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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

Scaling and high availability

Scalability refers to the elasticity of compute resources, meaning adding more compute capacity as data volume increases to support a heavier workload. It is sometimes necessary to scale down resources that aren't in use to save compute costs. Scaling can be of two types: vertical or horizontal. Vertical scaling refers to replacing existing node types with bigger instance types. This is not sustainable after a point because there is an upper bound on the largest possible instance. Horizontal scaling refers to the addition of more worker nodes of the same type and is truly infinitely scalable. Each serves different scenarios. If the largest partition is no longer divisible, we benefit from a bigger node type. However, the advantage is that some of the nodes can be turned off when there is low data volume. This is an infrastructure and architecture capability and not directly related to Delta.

High availability (HA) refers to the system uptime...

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