Data Engineering on Azure

**** The book "Data Engineering on Azure" positions itself within the technical literature landscape, where it faces the dual pressures of demanding content and high expectations from its readers. Emerging from Manning, a publisher well-regarded for its emphasis on quality, the book stands out for its practical applications and hands-on approach to learning. The author demonstrates a keen ability to translate complex concepts into digestible practices, making the learning process effective for those keen on gaining real-world skills in data engineering. Readers have particularly praised the book's organization, complete with a well-structured GitHub repository that enhances the practical learning experience through code exercises and examples. The use of diagrams is highlighted as a valuable tool that aids in conceptual understanding of the Azure ecosystem, which can be overwhelming for beginners. However, the specificity of the book's content raises concerns among some users. While it delves deeply into Azure Data Explorer, it neglects broader topics critical to data engineering, such as data lake organization and various compute engines' strengths and weaknesses. This focus may lead readers who are seeking comprehensive knowledge about Azure's data engineering landscape to feel somewhat shortchanged. The discrepancy in expectations versus reality, particularly for readers with prior Microsoft certification, indicates that while the book excels in practical aspects, it may leave some gaps in covering the breadth of Azure technologies. In summary, "Data Engineering on Azure" is a practical resource for new software engineers entering the field of data engineering. It delivers a clear guide to navigating Azure but may not satisfy those seeking an exhaustive overview of all associated technologies. **