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

Optimizing pipelines for descriptive versus analytical workloads

Data analytics is categorized into four different types:

  • Descriptive Analytics: The type of analytics that deals with the analysis of what happened and when it happened. Most BI reports, such as sales reports and trip reports, that display current and historical data points fall under this category. The analytics tasks would usually be counts, aggregates, filters, and so on.
  • Diagnostic Analytics: This type of analytics also does the why part, along with the what and when. Examples include Root Cause Analysis (RCA). Apart from identifying what happened, we also delve deeper into the logs or metrics to identify why something happened. For example, you could be looking at why a certain cab route is having a dip in revenue, or why a particular type of VM is failing sooner than others by looking into the load on those machines.
  • Predictive Analytics: As the name suggests, this type of analytics refers to the...