Microsoft Azure platform provides support for big data processing and analysis through services like Azure Data Lake Analytics (ADLA). In this article, we will explore the concepts and capabilities of ADLA and how it can be used to query and process data.

What is Azure Data Lake Analytics?

Azure Data Lake Analytics is a cloud-based service that allows you to analyze large volumes of data stored in Azure Data Lake Storage. It provides a scalable and cost-effective solution for processing and analyzing big data.

Key Features of Azure Data Lake Analytics

Some of the key features of Azure Data Lake Analytics are:

• Support for storing and processing data in its original format, including structured, semi-structured, and unstructured data.
• Ability to handle large data volumes, ranging from terabytes to petabytes.
• Flexible schema on reading, allowing data to be transformed as per requirement.
• Integration with other Azure services, such as Azure Data Lake Storage, Azure Synapse Analytics, and Azure Databricks.

Getting Started with Azure Data Lake Analytics

To start using Azure Data Lake Analytics, you need to create an ADLA account in the Azure portal. Here are the steps to create an ADLA account:

1. Login to the Azure portal using your credentials.
2. Click on “Data Lake Analytics” in the Azure Services.
3. In the “New Data Lake Analytics account” page, enter the required information such as subscription, resource group, account name, location, and storage subscription.
4. Review your configurations and create the ADLA account.

Querying Data with U-SQL

Azure Data Lake Analytics uses a query and processing language called U-SQL. U-SQL combines SQL-like syntax with C# programming language, making it familiar to SQL Server database professionals.

Here is an example of a U-SQL script that defines a dataset and stores the output in Azure Data Lake Storage:

@a = SELECT *
     FROM (VALUES ("Laptop", 500.0),
                 ("Keyboard", 950.0),
                 ("Mouse", 1350.0)) AS D(Product, Amount);

OUTPUT @a TO "/Sampledata.csv" USING Outputters.Csv();

To execute a U-SQL script, you need to create a job in the ADLA account and submit the script. The job will go through different phases such as preparing, queuing, running, and done. Once the job is completed, you can view the job graph and access the output data.

Conclusion

Azure Data Lake Analytics is a powerful tool for processing and analyzing big data in the cloud. In this article, we introduced the concepts of ADLA and discussed how to query data using U-SQL. In the upcoming articles, we will explore more features of U-SQL and its integration with other Azure services.