So you don't like writing all of your SQL CREATEs by hand? Design your database with Vertabelo and let it generate the SQL file for you! As you may already know, there are three different levels of data models: conceptual, logical, and physical data models. The conceptual model is the most abstract, while the logical model has a few more technical details. The physical data model defines all the details needed for a specific database: column data types, primary and foreign keys, constraints, indexes, sequences, views, and other physical objects.

Learn how to import your database into Vertabelo and generate its data model with our improved reverse engineering feature. We’re excited to announce the release of a new version of our popular reverse engineering tool. You've been asking for a user-friendly interface, and we heard you. If working with Java is not your thing, our new graphical user interface lets you click through the process in no time.  Download the upgraded reverse engineering tool here.

Find out how to design an Amazon Redshift schema in Vertabelo. Thanks to increasing volumes of data, analytical databases like Amazon Redshift are gaining market. We introduced Redshift support at the end of 2019; in this article, we will explain how to design a Redshift data model using Vertabelo. How to Create a Model Let's start with the data model creation process. To create a Redshift schema, please: Log into Vertabelo and click on Create new document.

Have you finished preparing your logical data model in Vertabelo? Awesome! In this article, we'll show you how to generate the physical data model from the logical model in Vertabelo. It’s just a few clicks away. Ready? Let's dive into it. Quick Intro In this article, we'll deal with a slightly modified version of Microsoft's Northwind Database. We often use it in our LearnSQL courses, such as Customer Behavior Analysis in SQL.

Amazon Redshift is one of the most popular cloud databases. Could it be your data-warehouse solution? Read on to find out if Amazon Redshift meets your needs. In 2012, Amazon announced its new cloud database system called Redshift. Basically, it is a data-warehouse solution intended for analytical systems, which can handle huge volumes of data—up to 1 petabyte (1024 TB). Amazon Redshift is available as a service (Database as a Service) and is a part of a bigger cloud ecosystem called Amazon Web Services (AWS).

Find out how to create logical data models in Vertabelo! If you have some experience with physical diagrams, this will be as easy as pie. There are three different levels of data models: conceptual, logical, and physical. Of these, the conceptual model is the most abstract, and the logical model has a few more technical details. The physical model has all the details of the physical database, such as data types (integer, decimal, money, varchar, etc.

There are many online portals which allow investors to lend money directly to individual borrowers – with no banks acting as intermediaries. What data model might underlie such a site? Online lending platforms bring borrowers and investors together and allow them to choose to whom they want to lend their money (in the case of investors) and who they want to borrow money from (in the case of borrowers). Some peer-to-peer lending sites also allow borrowers and investors to make their own deals in terms of lending rates (i.

Do you dream of running a marathon? Let’s look at the data model for an app that could take you from lazy couch potato to marathoner. What do you need to run a marathon? You’ll need enthusiasm and determination. A good pair of running shoes. And lots of physical training! Let’s say you have an app that helps you go from novice runner to marathon finisher. What would the data model look like?

Board games like dominoes are still very popular. Let’s take a look at dominoes from a data modeling point of view. The game of dominoes has been around for hundreds of years, and it’s played all over the world. As you might expect, this means a lot of variations in play! In this article, we’re going to examine a data model that could support the most common variants – draw and block.

Various apps promise to make your search for parking painless. Let’s examine this type of app using our data modeling glasses. What does the underlying model look like? In an earlier article, we explained how a parking lot is structured and how a data model can be designed to manage one. In this article, we are examining the data model for a parking app. You know these apps: they list nearby parking options, tell you the prices, and let you book or reserve a space or buy a parking pass.