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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

Product type Book

Published in Oct 2021

Publisher Packt

ISBN-13 9781801077743

Pages 480 pages

Edition 1st Edition

Languages

Concepts

Data Processing

Author (1):

Manoj Kukreja

Table of Contents (17) Chapters

Preface

1. Section 1: Modern Data Engineering and Tools

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

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

Summary

In this chapter, we got our first taste of deploying cloud resources using the principle of IaC. The overall deployment process can be hugely accelerated using this principle. This process can be further improved and streamlined by introducing automation.

In the final chapter, Chapter 12, Continuous Integration and Deployment (CI/CD) of Data Pipelines, we will learn how to automate pipeline deployments. Using CI/CD is a hallmark practice of mature cloud deployments. CI/CD can dramatically accelerate code delivery while maintaining a high level of efficiency. In the next chapter, we will find out how to automate infrastructure deployment and code delivery using CI/CD.

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Authors (1)

Manoj 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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