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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

Understanding what a data warehouse really solves

At its core, a data warehouse is a data repository of disparate data sources, mostly structured to help answer BI analysis questions using reports and dashboards. It is typically used by heads of departments and businesses to get a bird's-eye view of how the organization is doing holistically using SQL interfaces. It analyzes operational data to predict growth and identify bottlenecks and other business stragglers to help the business evaluate its performance using KPI metrics in the face of its competitors and plan more strategically. The older on-premises offerings are moving into the cloud to take advantage of the elasticity of cloud computing.

This can be simplified into two main parts:

  • Base underlying storage:
    • Cloud storage is increasingly popular on account of it being affordable, scalable, and reliable.
  • The analytic layer built on top of storage:
    • The analytic layer houses several pieces beyond just data, such...

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