Graph Data Modeling for NoSQL and SQL: Visualize Structure and Meaning

Technics Publications
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Master a graph data modeling technique superior to traditional data modeling for both relational and NoSQL databases (graph, document, key-value, and column), leveraging cognitive psychology to improve big data designs.

From Karen Lopez’s Foreword:

In this book, Thomas Frisendal raises important questions about the continued usefulness of traditional data modeling notations and approaches:

Are Entity Relationship Diagrams (ERDs) relevant to analytical data requirements? Are ERDs relevant in the new world of Big Data? Are ERDs still the best way to work with business users to understand their needs? Are Logical and Physical Data Models too closely coupled? Are we correct in using the same notations for communicating with business users and developers? Should we refine our existing notations and tools to meet these new needs, or should we start again from a blank page? What new notations and approaches will we need? How will we use those to build enterprise database systems?

Frisendal takes us through the history of data modeling, enterprise data models and traditional modeling methods. He points out, quite contentiously, where he feels we have gone wrong and in a few places where we got it right. He then maps out the psychology of meaning and context, while identifying important issues about where data modeling may or may not fit in business modeling. The main subject of this work is a proposal for a new exploration-driven modeling approach and new modeling notations for business concept models, business solutions models, and physical data models with examples on how to leverage those for implementing into any target database or datastore. These new notations are based on a property graph approach to modeling data.

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About the author

 Thomas Frisendal is an experienced database consultant with more than 30 years on the IT vendor side and as an independent consultant. He has worked with databases and data modeling since the late 70s; since 1995 primarily on data warehouse projects. His area of excellence lies within the art of turning data into information and knowledge. Since 2005 he has specialized in business analysis, concept "harvesting" and mapping, modeling of information and data as well as design of analytical solutions. His approach to information-driven analysis and design is "New Nordic" in the sense that it represents the traditional Nordic values such as superior quality, functionality, reliability and innovation by new ways of communicating the structure and meaning of the business context. Thomas is an active writer and speaker, and at the time of writing also Chief Data Warehouse Architect at SimCorp. Thomas lives in Copenhagen, Denmark.

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Additional Information

Publisher
Technics Publications
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Published on
Sep 9, 2016
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Pages
214
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ISBN
9781634621236
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Language
English
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Genres
Computers / Data Modeling & Design
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Content Protection
This content is DRM protected.
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Available on Android devices
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Eligible for Family Library

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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:

 

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. 

A beginner's guide to get you up and running with Cassandra, DynamoDB, HBase, InfluxDB, MongoDB, Neo4j, and RedisKey FeaturesCovers the basics of 7 NoSQL databases and how they are used in the enterprisesQuick introduction to MongoDB, DynamoDB, Redis, Cassandra, Neo4j, InfluxDB, and HBaseIncludes effective techniques for database querying and managementBook Description

This is the golden age of open source NoSQL databases. With enterprises having to work with large amounts of unstructured data and moving away from expensive monolithic architecture, the adoption of NoSQL databases is rapidly increasing. Being familiar with the popular NoSQL databases and knowing how to use them is a must for budding DBAs and developers.

This book introduces you to the different types of NoSQL databases and gets you started with seven of the most popular NoSQL databases used by enterprises today. We start off with a brief overview of what NoSQL databases are, followed by an explanation of why and when to use them. The book then covers the seven most popular databases in each of these categories: MongoDB, Amazon DynamoDB, Redis, HBase, Cassandra, InfluxDB, and Neo4j. The book doesn't go into too much detail about each database but teaches

you enough to get started with them.

By the end of this book, you will have a thorough understanding of the different NoSQL databases and their functionalities, empowering you to select and use the right

database according to your needs.

What you will learnUnderstand how MongoDB provides high-performance, high-availability, and automatic scalingInteract with your Neo4j instances via database queries, Python scripts, and Java application codeGet familiar with common querying and programming methods to interact with RedisStudy the different types of problems Cassandra can solveWork with HBase components to support common operations such as creating tables and reading/writing dataDiscover data models and work with CRUD operations using DynamoDBDiscover what makes InfluxDB a great choice for working withtime-series dataWho this book is for

If you are a budding DBA or a developer who wants to get started with the fundamentals of NoSQL databases, this book is for you. Relational DBAs who want to get insights into the various offerings of popular NoSQL databases will also find this book to be very useful.

Leverage the power of MongoDB 4.x to build and administer fault-tolerant database applicationsKey FeaturesMaster the new features and capabilities of MongoDB 4.x Implement advanced data modeling, querying, and administration techniques in MongoDBIncludes rich case-studies and best practices followed by expert MongoDB developersBook Description

MongoDB is the best platform for working with non-relational data and is considered to be the smartest tool for organizing data in line with business needs. The recently released MongoDB 4.x supports ACID transactions and makes the technology an asset for enterprises across the IT and fintech sectors.

This book provides expertise in advanced and niche areas of managing databases (such as modeling and querying databases) along with various administration techniques in MongoDB, thereby helping you become a successful MongoDB expert. The book helps you understand how the newly added capabilities function with the help of some interesting examples and large datasets. You will dive deeper into niche areas such as high-performance configurations, optimizing SQL statements, configuring large-scale sharded clusters, and many more. You will also master best practices in overcoming database failover, and master recovery and backup procedures for database security.

By the end of the book, you will have gained a practical understanding of administering database applications both on premises and on the cloud; you will also be able to scale database applications across all servers.

