Data Modeling for MongoDB: Building Well-Designed and Supportable MongoDB Databases

Master how to data model MongoDB applications.

Congratulations You completed the MongoDB application within the given tight timeframe and there is a party to celebrate your application's release into production. Although people are congratulating you at the celebration, you are feeling some uneasiness inside. To complete the project on time required making a lot of assumptions about the data, such as what terms meant and how calculations are derived. In addition, the poor documentation about the application will be of limited use to the support team, and not investigating all of the inherent rules in the data may eventually lead to poorly-performing structures in the not-so-distant future.

Now, what if you had a time machine and could go back and read this book. You would learn that even NoSQL databases like MongoDB require some level of data modeling. Data modeling is the process of learning about the data, and regardless of technology, this process must be performed for a successful application. You would learn the value of conceptual, logical, and physical data modeling and how each stage increases our knowledge of the data and reduces assumptions and poor design decisions.

Read this book to learn how to do data modeling for MongoDB applications, and accomplish these five objectives:

1. Understand how data modeling contributes to the process of learning about the data, and is, therefore, a required technique, even when the resulting database is not relational. That is, NoSQL does not mean NoDataModeling
2. Know how NoSQL databases differ from traditional relational databases, and where MongoDB fits.
3. Explore each MongoDB object and comprehend how each compares to their data modeling and traditional relational database counterparts, and learn the basics of adding, querying, updating, and deleting data in MongoDB.
4. Practice a streamlined, template-driven approach to performing conceptual, logical, and physical data modeling. Recognize that data modeling does not always have to lead to traditional data models
5. Distinguish top-down from bottom-up development approaches and complete a top-down case study which ties all of the modeling techniques together.

掌握如何對MongoDB應用進行數據建模。

恭喜！你在給定的緊迫時間內完成了MongoDB應用程序，並且有一個派對來慶祝你的應用程序正式上線。儘管人們在慶祝中祝賀你，但你內心感到一些不安。為了按時完成項目，你不得不對數據做出很多假設，比如詞語的含義以及計算的推導方式。此外，關於應用程序的文檔資料很少，對於支持團隊來說幫助有限，而不調查數據中的所有內在規則可能最終導致性能不佳的結構。

現在，如果你有一台時光機，可以回到過去並閱讀這本書。你將會了解，即使是像MongoDB這樣的NoSQL數據庫也需要一定程度的數據建模。數據建模是學習數據的過程，無論使用的技術如何，這個過程都是成功應用的必要步驟。你將學習到概念、邏輯和物理數據建模的價值，以及每個階段如何增加我們對數據的了解，減少假設和設計不良的決策。

閱讀本書，學習如何為MongoDB應用進行數據建模，並實現以下五個目標：
1. 理解數據建模對於學習數據的過程的貢獻，因此即使結果數據庫不是關聯型，數據建模仍然是一種必需的技術。也就是說，NoSQL並不意味著NoDataModeling。
2. 知道NoSQL數據庫與傳統關聯型數據庫的區別，以及MongoDB的定位。
3. 探索每個MongoDB對象，了解它們與數據建模和傳統關聯型數據庫對應的基礎知識，並學習在MongoDB中添加、查詢、更新和刪除數據的基本操作。
4. 採用簡化的、模板驅動的方法進行概念、邏輯和物理數據建模。認識到數據建模不總是要導致傳統的數據模型。
5. 區分自上而下和自下而上的開發方法，並完成一個自上而下的案例研究，將所有建模技術結合在一起。