There are several basic and advanced data structures; they’re created to arrange data to make it useful for a specific purpose. At their core, data structures are intended to frame information in a way that makes that information clear and easily accessible to both humans and machines.
4 Types of Data Structures
- Linear data structures
- Tree data structures
- Hash tables
- Graph data structures
Structured vs. Unstructured Data: What’s the Difference?
Structured and unstructured data differ in many ways, however, the primary difference between the two is that structured data is defined and searchable while unstructured data is contained in its native form. There is vastly more unstructured data in existence, which makes up 80 percent of all enterprise data. This percentage continues to grow, which presents opportunities for forward-thinking businesses to mine data insights.
Other differences include:
- Structured data is quantitative while unstructured data is qualitative.
- Structured data is stored in data warehouses while unstructured data is contained in data lakes.
- Structured data can be immediately analyzed while unstructured data requires processing and transformation.
- Structured data follows predefined formats while unstructured data comes in many different formats.
What Are Different Data Structures?
The four basic data structure types are linear data structures, tree data structures, hash data structures and graph data structures.
1. Linear Data Structures
Linear data structures include arrays, or finite groups of data, with memory locations that allow elements to be accessed through an index key and linked lists. Linked lists order elements within a list so they can be sporadically placed within memory.
2. Tree Data Structures
Tree data structures are hierarchical and present a root value with subsets of children that are represented as linked nodes. Tree data structures come in many varieties with several distinct characteristics, including binary trees, binary search trees, red-black trees, weight-balanced trees and binary heaps.
3. Hash Tables
Hash tables map keys to values, abstracted by additional behaviors through the use of high-level programming languages. Some variations of hash tables include separate chaining and linear probing.
4. Graph Data Structures
Graph data structures are implemented through graph theory principles, utilizing nodes and edges to weigh graphs and represent the directional flow of networks.
Data Structures and Algorithms: What’s the Difference?
Data structures and algorithms are entirely different concepts. Data structures represent data relationships and provide methods of sorting and accessing data effectively. Algorithms are a detailed set of instructions that computer software, web pages, programs and hardware use to complete a task.
Algorithms are well-defined, finite sequences of instructions you can be implement with a computational system to reach a repeatable goal. Some examples of algorithms include sorting algorithms, searching algorithms and shortest path algorithms. Each provides a way for a computer to not only access necessary existing data but also to respond to a command in order to complete a task. We can design algorithms to be efficient for specific data structures. If an algorithm is designed for a specific data structure, using it with a different data structure will lead to inefficiencies in the results.