By Steve Hoberman
Congratulations! You accomplished the MongoDB program in the given tight time frame and there's a celebration to have fun your application's unencumber into creation. even supposing individuals are congratulating you on the social gathering, you feel a few uneasiness within. to accomplish the undertaking on time required creating a lot of assumptions in regards to the info, comparable to what phrases intended and the way calculations are derived. additionally, the terrible documentation concerning the program might be of restricted use to the help crew, and never investigating all the inherent principles within the information might ultimately result in poorly-performing constructions within the not-so-distant destiny.
Now, what in case you had a time computing device and will return and browse this e-book. you are going to 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 expertise, this procedure has to be played for a winning application. you will research the price of conceptual, logical, and actual facts modeling and the way each one level raises our wisdom of the knowledge and decreases assumptions and bad layout decisions.
Read this booklet to profit find out how to do facts modeling for MongoDB functions, and achieve those 5 objectives:
- Understand how information modeling contributes to the method of studying in regards to the info, and is, for that reason, a required procedure, even if the ensuing database isn't really relational. that's, NoSQL doesn't suggest NoDataModeling!
- Know how NoSQL databases vary from conventional relational databases, and the place MongoDB fits.
- Explore each one MongoDB item and understand how each one compares to their facts modeling and conventional relational database opposite numbers, and examine the fundamentals of including, querying, updating, and deleting facts in MongoDB.
- Practice a streamlined, template-driven method of acting conceptual, logical, and actual information modeling. realize that information modeling doesn't regularly need to result in conventional info models!
- Distinguish top-down from bottom-up improvement ways and entire a top-down case research which ties the entire modeling recommendations together.
This ebook is written for an individual who's operating with, or could be operating with MongoDB, together with enterprise analysts, facts modelers, database directors, builders, venture managers, and information scientists. There are 3 sections:
- In part I, Getting Started, we'll show the ability of information modeling and the tight connections to information types that exist whilst designing any form of database (Chapter 1), evaluate NoSQL with conventional relational databases and the place MongoDB matches (Chapter 2), discover every one MongoDB item and understand how every one compares to their facts modeling and standard relational database opposite numbers (Chapter 3), and clarify the fundamentals of including, querying, updating, and deleting info in MongoDB (Chapter 4).
- In part II, Levels of Granularity, we hide Conceptual facts Modeling (Chapter 5), Logical information Modeling (Chapter 6), and actual info Modeling (Chapter 7). become aware of the "ing" on the finish of every of those chapters. We specialise in the method of establishing each one of those types, that is the place we achieve crucial enterprise wisdom.
- In part III, Case Study, we'll clarify either most sensible down and backside up improvement methods and plow through a most sensible down case research the place we commence with enterprise requisites and finish with the MongoDB database. this situation examine will tie jointly the entire recommendations within the past seven chapters.
Nike Senior info Architect Ryan Smith wrote the foreword. Key issues are integrated on the finish of every bankruptcy so that it will strengthen thoughts. furthermore, this ebook is loaded with hands-on workouts, 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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Extra info for Data Modeling for MongoDB: Building Well-Designed and Supportable MongoDB Databases
No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without written permission from the publisher, except for the inclusion of brief quotations in a review. The author and publisher have taken care in the preparation of this book, but make no expressed or implied warranty of any kind and assume no for errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of the use of the information or programs contained herein.
It was about an hour before dawn, I was driving in France, and an important business meeting awaited me. I spotted a gas station up ahead that appeared to be open. I parked, went inside, and showed the attendant the address of my destination. I don’t speak French and the attendant didn’t speak English. The attendant did, however, recognize the name of the company I needed to visit. Wanting to help and unable to communicate verbally, the attendant took out a pen and paper. He drew lines for streets, circles for roundabouts along with numbers for exit paths, and rectangles for his gas station and my destination, an organization called “MFoods”: With this custom-made map, which contained only the information that was relevant to me, I arrived at my address without making a single wrong turn.
An attribute on a physical data model represents a database column or MongoDB field. The attribute Author Last Name might be represented as the column AUTH_LAST_NM within the relational table AUTH or the MongoDB field authorLastName within the Author collection. MONGODB FIELD = RDBMS FIELD (ATTRIBUTE AT PHYSICAL LEVEL) The concept of a physical attribute (also called a column or field) in relational databases is equivalent to the concept of a field in MongoDB. MongoDB fields contain two parts, a field name and a field value.