Data Engineering For Modern Applications

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Data Engineering for Modern Applications

Data Engineering for Modern Applications
Author :
Publisher : Addition Publishing House
Total Pages : 220
Release :
ISBN-10 : 9789364225717
ISBN-13 : 9364225716
Rating : 4/5 (716 Downloads)

Book Synopsis Data Engineering for Modern Applications by : Dr. RVS Praveen

Download or read book Data Engineering for Modern Applications written by Dr. RVS Praveen and published by Addition Publishing House. This book was released on 2024-09-23 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: A resource designed for anybody interested in comprehending the whole lifecycle of data management in the current digital era is Data Engineering for Modern Applications. The book is organised into parts that systematically address key subjects. An introduction to data engineering principles is given first, followed by a thorough examination of data pipelines, storage options, and data transformation techniques. Data orchestration systems, cloud services, and distributed computing are just a few of the specialised tools and platforms that are being addressed in depth as the discipline of data engineering develops. This book places a lot of emphasis on using data engineering concepts in practical situations. The purpose of the chapters is to demonstrate best practices for creating, implementing, and overseeing scalable and effective data pipelines. Data Engineering for Modern Applications offers a useful framework that is easily applicable in a range of fields by including real-world examples and case studies. The book also discusses how data engineering supports AI and machine learning, outlining the procedures that guarantee data availability, consistency, and quality for these cutting-edge applications. This book serves as a manual for engineers, data scientists, and business professionals who are dedicated to using data in a future where decisions are made based on facts. This thorough guide will provide readers with the knowledge and self-assurance they need to address data difficulties, adjust to new technologies, and eventually help current data-driven systems be implemented successfully.


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