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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

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801077743
Length 480 pages
Edition 1st Edition
Languages
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Author (1):
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Manoj Kukreja Manoj Kukreja
Author Profile Icon Manoj Kukreja
Manoj Kukreja
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Table of Contents (17) Chapters Close

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

The journey of data

Data engineering is the vehicle that makes the journey of data possible, secure, durable, and timely. A data engineer is the driver of this vehicle who safely maneuvers the vehicle around various roadblocks along the way without compromising the safety of its passengers. Waiting at the end of the road are data analysts, data scientists, and business intelligence (BI) engineers who are eager to receive this data and start narrating the story of data. You can see this reflected in the following screenshot:

Figure 1.1 – Data's journey to effective data analysis

Figure 1.1 – Data's journey to effective data analysis

Traditionally, the journey of data revolved around the typical ETL process. Unfortunately, the traditional ETL process is simply not enough in the modern era anymore. Due to the immense human dependency on data, there is a greater need than ever to streamline the journey of data by using cutting-edge architectures, frameworks, and tools.

You may also be wondering why the journey of data is even required. Gone are the days where datasets were limited, computing power was scarce, and the scope of data analytics was very limited. We now live in a fast-paced world where decision-making needs to be done at lightning speeds using data that is changing by the second. Let's look at how the evolution of data analytics has impacted data engineering.

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Data Engineering with Apache Spark, Delta Lake, and Lakehouse
Published in: Oct 2021
Publisher: Packt
ISBN-13: 9781801077743
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