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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

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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