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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 9: Delta for Reproducible Machine Learning Pipelines

"Repetition is the mother of learning, the father of action, which makes it the architect of accomplishment."

– Zig Ziglar, American author and motivational speaker

In previous chapters, we established the pivotal nature of Delta in architecting data pipelines. What about Machine Learning (ML) pipelines? They involve different personas with different skills and needs. ML has been around for a while; what has changed lately is broad access to large datasets and affordable compute, which has now made it possible for everyone to tinker with ML. Can Delta stand the litmus test of building a reproducible ML pipeline just as effectively as a data pipeline? There are specific challenges and nuances in building a model, staging it in production, and repeating the process over and over again. In this chapter, we will look into these challenges and map the capabilities of Delta...

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