Vertabelo Database Modeler

Design patterns

Designing a Database for a Recruitment System

8 May 2019 Do you want to learn how to design a database system and map a business process to a data model? Then this post is for you. In this article, you’ll see how to design a simple database schema for a recruitment company. After reading this tutorial, you will be able to understand how database schemas are designed for real-world applications. The Recruitment System Business Process Before designing any database or data model, it is imperative to understand the basic business process for that system.

Design patterns

The Important Dates Data Model

by Emil Drkušić

3 Apr 2019 Are you forgetting something? A data model to help you remember important dates – before they happen. Have you ever forgotten an important date – your mom’s birthday or your anniversary? Or that you’re giving a lecture? Yup, things like that happen in real life. Maybe not to all of us, but to some of us (including me), they certainly do. To prevent such disasters, we’ll create a data model you could use as the background for an application that will notify you right on time.

Design patterns

The 9 Most Common Database Design Errors

by Emil Drkušić

12 Dec 2018 You’ve probably made some of these mistakes when you were starting your database design career. Maybe you’re still making them, or you’ll make some in the future. We can’t go back in time and help you undo your errors, but we can save you from some future (or present) headaches. Reading this article might save you many hours spent fixing design and code problems, so let’s dive in. I’ve split the list of errors into two main groups: those that are non-technical in nature and those that are strictly technical.

Design patterns

A look at algorithms used in RDBMS implementations of DWH systems

9 Nov 2016 When you’re using a data warehouse, some actions get repeated over and over. We will take a look at four common algorithms used to deal with these situations. Most DWH (data warehouse) systems today are on a RDBMS (relational database management system) platform. These databases (Oracle, DB2, or Microsoft SQL Server) are widely used, easy to work with, and mature – a very important thing to bear in mind when choosing a platform.

Design patterns

Again and Again! Managing Recurring Events In a Data Model

by Shantanu Kher

4 Oct 2016 A recurring event, by definition, is an event that recurs at an interval; it’s also called a periodic event. There are many applications which allow their users to setup recurring events. How does a database system manage recurring events? In this article, we’ll explore one way that they are handled. Recurrence is not easy for applications to deal with. It can become a hurricane task, especially when it comes to covering every possible recurring scenario – including creating bi-weekly or quarterly events or allowing the rescheduling of all future event instances.

Design patterns

Does Evolving Contact Information Mean Changing Your Database?

by Jean-Marc Reynaud

18 Aug 2016 There are a number of ways to contact someone these days, right? We have various phones: mobile and landline, personal and work. We have different addresses – residential, mailing, billing, business, etc. – and likely several email addresses, too. Don’t forget Skype and various messaging apps. Now add in LinkedIn and Facebook –which by the way, both have their own messaging elements. Not that long ago, many of these didn’t exist.

Design patterns

Five Common Dimensional Modeling Mistakes and How to Solve Them

16 Aug 2016 When designing your dimensional model, it is worthwhile to watch out for mistakes that commonly occur during the process. Specifically, they can occur in the relationships between tables, both in fact-to-dimension and dimension-to-dimension relationships. In this post, we’re going to take a closer look at five common modeling mistakes and what you can do about them. As you start a BI-related project, bulletproof dimensional design is hugely important. What makes a design bulletproof is the early mitigation of common design mistakes.

Design patterns

Dimensions of Dimensions: A Look at Data Warehousing’s Most Common Dimensional Table Types

26 Jul 2016 When we start a data warehousing project, the first thing we do is define the dimensional tables. Dimensional tables are the interesting bits, the framework around which we build our measurements. They come in many shapes and sizes. In this article, we are going to take a closer look at each type of dimensional table. Dimensional tables provide context to the business processes we wish to measure. They tell us all we need to know about an event.

Design patterns

Party Relationship Pattern. How to Model Relationships

by Jean-Marc Reynaud

14 Jul 2016 Relationships are everywhere: between people, between organizations, between organizations and people. Think about being an employee of a company, being a member of a project team, or being a subsidiary of another company. Is there a straightforward way to accurately model and manage all these relationships? Can we easily answer the question ‘Who knows who?’ A Quick Review of Relationships Exactly how this basic model was derived was described in my previous article, Flexible and Manageable Bill of Materials (BOM) Designs.

Design patterns

Facts about Facts: Organizing Fact Tables in Data Warehouse Systems

7 Jul 2016 The process of defining your data warehousing system (DWH) has started. You’ve outlined the relevant dimension tables, which tie to the business requirements. These tables define what we weigh, observe and scale. Now we need to define how we measure. Fact tables are where we store these measurements. They hold business data that can be aggregated across dimension combinations. But the fact is that fact tables are not so easily described – they have flavors of their own.

1
2
3
4