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Data Engineering with Apache Spark, Delta Lake, and Lakehouse

You're reading from Data Engineering with Apache Spark, Delta Lake, and Lakehouse Create scalable pipelines that ingest, curate, and aggregate complex data in a timely and secure way

Product type Paperback

Published in Oct 2021

Publisher Packt

ISBN-13 9781801077743

Length 480 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Data Engineering

Author (1):

Manoj Kukreja

View More author details

Table of Contents (17) Chapters

Preface

1. Section 1: Modern Data Engineering and Tools

2. Chapter 1: The Story of Data Engineering and Analytics FREE CHAPTER

3. Chapter 2: Discovering Storage and Compute Data Lakes

4. Chapter 3: Data Engineering on Microsoft Azure

5. Section 2: Data Pipelines and Stages of Data Engineering

6. Chapter 4: Understanding Data Pipelines

7. Chapter 5: Data Collection Stage – The Bronze Layer

8. Chapter 6: Understanding Delta Lake

9. Chapter 7: Data Curation Stage – The Silver Layer

10. Chapter 8: Data Aggregation Stage – The Gold Layer

11. Section 3: Data Engineering Challenges and Effective Deployment Strategies

12. Chapter 9: Deploying and Monitoring Pipelines in Production

13. Chapter 10: Solving Data Engineering Challenges

14. Chapter 11: Infrastructure Provisioning

15. Chapter 12: Continuous Integration and Deployment (CI/CD) of Data Pipelines

16. Other Books You May Enjoy

Section 1: Modern Data Engineering and Tools

This section introduces you to the world of data engineering. It gives you an understanding of data engineering concepts and architectures. Furthermore, it educates you on how to effectively utilize the Microsoft Azure cloud services for data engineering.

This section contains the following chapters:

Chapter 1, The Story of Data Engineering and Analytics
Chapter 2, Discovering Storage and Compute Data Lake Architectures
Chapter 3, Data Engineering on Microsoft Azure

The rest of the chapter is locked

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Authors (1)

Kukreja

Kukreja

Manoj Kukreja is a Principal Architect at Northbay Solutions who specializes in creating complex Data Lakes and Data Analytics Pipelines for large-scale organizations such as banks, insurance companies, universities, and US/Canadian government agencies. Previously, he worked for Pythian, a large managed service provider where he was leading the MySQL and MongoDB DBA group and supporting large-scale data infrastructure for enterprises across the globe. With over 25 years of IT experience, he has delivered Data Lake solutions using all major cloud providers including AWS, Azure, GCP, and Alibaba Cloud. On weekends, he trains groups of aspiring Data Engineers and Data Scientists on Hadoop, Spark, Kafka and Data Analytics on AWS and Azure Cloud.

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