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Harness the power of distributed computing to create robust data pipelines
| Publisher | Packt Publishing |
| Publication date | May 25, 2021 |
| Language | English |
| Print length | 414 pages |
| ISBN-10 | 183864721X |
| ISBN-13 | 978-1838647216 |
| Item Weight | 1.56 pounds (710 grams) |
| Dimensions | 7.5 x 0.94 x 9.25 inches (19.1 x 2.4 x 23.5 cm) |
Ideal for data engineers seeking to create and manage scalable data pipelines using Azure Databricks efficiently.
Helpful for data analysts who need powerful tools for data transformation and insights generation through collaborative notebooks.
Beneficial for cloud architects designing distributed data systems in Azure, taking advantage of Databricks’ integrated analytics services.
Not suitable for beginners unfamiliar with data engineering concepts or cloud technologies, as it may overwhelm them.
Distributed Data Systems with Azure Databricks: Create, deploy, and manage enterprise data pipelines
**** The book on Azure Databricks presents itself as a comprehensive guide for beginners and intermediate users alike interested in mastering this powerful Microsoft Azure service. Launched in 2018, Azure Databricks is supported directly by Microsoft and is a pivotal tool for data engineers. This guide effectively begins with an introduction to the service, thereby laying a solid foundation for readers. The book is structured into three main sections: an introduction to setting up an Azure workspace, an exploration of ETL operations and Delta Lake, and a focus on Machine and Deep Learning. Each section is designed to be hands-on, which is beneficial for readers who wish to not only understand theoretical concepts but also apply them in practical scenarios. Most technical requirements are well-laid out to ensure readers can replicate the processes described. The author takes a commendable approach of using practical examples throughout, helping demystify complex topics associated with Azure Databricks. Although the book is a solid introduction for those unfamiliar with the platform, it is worth noting its reliance on Python—a limitation for those looking to explore Scala usage within Databricks. Some readers have raised concerns about the content being somewhat dated, particularly with changes in the Azure UI and public datasets, which may impede following along effectively with the examples provided. Despite this, the book successfully covers essential topics such as resource management, ETL processes, data streaming, and the use of Machine Learning libraries. Overall, it’s a valuable resource for those wanting to delve into the functionalities of Azure Databricks, provided they are ready to manage some discrepancies between the book's information and the current state of the platform. **Pros and Cons:** **
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