Book Image

Azure Data Engineer Associate Certification Guide

By : Newton Alex
Book Image

Azure Data Engineer Associate Certification Guide

By: Newton Alex

Overview of this book

Azure is one of the leading cloud providers in the world, providing numerous services for data hosting and data processing. Most of the companies today are either cloud-native or are migrating to the cloud much faster than ever. This has led to an explosion of data engineering jobs, with aspiring and experienced data engineers trying to outshine each other. Gaining the DP-203: Azure Data Engineer Associate certification is a sure-fire way of showing future employers that you have what it takes to become an Azure Data Engineer. This book will help you prepare for the DP-203 examination in a structured way, covering all the topics specified in the syllabus with detailed explanations and exam tips. The book starts by covering the fundamentals of Azure, and then takes the example of a hypothetical company and walks you through the various stages of building data engineering solutions. Throughout the chapters, you'll learn about the various Azure components involved in building the data systems and will explore them using a wide range of real-world use cases. Finally, you’ll work on sample questions and answers to familiarize yourself with the pattern of the exam. By the end of this Azure book, you'll have gained the confidence you need to pass the DP-203 exam with ease and land your dream job in data engineering.
Table of Contents (23 chapters)
1
Part 1: Azure Basics
3
Part 2: Data Storage
10
Part 3: Design and Develop Data Processing (25-30%)
15
Part 4: Design and Implement Data Security (10-15%)
17
Part 5: Monitor and Optimize Data Storage and Data Processing (10-15%)
20
Part 6: Practice Exercises

Summary

With that, we have come to the end of our third chapter. I hope you enjoyed learning about the different partitioning techniques available in Azure! We started with the basics of partitioning, where you learned about the benefits of partitioning; we then moved on to partitioning techniques for storage and analytical workloads. We explored the best practices to improve partitioning efficiency and performance. We understood the concept of distribution tables and how they impact the partitioning of Azure Synapse Analytics, and finally, we learned about storage limitations, which play an important role in deciding when to partition for ADLS Gen2. This covers the syllabus for the DP-203 exam, Designing a Partition Strategy. We will be reinforcing the learnings from this chapter via implementation details and tips in the following chapters.

Let's explore the serving layer in the next chapter.