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:
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! Know how NoSQL databases differ from traditional relational databases, and where MongoDB fits. 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. 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! Distinguish top-down from bottom-up development approaches and complete a top-down case study which ties all of the modeling techniques together.
This book is written for anyone who is working with, or will be working with MongoDB, including business analysts, data modelers, database administrators, developers, project managers, and data scientists. There are three sections:In Section I, Getting Started, we will reveal the power of data modeling and the tight connections to data models that exist when designing any type of database (Chapter 1), compare NoSQL with traditional relational databases and where MongoDB fits (Chapter 2), explore each MongoDB object and comprehend how each compares to their data modeling and traditional relational database counterparts (Chapter 3), and explain the basics of adding, querying, updating, and deleting data in MongoDB (Chapter 4).
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 go through a top down case study where we start with business requirements and end with the MongoDB database. This case study will tie together all of the techniques in the previous seven chapters.
Nike Senior Data Architect Ryan Smith wrote the foreword. Key points are included at the end of each chapter as a way to reinforce concepts. In addition, this book is loaded with hands-on exercises, along with their answers provided in Appendix A. Appendix B contains all of the book’s references and Appendix C contains a glossary of the terms used throughout the text.
This updated second edition provides guidance for database developers, advanced configuration for system administrators, and an overview of the concepts and use cases for other people on your project. Ideal for NoSQL newcomers and experienced MongoDB users alike, this guide provides numerous real-world schema design examples.Get started with MongoDB core concepts and vocabularyPerform basic write operations at different levels of safety and speedCreate complex queries, with options for limiting, skipping, and sorting resultsDesign an application that works well with MongoDBAggregate data, including counting, finding distinct values, grouping documents, and using MapReduceGather and interpret statistics about your collections and databasesSet up replica sets and automatic failover in MongoDBUse sharding to scale horizontally, and learn how it impacts applicationsDelve into monitoring, security and authentication, backup/restore, and other administrative tasks
Many businesses launch NoSQL databases without understanding the techniques for using their features most effectively. This book demonstrates the benefits of document embedding, polymorphic schemas, and other MongoDB patterns for tackling specific big data use cases, including:Operational intelligence: Perform real-time analytics of business dataEcommerce: Use MongoDB as a product catalog master or inventory management systemContent management: Learn methods for storing content nodes, binary assets, and discussionsOnline advertising networks: Apply techniques for frequency capping ad impressions, and keyword targeting and biddingSocial networking: Learn how to store a complex social graph, modeled after Google+Online gaming: Provide concurrent access to character and world data for a multiplayer role-playing game
The Data Model Scorecard is a data model quality scoring tool containing ten categories aimed at improving the quality of your organization’s data models. Many of my consulting assignments are dedicated to applying the Data Model Scorecard to my client’s data models – I will show you how to apply the Scorecard in this book.
This book, written for people who build, use, or review data models, contains the Data Model Scorecard template and an explanation along with many examples of each of the ten Scorecard categories. There are three sections:
In Section I, Data Modeling and the Need for Validation, receive a short data modeling primer in Chapter 1, understand why it is important to get the data model right in Chapter 2, and learn about the Data Model Scorecard in Chapter 3.
In Section II, Data Model Scorecard Categories, we will explain each of the ten categories of the Data Model Scorecard. There are ten chapters in this section, each chapter dedicated to a specific Scorecard category:
· Chapter 4: Correctness
· Chapter 5: Completeness
· Chapter 6: Scheme
· Chapter 7: Structure
· Chapter 8: Abstraction
· Chapter 9: Standards
· Chapter 10: Readability
· Chapter 11: Definitions
· Chapter 12: Consistency
· Chapter 13: DataIn Section III, Validating Data Models, we will prepare for the model review (Chapter 14), cover tips to help during the model review (Chapter 15), and then review a data model based upon an actual project (Chapter 16).
Data modelers render raw data-names, addresses, and salestotals, for instance-into information such as customer profiles andseasonal buying patterns that can be used for making criticalbusiness decisions. This book brings together thirty of the mosteffective tools for solving common modeling problems. The authorprovides an example of each tool and describes what it is, why itis needed, and how it is generally used to model data for bothdatabases and data warehouses, along with tips and warnings. Blanksample copies of all worksheets and checklists described areprovided in an appendix.
Companion Web site features updates on the latest tools andtechniques, plus links to related sites offering automatedtools.