Explorer:
Submit Second Job for Execution
After submitting the second job for execution, again, I can see that it was successfully executed.
Below, you can see the relevant job graph that illustrates the job’s execution steps:
Check the Second Job’s Outcome
Let’s check the second job’s outcome. As you can see in the below screenshot, the .csv file has been successfully created, and we can check its contents:
When we click on “Job insights”, we can see useful information and statistics about our jobs executed in Data Lake Analytics.
Azure Data Lake Tools
Azure Data Lake Analytics, integrate with many tools that you can use to create and submit different jobs.
For example, if you click on the “Tools” link, you will easily get started with the below tools:
- Data Lake Tools for Visual Studio
- Data Lake Tools for Visual Studio Core
- Azure PowerShell
- Azure CLI
Azure Data Lake Analytics, is a powerful engine, which allows you to create and execute heavy jobs on Microsoft Azure. Depending on the job type, Azure Data Lake Analytics automatically scale, thus making efficient use of its powerful engine, in order to execute the job. Moreover, it provides in-depth insights and statistics, which help you assess each job’s execution, or even change how each jobs should execute.
With Azure Data Lake Analytics, you can process any data (relational, unstructured, big data, and more) through its job system fast and efficiently.
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