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Azure Data Engineer Associate Certification Guide

You're reading from  Azure Data Engineer Associate Certification Guide

Product type Book
Published in Feb 2022
Publisher Packt
ISBN-13 9781801816069
Pages 574 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Newton Alex Newton Alex
Profile icon Newton Alex
LinkedIn
Newton Alex leads several Azure Data Analytics teams in Microsoft, India. His team contributes to technologies including Azure Synapse, Azure Databricks, Azure HDInsight, and many open source technologies, including Apache YARN, Apache Spark, and Apache Hive. He started using Hadoop while at Yahoo, USA, where he helped build the first batch processing pipelines for Yahoo's ad serving team. After Yahoo, he became the leader of the big data team at Pivotal Inc., USA, where he was responsible for the entire open source stack of Pivotal Inc. He later moved to Microsoft and started the Azure Data team in India. He has worked with several Fortune 500 companies to help build their data systems on Azure.
See other products by Newton Alex

Table of Contents (23) Chapters

Preface Part 1: Azure Basics
Chapter 1: Introducing Azure Basics Part 2: Data Storage
Chapter 2: Designing a Data Storage Structure Chapter 3: Designing a Partition Strategy Chapter 4: Designing the Serving Layer Chapter 5: Implementing Physical Data Storage Structures Chapter 6: Implementing Logical Data Structures Chapter 7: Implementing the Serving Layer Part 3: Design and Develop Data Processing (25-30%)
Chapter 8: Ingesting and Transforming Data Chapter 9: Designing and Developing a Batch Processing Solution Chapter 10: Designing and Developing a Stream Processing Solution Chapter 11: Managing Batches and Pipelines Part 4: Design and Implement Data Security (10-15%)
Chapter 12: Designing Security for Data Policies and Standards Part 5: Monitor and Optimize Data Storage and Data Processing (10-15%)
Chapter 13: Monitoring Data Storage and Data Processing Chapter 14: Optimizing and Troubleshooting Data Storage and Data Processing Part 6: Practice Exercises
Chapter 15: Sample Questions with Solutions Other Books You May Enjoy

Designing Star and Snowflake schemas

Schemas are guidelines for arranging data entities such as SQL tables in a data store. Designing a schema refers to the process of designing the various tables and the relationships among them. Star and Snowflake schemas are two of the most commonly used schemas in the data analytics and BI world. In fact, Star schemas are used more frequently than Snowflake schemas. Both have their own advantages and disadvantages, so let's explore them in detail.

Star schemas

A Star schema is the simplest of the data warehouse schemas. It has two sets of tables: one that stores quantitative information such as transactions happening at a retail outlet or trips happening at a cab company, and another that stores the context or descriptions of events that are stored in the quantitative table.

The quantitative tables are called fact tables and the descriptive or context tables are called dimension tables.

The following diagram shows an example of...

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Authors (1)

Profile icon Newton Alex
LinkedIn
Newton Alex leads several Azure Data Analytics teams in Microsoft, India. His team contributes to technologies including Azure Synapse, Azure Databricks, Azure HDInsight, and many open source technologies, including Apache YARN, Apache Spark, and Apache Hive. He started using Hadoop while at Yahoo, USA, where he helped build the first batch processing pipelines for Yahoo's ad serving team. After Yahoo, he became the leader of the big data team at Pivotal Inc., USA, where he was responsible for the entire open source stack of Pivotal Inc. He later moved to Microsoft and started the Azure Data team in India. He has worked with several Fortune 500 companies to help build their data systems on Azure.
Read more
See other products by Newton Alex

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