Simplifying Data Engineering and Analytics with Delta

Create analytics-ready data that fuels artificial intelligence and business intelligence

By: Anindita Mahapatra

Write A Review

eText | 29 July 2022 | Edition Number 1

At a Glance

Format
ePUB

eText

$59.39

or 4 interest-free payments of $14.85 with

Instant online reading in your Booktopia eTextbook Library *

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

Explore how Delta brings reliability, performance, and governance to your data lake and all the AI and BI use cases built on top of it

Key Features

Learn Delta's core concepts, features and what makes it a perfect match for data engineering and analysis
Using a scenario-based approach, solve business challenges of different industry verticals
Make optimal choices by understanding the various tradeoffs provided by Delta

Book Description

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 upon. This is especially important considering the same architecture is reused when onboarding new use cases.

In this book, you'll learn the principles of distributed computing, data modeling techniques, 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. Next, 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.

What you will learn

Explore the key challenges of traditional data lakes
Appreciate the unique features of Delta that come out of the box
Address reliability, performance, and governance concerns using Delta
Analyze the open data format for an extensible and pluggable architecture
Handle multiple use cases to support BI, AI, streaming, and data discovery
Discover how common data and ML design patterns are executed on Delta
Build and deploy data and ML pipelines at scale leveraging Delta

Who This Book Is For

Individuals in the data domain such as data engineers, data scientists, ML practitioners and BI analysts working with big data will be able to put their knowledge to work with this practical guide to executing pipelines and supporting diverse use cases using the Delta protocol. Basic knowledge of SQL, Python programming, and Spark is required to get the most out of this book.