Data Source Types

Data Source Types

1. Structured data is often organized to support transactional and analytical applications. Structured data is most commonly stored in relational databases but can also be stored in non-relational databases. This data source type is valuable because you can gain insight into overarching trends by efficiently running powerful data queries and analysis.

2. Semistructured data can be just as predictable and organized as structured data. The difference is that semistructured data is flexible and can be updated without the requirement to change the schema for every single record in a table. Semistructured data allows a user to capture any data in any structure as data evolves and changes over time. Semistructured data is often stored in non-relational stores.

3. Unstructured data is not organized in any distinguishable or predefined manner. Common stores for unstructured data are non-relational key-value databases. Unstructured data is full of irrelevant information, which means data needs to first be processed to perform any kind of meaningful analysis.

Examples of data considered to be unstructured are text messages, word processing documents, videos, photos, and other images. These files are not organized other than being placed into a file system, object store, or another repository such as a data lake.