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

Splitting data

ADF provides multiple ways to split data in a pipeline. The important ones are Conditional Split and cloning (New branch). While Conditional Split is used to split data based on certain conditions, the New branch option is used to just copy the entire dataset for a new execution flow. We have already seen an example of Conditional Split in Figure 8.11. Let's see how we can create a new branch in the data pipeline.

In order to create a new branch, just click on the + icon next to any data source artifact (such as the DriverCSV11 block shown in the following screenshot). From there, you can choose the New branch option:

Figure 8.22 – New branch option in ADF

Apart from these two options, ADF also provides the ability to split the input files into multiple sub-files using partitions. Let's see how to accomplish that next.

File splits

In order to use file splits, create a new Sink artifact, and in the Optimize tab, just...