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Simplifying Data Engineering and Analytics with Delta

You're reading from   Simplifying Data Engineering and Analytics with Delta Create analytics-ready data that fuels artificial intelligence and business intelligence

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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781801814867
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Anindita Mahapatra Anindita Mahapatra
Author Profile Icon Anindita Mahapatra
Anindita Mahapatra
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Table of Contents (18) Chapters Close

Preface 1. Section 1 – Introduction to Delta Lake and Data Engineering Principles
2. Chapter 1: Introduction to Data Engineering FREE CHAPTER 3. Chapter 2: Data Modeling and ETL 4. Chapter 3: Delta – The Foundation Block for Big Data 5. Section 2 – End-to-End Process of Building Delta Pipelines
6. Chapter 4: Unifying Batch and Streaming with Delta 7. Chapter 5: Data Consolidation in Delta Lake 8. Chapter 6: Solving Common Data Pattern Scenarios with Delta 9. Chapter 7: Delta for Data Warehouse Use Cases 10. Chapter 8: Handling Atypical Data Scenarios with Delta 11. Chapter 9: Delta for Reproducible Machine Learning Pipelines 12. Chapter 10: Delta for Data Products and Services 13. Section 3 – Operationalizing and Productionalizing Delta Pipelines
14. Chapter 11: Operationalizing Data and ML Pipelines 15. Chapter 12: Optimizing Cost and Performance with Delta 16. Chapter 13: Managing Your Data Journey 17. Other Books You May Enjoy

Optimizing with Delta

Delta's support for ACID transactions and quality guarantees helps ensure data reliability, thereby reducing superfluous validation steps and shortening the end-to-end time. This involves less downtime and triage cycles. Delta's support of fine-grained updates, deletes, and merges applies at a file level instead of to the entire partition, leading to less data manipulation and faster operations. This also leads to fewer compute resources, leading to cost savings.

Changing the data layout in storage

Optimizing the layout of the data can help speed up query performance, and there are two ways to do so, namely the following:

  • Compaction, also known as bin-packing
    • Here, lots of smaller files are combined into fewer large ones.
    • Depending on how many files are involved, this can be an expensive operation and it is a good idea to run it either during off-peak hours or on a separate cluster from the main pipeline to avoid unnecessary delays to the...
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