Online systems tend to track user’s actions. Gathering information about users’ behavior can increase the quality of their experience, which can lead to increased business income. In this article, we will show how to reimplement an existing Postgres database to a more complex analytics database like Amazon Redshift. The solution we want to reengineer is a tracking system for an online SQL learning platform like LearnSQL.com. The source implementation is built on a PostgreSQL database and contains two main tables:
Confused by online transaction processing (OLTP) and online analytical processing (OLAP) in the world of databases? Fear not. We have a simple explanation right here. To understand the differences between OLTP and OLAP, we first need to understand where they fit into the world of data and databases. And the answer is “data warehousing”. A data warehouse is a system used for reporting and data analysis. They are central repositories of data from one or more disparate sources, including relational databases.
Data modeling is the process of creating a structure to store data. Database modeling creates a model of your database; it captures information about the data that must be stored for an application. Applications are tools that implement some sort of business logic, so if we are creating a model of the data required by some application, then data modeling must start with collecting business requirements. If you're unsure about the steps involved in the database design process, I refer you to other articles like the description of database design steps and tips for better database design.
A starting point for keeping your database management hassle-free is a good database modeling tool. A good ER diagram is not only about a pretty picture; it can actually carry a lot of secondary data. For example, it may contain all necessary column constraints or additional SQL scripts to be run at a specified time. A good database modeling tool lets you create a physical ER diagram, oversees and validates your model (including your custom data types), and also lets you generate SQL scripts to set up your database or adjust it to the changes in the model.
Create good logical, physical, and conceptual data models using these expert database model preparation tips. To put it simply, a database model is a data model that determines the logical or physical structure of a database. Database design is the process of creating a database model. A database model is used to capture information about the data that must be stored in a database. If you're a bit unsure about the steps involved in the database design process, I suggest you read this description of database design steps.
Find out how to keep track of data model changes in Vertabelo using Git as a source control management tool. In this article we will discuss a possible way to use source control management tools like GIT to track all changes in your Vertabelo data model. We are also going to detail the code-database workflow that we use daily to develop Vertabelo itself as well as our other applications.
Database design goes way beyond just drawing lines and boxes. In this article, I reflect on the process of data modeling with an emphasis on best practices, as well as on how to use tools to implement those best practices to create a good database design. Database design is the process of producing a detailed model of a database. The start of database modeling involves getting a grasp on the business area and the functionality being developed.
Curious about why we model data with an entity-relationship diagram (ERD)? You've come to the right place. An entity-relationship diagram, or ER diagram, is essential for modeling the data stored in a database. It is the basic design upon which a database is built. ER diagrams specify what data we will store: the entities and their attributes. They also show how entities relate to other entities. Another advantage of ERDs is that they represent the data in a graphical manner.
Find out how to use the new Vertabelo Public API to access data models, SQL scripts, and more. One of the signs of a decent SaaS solution is its public API. A good API helps users to incorporate some automation into an application. Based on our experience in using other SaaS services, we have introduced a more document-oriented version of our public API. In this article, we will demonstrate how to use Vertabelo API to fetch a list of documents (models), SQL scripts, and data models in various formats.
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.