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Simplifying Data Engineering and Analytics with Delta

Simplifying Data Engineering and Analytics with Delta

By : Anindita Mahapatra
4.9 (15)
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Simplifying Data Engineering and Analytics with Delta

Simplifying Data Engineering and Analytics with Delta

4.9 (15)
By: Anindita Mahapatra

Overview of this book

Delta helps you generate reliable insights at scale and simplifies architecture around data pipelines, allowing you to focus primarily on refining the use cases being worked on. This is especially important when you consider that existing architecture is frequently reused for new use cases. In this book, you’ll learn about the principles of distributed computing, data modeling techniques, and big data design patterns and templates that help solve end-to-end data flow problems for common scenarios and are reusable across use cases and industry verticals. You’ll also learn how to recover from errors and the best practices around handling structured, semi-structured, and unstructured data using Delta. After that, you’ll get to grips with features such as ACID transactions on big data, disciplined schema evolution, time travel to help rewind a dataset to a different time or version, and unified batch and streaming capabilities that will help you build agile and robust data products. By the end of this Delta book, you’ll be able to use Delta as the foundational block for creating analytics-ready data that fuels all AI/BI use cases.
Table of Contents (18 chapters)
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1
Section 1 – Introduction to Delta Lake and Data Engineering Principles
5
Section 2 – End-to-End Process of Building Delta Pipelines
13
Section 3 – Operationalizing and Productionalizing Delta Pipelines

Chapter 3: Delta – The Foundation Block for Big Data

"Without a solid foundation, you will have trouble creating anything of value."

– Erica Oppenheimer, on academic mastery

In the previous chapters, we looked at the trends in big data processing and how to model data. In this chapter, we will look at the need to break down data silos and consolidate all types of data in a centralized data lake to get holistic insights. First, we will understand the importance of the Delta protocol and the specific problems that it helps address. Data products have certain repeatable patterns and we will apply Delta in each situation to analyze the before and after scenarios. Then, we will look at the underlying file format and the components that are used to build Delta, its genesis, and the high-level features that make Delta the go-to file format for all types of big data workloads. It makes not only the data engineer's job easier, but also other data personas...

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