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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

Life with and without Delta

The tech landscape is changing rapidly, with the whole industry innovating faster today than ever before. A complex system is hard to change and is not agile enough to take advantage of the pace of innovation, especially in the open source world. Delta is an open source protocol that facilitates flexible analytic platforms as it comes prepackaged with a lot of features that benefit all kinds of data personas. With its support for ACID transactions and full compatibility with Apache Spark APIs, it is a no-brainer to adopt it for all your data use cases. This helps simplify the architecture both during development as well as during subsequent maintenance phases. Features such as the unification of batch and streaming, schema inference, and evolution take the burden off DevOps and data engineer personnel, allowing them to focus on the core use cases to keep the business competitive.

It is very easy to create a Delta table, store data in Delta format, or...

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