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Autor(en): 
  • Helder Russa
  • Rui Machado
  • Analytics Engineering with SQL and DBT: Building Meaningful Data Models at Scale 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Dezember 2023  
    Genre:  EDV / Informatik 
     
    COMPUTERS / Data Science / Data Analytics / COMPUTERS / Data Science / Machine Learning / COMPUTERS / Database Administration & Management / COMPUTERS / Languages / SQL / Data Analytics / Data capture and analysis / data maintenance collaborative data transformation / Data Mining
    ISBN:  9781098142384 
    EAN-Code: 
    9781098142384 
    Verlag:  O'Reilly 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 233 mm / B 178 mm / D 15 mm 
    Gewicht:  666 gr 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:

    With the shift from data warehouses to data lakes, data now lands in repositories before it's been transformed, enabling engineers to model raw data into clean, well-defined datasets. dbt (data build tool) helps you take data further. This practical book shows data analysts, data engineers, BI developers, and data scientists how to create a true self-service transformation platform through the use of dynamic SQL.

    Authors Rui Machado from Monstarlab and Hélder Russa from Jumia show you how to quickly deliver new data products by focusing more on value delivery and less on architectural and engineering aspects. If you know your business well and have the technical skills to model raw data into clean, well-defined datasets, you'll learn how to design and deliver data models without any technical influence.

    With this book, you'll learn:

    • What dbt is and how a dbt project is structured
    • How dbt fits into the data engineering and analytics worlds
    • How to collaborate on building data models
    • The main tools and architectures for building useful, functional data models
    • How to fit dbt into data warehousing and laking architecture
    • How to build tests for data transformations

      



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