Search icon
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
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

Moving toward real-time systems

As their names suggest, batch is a form of periodic ingestion of data, whereas streaming is a process where data ingestion is either continuous or in micro-batches. There is no denying that the trend is toward the real-time ingestion, analysis, and consumption of data. This gives rise to the question of why is every pipeline not a streaming one?

There may be several producers of data for the same target table. Some may be fast-moving, while others could be slower. If the nature of your data is such that it comes once a month, then we certainly do not want to have compute running more frequently than once a month from a cost savings perspective. Hence, some folks may say that cases such as these force us to have batch ingestion. In this chapter, we will present an argument to justify that batch is actually a type of streaming workload and that all workloads can be expressed as a streaming pipeline. You may argue, 'Isn't streaming more complex...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime}