Read e-book online Data Modeling for MongoDB Building Well-Designed and PDF

By Steve Hoberman

ISBN-10: 1935504703

ISBN-13: 9781935504702

Congratulations! You accomplished the MongoDB software in the given tight time frame and there's a get together to have a good time your application's liberate into construction. even though everyone is congratulating you on the occasion, you feel a few uneasiness within. to accomplish the undertaking on time required creating a lot of assumptions in regards to the info, equivalent to what phrases intended and the way calculations are derived. furthermore, the negative documentation in regards to the software can be of restricted use to the help group, and never investigating the entire inherent ideas within the facts may well finally bring about poorly-performing constructions within the not-so-distant destiny.

Now, what if you happen to had a time computing device and will return and browse this booklet. you are going to research that even NoSQL databases like MongoDB require a few point of knowledge modeling.
Data modeling is the method of studying concerning the facts, and despite expertise, this method has to be played for a winning application. you are going to research the price of conceptual, logical, and actual facts modeling and the way every one level raises our wisdom of the knowledge and decreases assumptions and terrible layout decisions.

Read this publication to benefit how one can do information modeling for MongoDB functions, and achieve those 5 objectives:

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

This booklet is written for somebody who's operating with, or should be operating with MongoDB, together with company analysts, information modelers, database directors, builders, venture managers, and information scientists. There are 3 sections:

  • In part I, Getting Started, we are going to display the facility of information modeling and the tight connections to facts types that exist while designing any form of database (Chapter 1), examine NoSQL with conventional relational databases and the place MongoDB suits (Chapter 2), discover every one MongoDB item and understand how every one compares to their information modeling and standard 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 hide Conceptual facts Modeling (Chapter 5), Logical facts Modeling (Chapter 6), and actual facts Modeling (Chapter 7). realize the "ing" on the finish of every of those chapters. We specialize in the method of creating each one of those types, that's the place we achieve crucial enterprise knowledge.
  • In part III, Case Study, we are going to clarify either best down and backside up improvement ways and wade through a best down case research the place we commence with enterprise requisites and finish with the MongoDB database. this example research will tie jointly the entire strategies within the earlier seven chapters.

Nike Senior information Architect Ryan Smith wrote the foreword. Key issues are integrated on the finish of every bankruptcy in order to make stronger suggestions. moreover, this e-book is loaded with hands-on routines, besides their solutions supplied in App A. App B comprises all the book’s references and App C the

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With NoSQL, the focus is on data performance and availability. ” Key–value databases allow the application to store its data in only two columns (“key” and “value”), with more complex information sometimes stored within the “value” columns. Column-oriented databases work with more complex data types such as unformatted text and imagery, and this data can also be defined on the fly. A graph database is designed for data whose relations are well represented as a set of nodes with an undetermined number of connections between those nodes.

The attribute Gross Sales Amount measures the monetary value of a transaction. As with entities, attributes can exist at conceptual, logical, and physical levels. An attribute at the conceptual level must be a concept both basic and critical to the business. We do not usually think of attributes as concepts, but depending on the business need, they can be. When I worked for a telecommunications company, Telephone Number was an attribute that was so important to the business that it was represented on a number of conceptual data models.

Duplicate names are not allowed. The same document cannot contain two or more of the same field names at the same level of depth. Just like a relational database table cannot contain the element customerLastName twice, a MongoDB document cannot contain the same field twice at the same level of depth. You can, however, have the same field name at a different level of depth. For example, although { customerLastName : “Smith”, customerLastName : “Jones” } is not allowed, { customerLastName : { customerLastName : “Jones” } } is allowed.

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

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