Your

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Karen LopezSr. Project Manager / Architect

Infoadvisors@datachick

#BCDModeling

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Kare

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Dan McCrearyPrincipal

Kelly-McCreary & Associates@dmccreary

NoSQL And Data ModelingBig Challenges in Data Modeling

Making Sense of NoSQL clearly

and concisely explains the

concepts, features, benefits,

potential, and limitations of

NoSQL technologies. Using

examples and use cases,

illustrations, and plain, jargon-

free writing, this guide shows

how you can effectively

assemble a NoSQL solution to

replace or augment the

traditional RDBMS you have

now.

Data Models – Traditional Process

Conceptual (Data) Model

Logical Data Model

Physical Data

Model(s) OLTP

OLTPOLTP OLTP

OLTP

MARTMART

OLTP

OLTPOLTP

NoSQL Data Store Types

• Key Value

• Graph

• Columnar

• Document

• XML

• ….more?

Traditional Data Modeler Involvement

Project Initiation

Architecture and

Infrastructure Design

SW Requirements

Development

Deployment

Modern Data Modeler Involvement

Project Initiation

Architecture and

Infrastructure Design

SW Requirements

Development

Deployment

ETL

EDW

Data Mart

Data Mart

Hadoop

ETL

EDW

Analytics Mart

Data Mart

“Every design decision should include cost, benefit and risk”

• - Karen Lopez

10 Tips For Modeling in a Hybrid World

1. Models require a modeler

2. Data modeling tools are essential – choose wisely

3. There are many types of data models: know which ones you need

4. Modeling does not have to happen at the same time in every project. It should happen at the right time

5. Modeling is not just schema design. Think outside the boxes and lines

10 Tips for Modeling in a Hybrid World

6. A data model is much more than a diagram

7. You will need training. More than you think.

8. Team members may not understand modeling. They will need training

9. NoSQL is not one thing. Learn many patterns

10.Modern data architectures are likely hybrid solutions. You can’t just support one part.

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.

See you 24 JulyBig Data - Myths, Misunderstandings and Mistakes

Thank you!

Karen Lopez@datachick