First, notice the key icons to the left of various attribute names. There are a few additional points worth discussing. Click the down arrow button to the right of mysqlite.db (if this button does not appear double-click the text box containing mysqlite.db), find the student_records.db file on your machine, and select the file. Now, select “SQLite” from the driver dropdown menu and click “Next.” Then, navigate to “Database file name” in the “Database” section of the proceeding window. In the next window, name the connection student_records and click the “Next” button. In addition, if you have not downloaded the SQLite database from GitHub, go to this GitHub repository and either clone the entire repository or download student_records.db.Īfter opening DbVisualizer, navigate to “Tools” and then click “Connection Wizard…” from the drop-down menu. If you have not done so already, download and install DbVisualizer. Indeed, an ERD is like a treasure map to joined data! Using DbVisualizer to Generate an ERD Of course, databases in the real world are seldom as simple as this example, so it is beneficial to learn how to generate and read an ERD. Each table has two to three columns of data, referred to from here on out as attributes, and anywhere from four to 16 records (i.e., rows). The individual tables - student_courses, students, courses, and grades - are shown below. These data are stored across four tables in a relational database named student_records. Let’s consider a small sample of fictitious data on students currently enrolled in a hypothetical college (student names resembling real persons are entirely coincidental). NOTE: The SQLite database used in this article, student_records.db, can be found here on GitHub. It is quite simple to generate an ERD using a tool like DbVisualizer. Sound familiar? The good news is that it is easy to figure out how to join your tables together using an entity-relationship diagram (ERD), a diagram that shows how tables are related to each other. Now you are wondering how to join tables together to create a usable dataset. After getting access to the database itself you realize the quality of the documentation is poor - or the documentation is nonexistent. Has this ever happened to you? You are excited to start a new project using data from an unfamiliar relational database.
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