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Simplifying Data Engineering and Analytics with Delta

You're reading from  Simplifying Data Engineering and Analytics with Delta

Product type Book
Published in Jul 2022
Publisher Packt
ISBN-13 9781801814867
Pages 334 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Anindita Mahapatra Anindita Mahapatra
Profile icon Anindita Mahapatra

Table of Contents (18) Chapters

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

The main features of Delta

The features we will define in this section are equivalent to weapons in an arsenal that Delta provides so that you can create data products and services. These will help ensure that your pipelines are built around sound principles of reliability and performance to maximize the effectiveness of the use cases built on top of these pipelines. Without any more preamble, let's dive right in.

ACID transaction support

In a cloud ecosystem, even the most robust and well-tested pipelines can fail on account of temporary glitches, reinforcing the fact that a chain is as strong as its weakest link and it doesn't matter that a long-running job failed in the first few minutes or the last few minutes. Cleaning up the subsequent mess in a distributed system would be an arduous task. Worse still is the fact that partial data has now been exposed to consumers who may use it in their dashboards or models to arrive at wrong insights and trigger incorrect alarms...

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