Analytics Engineering with SQL and Dbt: Building Meaningful Data Models at Scale

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 Helder 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

隨著從資料倉庫轉向資料湖，資料現在在轉換之前會先落地到儲存庫中，使工程師能夠將原始資料建模為乾淨、明確的資料集。DBT（資料建置工具）可以幫助您更進一步處理資料。這本實用書向數據分析師、數據工程師、BI開發人員和數據科學家展示了如何通過使用動態SQL創建真正的自助轉換平台。

作者Rui Machado來自Monstarlab，Helder Russa來自Jumia，他們向您展示如何通過更多關注價值交付而少關注架構和工程方面，快速交付新的數據產品。如果您對業務非常了解並具備將原始資料建模為乾淨、明確的資料集的技術能力，您將學習如何在沒有任何技術影響的情況下設計和交付數據模型。

這本書將教您：
- DBT是什麼以及DBT項目的結構
- DBT如何適用於數據工程和分析領域
- 如何協作建立數據模型
- 建立有用、功能性數據模型的主要工具和架構
- 如何將DBT融入資料倉儲和資料湖架構
- 如何為數據轉換建立測試