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

Designing a partition strategy for efficiency/performance

In the last few sections, we explored the various storage and analytical partitioning options and learned about how partitioning helps with performance, scale, security, availability, and so on. In this section, we will recap the points we learned about performance and efficiency and learn about some additional performance patterns.

Here are some strategies to keep in mind while designing for efficiency and performance:

  • Partition datasets into smaller chunks that can be run with optimal parallelism for multiple queries.
  • Partition the data such that queries don't end up requiring too much data from other partitions. Minimize cross-partition data transfers.
  • Design effective folder structures to improve the efficiency of data reads and writes.
  • Partition data such that a significant amount of data can be pruned while running queries.
  • Partition in units of data that can be easily added, deleted, swapped...