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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

Delta unifies all types of data

In this section, we will give you some examples of how to ingest (read) different data types into a Spark DataFrame and save it all in the Delta format in the data lake using a common API, df.write.format. ("delta") and this curated data is the single source of truth for all BI and AI use cases, as shown in the following diagram:

Figure 5.3 – Consolidating all your data in a central Delta Lake

The three main data types are structured, semi-structured, and unstructured, and Spark native APIs (along with partner-aided connectors) can ingest data from a wide variety of data sources to create a curated view that all consumers can access, depending on their privilege levels. In some cases, different Lines Of Business (LOBs) may want to have their own mini data lake, and that is perfectly fine as it follows a hub-spoke model of a central data repository for common access and specialized ones with tighter guardrails...

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