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size
22.86 x 17.78 x 2.16 cm
Product Information
Ideal For
General Readers, Fiction Lovers, Mythology & Historical Fiction Readers, Indian Mythology Enthusiasts, Young Adults (YA) & Adults, College Students, Competitive Exam Aspirants (General Reading / Culture), Gift Purpose (Books Gift Set), Spiritual & Philosophy Interest Readers, Library / Book Collection Buyers
Country Of Origin
India
About Author
Joe Reis Is A Business-Minded Data Nerd Who'S Worked In The Data Industry For 20 Years, With Responsibilities Ranging From Statistical Modeling, Forecasting, Machine Learning, Data Engineering, Data Architecture, And Almost Everything Else In Between. Joe Is The Ceo And Cofounder Of Ternary Data, A Data Engineering And Architecture Consulting Firm Based In Salt Lake City, Utah. In Addition, He Volunteers With Several Technology Groups And Teaches At The University Of Utah. In His Spare Time, Joe Likes To Rock Climb, Produce Electronic Music, And Take His Kids On Crazy Adventures.
Author Name
Joe Reis
Genre
Computers & Technology
No Of Pages
448
Publisher
O'Reilly Media
Isbn Code
9.78E+12
Product Description
- Premium Quality: Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice, and this practical book delivers exactly that.
- Product Design: Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers.
- User Experience: With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.
- Versatile Occasion: Suited for software engineers, data scientists, analysts, college students, and professionals seeking a structured and authoritative foundation in modern data engineering practice.
- Quality Assurance: Spanning 448 pages and published under ISBN 9781492080510, this title draws on decades of combined industry expertise from authors who have worked across statistical modelling, machine learning, data architecture, and engineering.