This module delves into the fundamentals and practical applications of U-SQL within Azure Data Lake Analytics. Learners will gain a thorough understanding of how to write and optimize U-SQL scripts, manage Analytics Units for performance tuning, and apply filtering techniques to datasets. The module also introduces learners to U-SQL job execution stages, the structure and syntax of U-SQL language, and schema handling concepts such as "Schema on Read". By the end of this module, learners will be able to write, execute, and interpret U-SQL scripts effectively within real-world big data scenarios.

This module focuses on handling larger volumes of data in Azure Data Lake by exploring aggregation techniques, multi-file ingestion, and distributed processing methods using U-SQL. Learners will develop the ability to group and summarize data effectively, troubleshoot aggregation logic, ingest and process multiple structured files, and understand various data distribution strategies such as hash, range, and round-robin. By the end of this module, learners will be equipped to manage and transform scalable datasets using advanced features of Azure Data Lake Analytics.

This module explores advanced techniques for querying and managing data within Azure Data Lake using U-SQL. Learners will gain hands-on experience working with structured objects such as views, table-valued functions, and stored procedures, while also incorporating inline C# functions for enhanced query logic. The module emphasizes reusability, modular design, and dynamic data manipulation strategies to improve analytical workflows in real-world data lake environments.

This module guides learners through developing, deploying, and monitoring U-SQL jobs using Visual Studio and Azure Data Lake Analytics. It introduces project setup, job submission, and custom code integration using Visual Studio tools. Learners will explore job diagnostics, performance analysis with job graphs and heat maps, and advanced debugging with the vertex execution view. The module concludes with tools for evaluating job efficiency and identifying execution bottlenecks, empowering users to optimize big data workflows.

This module provides a comprehensive overview of accessing, managing, and organizing data within Azure Data Lake using both graphical and command-line tools. Learners explore various access methods including Azure CLI and PowerShell, understand how to create, upload, and manage files and directories in the Data Lake Store, and perform operations such as renaming and deleting accounts. The module also explains Azure Data Lake pricing models and ends with a concise summary to reinforce best practices and service structure.