Structured vs. Semi-Structured vs. Unstructured Data
Explore the key characteristics that distinguish structured, semi-structured, and unstructured data. Understand how each type is stored, examples of their formats, and the challenges they present in data science workflows.
We'll cover the following...
Structured Data
It comes with a predefined format and structure. Structured Data is usually stored in Relational Databases. It is easy to deal with in the Data Science domain.
Examples
- Here’s a simple table with columns of diverse types. It can be stored in any Relational Database, Excel file, etc.
| Sepal_length | Sepal_width | Petal_length | Petal_width | Species |
|---|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 4.9 | 3.0 | 1.4 | 0.2 | versicolor |
| 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 4.6 | 3.1 | 1.5 | 0.2 | virginica |
Semi-Structured Data
It comes with a predefined format and structure but is not stored in the Relational Database.
Examples
- JSON (Javascript Object Notation)
- XML (Extensible Markup Language)
Unstructured Data
It does not have a specific format and lacks structure. It is the type of data that presents many challenges to handle in the Data Science domain.
Examples
- Images
- Videos
- Speech
- Email or any article as seen below
Quantum computing uses quantum-mechanical phenomena such as superposition and entanglement, to perform computation. A quantum computer performs such computation, which can be implemented theoretically or physically. There are currently two main approaches to physically implementing a quantum computer: analog and digital. Analog approaches are further divided into the quantum simulation, quantum annealing, and adiabatic quantum computation.
- Any webpage like the Educative homepage