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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 1: Introduction to Data Engineering

"Water, water, everywhere, nor any drop to drink...

Data data everywhere, not a drop of insight!"

With the vast exodus of data around us, it is important to crunch it meaningfully and promptly to extract value from all the noise. This is where data engineering steps in. If collecting data is the first step, drawing useful insights is the next. Data engineering encompasses several personas that come together with their unique individual skill sets and processes to bring this to fruition. Data usually outlives the technology, and it continues to grow. New tools and frameworks come to the forefront to solve a lot of old problems. It is important to understand business requirements, the accompanying tech challenges, and typical shifts in paradigms to solve these age-old problems in a better manner.

By the end of this chapter, you should have an appreciation of the data landscape, the players, and the advances in distributed computing and cloud infrastructure that make it possible to support the high pace of innovation.

In this chapter, we will cover the following topics:

  • The motivation behind data engineering
  • Data personas
  • Big data ecosystem
  • Evolution of data stores
  • Trends in distributed computing
  • Business justification for tech spending

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