SQL Tutorial: Storing data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/database-design at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Let's discuss the different ways you can store data. Data can be stored in three different levels. The first is structured data, which is usually defined by schemas. Data types and tables are not only defined, but relationships between tables are also defined, using concepts like foreign keys. The second is unstructured data, which is schemaless and data in its rawest form, meaning it's not clean. Most data in the world is unstructured. Examples include media files and raw text. The third is semi-structured data, which does not follow a larger schema, rather it has an ad-hoc self-describing structure. Therefore, it has some structure. This is an inherently vague definition as there can be a lot of variation between structured and unstructured data. Examples include NoSQL, XML, and JSON, which is shown here on the right. Because its clean and organized, structured data is easier to analyze. However, it's not as flexible because it needs to follow a schema, which makes it less scalable. These are trade-offs to consider as you move between structured and unstructured data. You should already be familiar with traditional databases. They generally follow relational schemas. Operational databases, which are used for OLTP, are an example of traditional databases. Decades ago, traditional databases used to be enough for data storage. Then as data analytics took off, data warehouses were popularized for OLAP approaches. And, now in the age of big data, we need to analyze and store even more data, which is where the data lake comes in. I use the term "traditional databases" because many people consider data warehouses and lakes to be a type of database. Data warehouses are optimized for read-only analytics. They combine data from multiple sources and use m