Data Modeling for MongoDB: Building Well-Designed and by Steve Hoberman

By Steve Hoberman

Congratulations! You accomplished the MongoDB software in the given tight time-frame and there's a get together to have fun your application's unlock into construction. even though everyone is congratulating you on the get together, you feel a few uneasiness within. to accomplish the undertaking on time required creating a lot of assumptions in regards to the info, resembling what phrases intended and the way calculations are derived. moreover, the negative documentation concerning the program might be of restricted use to the help workforce, and never investigating the entire inherent ideas within the information may possibly ultimately result in poorly-performing buildings within the not-so-distant destiny.

Now, what should you had a time computing device and will return and skim this publication. you will examine that even NoSQL databases like MongoDB require a few point of information modeling.

Data modeling is the method of studying in regards to the facts, and despite know-how, this strategy needs to be played for a winning application. you will research the worth of conceptual, logical, and actual info modeling and the way each one level raises our wisdom of the knowledge and decreases assumptions and negative layout decisions.

Read this e-book to profit how one can do info modeling for MongoDB purposes, and achieve those 5 objectives:

  1. Understand how information modeling contributes to the method of studying in regards to the information, and is, hence, a required process, even if the ensuing database isn't relational. that's, NoSQL doesn't suggest NoDataModeling!
  2. Know how NoSQL databases fluctuate from conventional relational databases, and the place MongoDB fits.
  3. Explore every one MongoDB item and understand how every one compares to their information modeling and standard relational database opposite numbers, and research the fundamentals of including, querying, updating, and deleting info in MongoDB.
  4. Practice a streamlined, template-driven method of appearing conceptual, logical, and actual facts modeling. realize that facts modeling doesn't continuously need to bring about conventional information models!
  5. Distinguish top-down from bottom-up improvement methods and whole a top-down case learn which ties the entire modeling suggestions together.

This e-book is written for somebody who's operating with, or may be operating with MongoDB, together with enterprise analysts, facts modelers, database directors, builders, undertaking managers, and knowledge scientists. There are 3 sections:

  • In part I, Getting Started, we are going to demonstrate the ability of information modeling and the tight connections to information types that exist whilst designing any kind of database (Chapter 1), examine NoSQL with conventional relational databases and the place MongoDB suits (Chapter 2), discover each one MongoDB item and understand how every one compares to their information modeling and conventional relational database opposite numbers (Chapter 3), and clarify the fundamentals of including, querying, updating, and deleting information in MongoDB (Chapter 4).
  • In part II, Levels of Granularity, we conceal Conceptual information Modeling (Chapter 5), Logical information Modeling (Chapter 6), and actual information Modeling (Chapter 7). discover the "ing" on the finish of every of those chapters. We specialise in the method of creating every one of those versions, that is the place we achieve crucial enterprise wisdom.
  • In part III, Case Study, we are going to clarify either best down and backside up improvement methods and battle through a best down case learn the place we begin with company standards and finish with the MongoDB database. this example research will tie jointly all the recommendations within the prior seven chapters.

Nike Senior info Architect Ryan Smith wrote the foreword. Key issues are integrated on the finish of every bankruptcy so one can toughen suggestions. moreover, this publication is loaded with hands-on routines, in addition to their solutions supplied in App A. App B includes all the book’s references and App C the

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

Sample text

Each of these variations will be treated as a new field, with no warnings given. Everything will work great until you ask for your zipCode values back and only one document out of four has a field by that name. The flexible schema is a great innovation for quick evolution of your data model, and yet it requires discipline to harvest the benefits without experiencing major data quality issues and other frustrations as a result. Data modeling is essential for success, but it’s not rocket science, and with this book it is easier than ever to implement effectively.

In Section II, Levels of Granularity, we cover Conceptual Data Modeling (Chapter 5), Logical Data Modeling (Chapter 6), and Physical Data Modeling (Chapter 7). Notice the “ing” at the end of each of these chapters. We focus on the process of building each of these models, which is where we gain essential business knowledge. In Section III, Case Study, we will explain both top-down and bottom-up development approaches and complete a top-down case study where we start with conceptual data modeling and end with a MongoDB database.

We use the Embarcadero ER/Studio® Data Architect tool to build our data models throughout the text. com/products/er-studio-data-architect. There is an important distinction between the term relational database and the term relational. The term relational database refers to the technology on how the data is stored, whereas the term relational implies the technique of modeling business rules and applying normalization. The term object includes any data model component such as entities, attributes, and relationships.

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