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

Integrating Jupyter/Python notebooks into a data pipeline

Integrating Jupyter/Python notebooks into our ADF data pipeline can be done using the Spark activity in ADF. You will need an Azure HDInsight Spark cluster for this exercise.

The prerequisite for integrating Jupyter notebooks is to create linked services to Azure Storage and HDInsight from ADF and have an HDInsight Spark cluster running.

You have already seen how to create linked services, in the Developing batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, and Azure Databricks section earlier in this chapter, so I'll not repeat the steps here.

Select the Spark activity from ADF and specify the HDInsight linked service that you created in the HDInsight linked service field under the HDI Cluster tab as shown in the following screenshot.

Figure 9.26 – Configuring a Spark activity in ADF

Now, start the Jupyter notebook by going to...