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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

Performing time travel

A unique feature of Delta Lake is its ability to perform time travel. By using this feature, you can query and restore previous snapshots of your table. Access to previous snapshots is granted by using the versionAsOf option.

Important Note

The time travel functionality in Delta Lake implements data lineage. Data lineage is an extremely critical tool for data audits and compliance purposes. The same feature comes in handy for data engineers who are trying to trace data anomalies.

  1. This is how you can query previous versions of the delta table. In this example, we are querying version 0 of the table – in other words, when it was created:
    %sql
    SELECT * FROM store_orders VERSION AS OF 0 WHERE order_number=5;

    This results in the following output:

    Figure 6.25 – Checking the data in the sales_orders table for the sample row for version 0

    Notice how the previous version of the table shows the sale_ price value as 98.41.

  2. This time we will delete...
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