To model data is to know data. In this course, you will learn how to create a relational data model, incorporate entities from a decomposed JSON document, create a dimensional model, and build a database from a script generated from your data model.
How can you get value from your data if you don’t know your data? How many data projects have you started only to go through the same process of hunting down someone who knows something about the data you need to work with? Do they still work with the company? This is why we model data. In this introductory course, Enterprise Data Modeling: Getting Started, you will learn about the relational data model and normalization techniques. First, you will explore the NoSQL data stores and the JSON data format. Next, you will learn about OLAP and the dimensional model. Finally, you will take what you have learned and put into practice with a few demonstrations including creating a physical data model from a logical model to generate a script to build a database. By the end of the course, you will have a firm understanding of what a data model is, different types of data models, how to apply normalization, the JSON data format, and how to create both a relational and dimensional data model.
Joe is a senior data engineer and has worked as a data management professional in various capacities and titles for the last nineteen years. In his current role, Joe is working to build a competency around big data, predictive analytics, and BI development.
Course Overview Hi everyone, my name is Joe Cline, and welcome to my course, Enterprise Data Modeling: Getting Started. Have you ever been asked to create a data model, but were confused by terminology or how to apply the relational data model's normal forms? Have you ever had to work with NoSQL data stores, but didn't know how to model that data for import into your relational environment? Over the last decade more and more NoSQL database systems have been developed, by the relational database system is still the most common. We also still use the dimensional model for data warehouses, But did you know that you can use it for Hadoop data lakes too? Because some of the major topics that we'll cover in this course include terminology and concepts of data modeling, the relational data model including an explanation of normalization and normal forms, NoSQL data structures, including the JSON data format, dimensional modeling for reporting and analytics, and there'll be plenty of demos to show you how to put what you've learned into practice with a professional, enterprise-grade data modeling tool. By the end of this course you will have a firm understanding of what a data model is, the difference between different types of data models, how to apply normalization techniques to third normal form, how to break down a JSON data format, create a dimensional data model, promote your relational logical data model to a physical data model, and generate a script so you can run and build an actual database ready for application development. This is an introductory data modeling course so no previous data modeling experience is necessary, but after completing this course you should feel comfortable creating and maintaining models for the enterprise data tier. I hope you'll join me on this journey to learn about modeling enterprise data with the Enterprise Data Modeling: Getting Started course, only on Pluralsight.
Understanding Data Modeling Concepts Hi, and welcome to Understanding Data Modeling Concepts. In the course introduction I described what you can expect to learn if you follow along to the end of the course. As mentioned before, this is a beginner course for data modeling, so if you're not already familiar with what a database is, or does, or how it might fit into the enterprise environment, or how a data modeler interacts with other data professionals, like data governance directors, DBAs, data architects, etc. , I'll keep my examples as simple as I can. If you do know what a database is, even if you're only experience is with Excel, or maybe you've written some database queries, you should be fine. That being said, if at any time you feel concepts are getting away from you, feel free to stop and go watch my intro course, Big Picture: Enterprise Data Management, for a foundational understanding of what we are discussing here today. I mean, if you're already paying for this subscription or already have enough time remaining on your free trial, what would it hurt, right? Anyway, I don't want you to feel like you're missing something, not to mention, you can always shoot me a tweet @d8ajoe, or connect with me on LinkedIn. Conversely, if you are more advanced and already comfortable with the terminology and concepts, you can skip ahead. In this module you'll get an introduction to common terminology, and then understanding of data and modeling concepts, and finally, discover how your data model fits into the enterprise. Now that that's out of the way, I have to ask you a very serious question, are you ready to have another fun-filled, action-packed learning adventure? Fantastic, let's continue on with Understanding Enterprise Data Modeling Concepts.
Beginning Dimensional Modeling Welcome back to Enterprise Data Modeling: Getting Started. So far in the course we learned about data modeling concepts and terminology, the relational data model and normalization, various new SQL database solutions and the popular JSON data format, and creating a relational logical data model from NoSQL data by breaking down a JSON document. In this module you'll learn why we have a separate data model for analytic processing, some basic terminology and concepts of dimensional modeling, how key performance indicators, or KPIs, are needed for a dimensional model, and then it's demo time where we'll take the data elements and the relational logical data model we created earlier in the course and create a dimensional data model. Okay, we've got a lot to cover, so let's get started. I'll see you in the next clip.
Building a Database from DDL Hi, and welcome back. In this module we're going to take our relational logical data model we created earlier in the course, create a physical data model, and then we're going to generate a DDL script, and if you remember DDL stands for data definition language. Data definition language is a type of SQL that is used to create the objects in a database. And it's all based on our relational logical data model. We're going to take that script and we're going to deploy it to a SQL Server to create an application-ready OLTP database. Let's move onto the demo and get to work, I'll see you there.