What you will learnPerform advanced querying techniques such as indexing and expressionsConfigure, monitor, and maintain a highly scalable MongoDB environment Master replication and data sharding to optimize read/write performance Administer MongoDB-based applications on premises or on the cloud Integrate MongoDB with big data sources to process huge amounts of data Deploy MongoDB on Kubernetes containers Use MongoDB in IoT, mobile, and serverless environmentsWho this book is for

This book is ideal for MongoDB developers and database administrators who wish to become successful MongoDB experts and build scalable and fault-tolerant applications using MongoDB. It will also be useful for database professionals who wish to become certified MongoDB professionals. Some understanding of MongoDB and basic database concepts is required to get the most out of this book.

 How do we design for data when traditional design techniques cannot extend to new database technologies? In this era of big data and the Internet of Things, it is essential that we have the tools we need to understand the data coming to us faster than ever before, and to design databases and data processing systems that can adapt easily to ever-changing data schemas and ever-changing business requirements. There must be no intellectual disconnect between data and the software that manages it. It must be possible to extract meaning and knowledge from data to drive artificial intelligence applications. Novel NoSQL data organization techniques must be used side-by-side with traditional SQL databases. Are existing data modeling techniques ready for all of this?

The Concept and Object Modeling Notation (COMN) is able to cover the full spectrum of analysis and design. A single COMN model can represent the objects and concepts in the problem space, logical data design, and concrete NoSQL and SQL document, key-value, columnar, and relational database implementations. COMN models enable an unprecedented level of traceability of requirements to implementation. COMN models can also represent the static structure of software and the predicates that represent the patterns of meaning in databases.

This book will teach you:

the simple and familiar graphical notation of COMN with its three basic shapes and four line styles how to think about objects, concepts, types, and classes in the real world, using the ordinary meanings of English words that aren’t tangled with confused techno-speak how to express logical data designs that are freer from implementation considerations than is possible in any other notation how to understand key-value, document, columnar, and table-oriented database designs in logical and physical terms how to use COMN to specify physical database implementations in any NoSQL or SQL database with the precision necessary for model-driven development
The need to handle increasingly larger data volumes is one factor driving the adoption of a new class of nonrelational “NoSQL” databases. Advocates of NoSQL databases claim they can be used to build systems that are more performant, scale better, and are easier to program.

NoSQL Distilled is a concise but thorough introduction to this rapidly emerging technology. Pramod J. Sadalage and Martin Fowler explain how NoSQL databases work and the ways that they may be a superior alternative to a traditional RDBMS. The authors provide a fast-paced guide to the concepts you need to know in order to evaluate whether NoSQL databases are right for your needs and, if so, which technologies you should explore further.

The first part of the book concentrates on core concepts, including schemaless data models, aggregates, new distribution models, the CAP theorem, and map-reduce. In the second part, the authors explore architectural and design issues associated with implementing NoSQL. They also present realistic use cases that demonstrate NoSQL databases at work and feature representative examples using Riak, MongoDB, Cassandra, and Neo4j.

In addition, by drawing on Pramod Sadalage’s pioneering work, NoSQL Distilled shows how to implement evolutionary design with schema migration: an essential technique for applying NoSQL databases. The book concludes by describing how NoSQL is ushering in a new age of Polyglot Persistence, where multiple data-storage worlds coexist, and architects can choose the technology best optimized for each type of data access.

​This book undertakes to marry the concepts of "Concept Mapping" with a "Design Thinking" approach in the context of business analysis. While in the past a lot of attention has been paid to the business process side, this book now focusses information quality and valuation, master data and hierarchy management, business rules automation and business semantics as examples for business innovation opportunities. The book shows how to take "Business Concept Maps" further as information models for new IT paradigms. In a way this books redefines and extends business analysis towards solutions that can be described as business synthesis or business development. Business modellers, analysts and controllers, as well as enterprise information architects, will benefit from the intuitive modelling and designing approach presented in this book. The pragmatic and agile methods presented can be directly applied to improve the way organizations manage their business concepts and their relationships.

"This book is a great contribution to the information management community. It combines a theoretical foundation with practical methods for dealing with important problems. This is rare and very useful. Conceptual models that communicate business reality effectively require some degree of creative imagination. As such, they combine the results of business analysis with communication design, as is extensively covered in this book."

Dr. Malcolm Chisholm, President at AskGet.com Inc.

“Truly understanding business requirements has always been a major stumbling block in business intelligence (BI) projects. In this book, Thomas Frisendal introduces a powerful technique—business concept mapping—that creates a virtual mind-meld between business users and business analysts. Frisendal does a wonderful explaining and demonstrating how this tool can improve the outcome of BI and other development projects ."

Wayne Eckerson, executive director, BI Leadership Forum

 Data Modeling Made Simple will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices. This book is written in a conversational style that encourages you to read it from start to finish and master these ten objectives: Know when a data model is needed and which type of data model is most effective for each situation Read a data model of any size and complexity with the same confidence as reading a book Build a fully normalized relational data model, as well as an easily navigatable dimensional model Apply techniques to turn a logical data model into an efficient physical design Leverage several templates to make requirements gathering more efficient and accurate Explain all ten categories of the Data Model Scorecard Learn strategies to improve your working relationships with others Appreciate the impact unstructured data has, and will have, on our data modeling deliverables Learn basic UML concepts Put data modeling in context with XML, metadata, and agile development Book Review by Johnny Gay
In this book review, I address each section in the book and provide what I found most valuable as a data modeler. I compare, as I go, how the book's structure eases the new data modeler into the subject much like an instructor might ease a beginning swimmer into the pool.

This book begins like a Dan Brown novel. It even starts out with the protagonist, our favorite data modeler, lost on a dark road somewhere in France. In this case, what saves him isn't a cipher, but of all things, something that's very much like a data model in the form of a map! The author deems they are both way-finding tools.

The chapters in the book are divided into 5 sections. The chapters in each section end with an exercise and a list of the key points covered to reinforce what you've learned. I find myself comparing the teaching structure of the book to the way most of us learn to swim. 
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