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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

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

Handling skews in data

A data skew refers to an extreme, uneven distribution of data in a dataset. Let's take an example of the number of trips per month of our Imaginary Airport Cab (IAC) example. Let's assume the data distribution as shown in the following graph:

Figure 14.8 – An example of skewed data

As you can see from the graph, the trip numbers for November and December are quite high compared to the other months. Such an uneven distribution of data is referred to as a data skew. Now, if we were to distribute the monthly data to individual compute nodes, the nodes that are processing the data for November and December are going to take a lot more time than the ones processing the other months. And if we were generating an annual report, then all the other stages would have to wait for the November and December stages to complete. Such wait times are inefficient for job performance. To make the processing more efficient, we will have...

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