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 metastores in Azure Synapse Analytics and Azure Databricks

Metastores store the metadata of data in services such as Spark or Hive. Think of a metastore as a data catalog that can tell you which tables you have, what the table schemas are, what the relationships among the tables are, where they are stored, and so on. Spark supports two metastore options: an in-memory version and an external version.

In-memory metastores are limited in accessibility and scale. They can help jobs running on the same Java virtual machine (JVM) but not much further than this. Also, the metadata is lost once the cluster is shut down.

For all practical purposes, Spark uses an external metastore, and the only supported external metastore at the time of writing this book was Hive Metastore. Hive's metastore is mature and provides generic application programming interfaces (APIs) to access it. Hence, instead of rebuilding a new metastore, Spark just uses the mature and well-designed Hive...