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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

How to choose the right data format

Not all tools support all of the data formats. Every tool reads data off disk in chunks of blocks (KB/MB/GB), that is, minimizing these fetches helps improve the speed of access to data. Conversely, a single read for a single record brings back a lot more data than you may want, so caching it may help with subsequent queries. Different systems have different default block sizes. To choose the right data format, you need to consider several factors, such as the following:

  • What is the optimal tradeoff between cost, performance, and throughput considerations of ingestion and access patterns?
  • Are you constrained by storage or memory or CPU or I/O?
  • How large is a file? If your data is not splittable, we lose the parallelism that allows fast queries.
  • How many columns are being stored, and how many columns are used for the analysis?
  • Does your data change over time? If it does, how often does it happen, and how does it change?...
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