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Azure Data Engineer Associate Certification Guide

You're reading from Azure Data Engineer Associate Certification Guide Ace the DP-203 exam with advanced data engineering skills

Product type Paperback

Published in May 2024

Publisher Packt

ISBN-13 9781805124689

Length 548 pages

Edition 2nd Edition

Languages

SQL

Tools

Azure

Concepts

Big Data

Authors (3):

Newton Alex

Giacinto Palmieri

Mr. Surendra Mettapalli

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1: Azure Basics FREE CHAPTER

2. Chapter 1: Introducing Azure Basics

3. Part 2: Data Storage

4. Chapter 2: Implementing a Partition Strategy

5. Chapter 3: Designing and Implementing the Data Exploration Layer

6. Part 3:Data Processing

7. Chapter 4: Ingesting and Transforming Data

8. Chapter 5: Developing a Batch Processing Solution

9. Chapter 6: Developing a Stream Processing Solution

10. Chapter 7: Managing Batches and Pipelines

11. Part 4:Secure, Monitor, and Optimize Data Storage and Processing

12. Chapter 8: Implementing Data Security

13. Chapter 9: Monitoring Data Storage and Data Processing

14. Chapter 10: Optimizing and Troubleshooting Data Storage and Data Processing

15. Chapter 11: Accessing the Online Practice Resources

16. Other Books You May Enjoy

Implementing Azure Synapse Link and Querying Replicated Data

Before diving into the implementation of Azure Synapse Link and querying replicated data, it is essential to know the rationale behind integrating these two different products. This learning is crucial because SQL pools on Synapse offer most of the features provided by Transact-SQL on SQL Server or Azure SQL. Consequently, it is natural to feel some confusion about when and why to use one product over the other. The easiest approach to clarifying this confusion is to consider the distinction between transactional workloads and analytical workloads.

Note

This section primarily focuses on the Implement Azure Synapse Link and query the replicated data concept of the DP-203: Data Engineering on Microsoft Azure exam.

Transactional workloads consist of the insertion, update, or deletion of production data, and the main concern here is “data quality” and “consistency.” Analytical workloads consist...

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Authors (3)

Palmieri

Palmieri

Giacinto Palmieri is a freelance technical trainer and consultant who has been working in the IT secotr for for more than 25 years, He has a double background as a developer and a data engineer and focuses nowadays on Azure development and data services, but also on Power BI. He works for a wide range of training companies and for their clients in different business sectors and geographical areas.He prides himelf in always trying to go to the bottom of things, exploring not only the what and the how but also the why (his educational background is in Philosophy), which then allows him to convey this deep understanding to his course participants and readers

See other products by Palmieri

Alex

Alex

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 Alex

Surendra Mettapalli

Surendra Mettapalli

Surendra Mettapalli is leading expert in Azure technologies with proven track record of designing, implementing innovative cloud solutions. As a Lead Data Engineer/Architect, he specializes in pivotal services such as Azure Synapse Analytics, Azure Databricks, Azure Data Factory and Power BI. He has a deep understanding of Cloud Architecture, Data Engineering, AI-driven applications, and has successfully collaborated with the diverse clients across various sectors including technology, finance, retail and government. His expertise has been instrumental in delivering some of the largest and most impactful data projects, enabling organizations to utilize Azure's full potential and enhance their business operations.

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