1. Arrays: Basic structure storing elements of the same type, useful for random access.
  2. Linked Lists: Dynamic structure where each element points to the next, efficient for insertions and deletions.
  3. Stacks: LIFO (Last In, First Out) structure, commonly used for managing function calls and undo mechanisms.
  4. Queues: FIFO (First In, First Out) structure, suitable for tasks like job scheduling and breadth-first search.
  5. Trees: Hierarchical structure with a root node and branches, used for hierarchical relationships and searching.
  6. Graphs: Networks of nodes and edges, versatile for modeling relationships in social networks, maps, etc.
  7. Hash Tables: Key-value pairs for fast data retrieval, often used in dictionaries, caches, and databases.
  8. Heaps: Tree-based structure with each node having a value greater (or smaller) than its children, useful for priority queues.
  9. Trie: Tree-like structure for storing a dynamic set or associative array, commonly used in autocomplete systems.
  10. Sets: Collection of distinct elements with operations like union, intersection, and difference.
  11. Maps/Dictionaries: Key-value pairs for efficient data retrieval, often used in various applications for storing and retrieving data.
  12. Sparse Matrix: Efficient representation for matrices with a large number of zero elements.
  13. B-trees: Balanced tree structure, frequently used in databases and file systems for efficient search and insertion.
  14. Red-Black Trees: Self-balancing binary search trees, commonly used in computer science libraries.
  15. Skip Lists: Data structure that allows for faster search and insertion than traditional linked lists.
  16. Union-Find (Disjoint Set): Used for tracking a partition of a set into disjoint subsets.
  17. Bloom Filter: Probabilistic data structure for testing whether an element is a member of a set, useful in caching and spell checking.
  18. Priority Queues: Abstract data type supporting the retrieval of the highest (or lowest) priority element.

Data Structures Use Case Examples

These are just a few examples, and each data structure has its own strengths and use cases depending on the requirements of the specific problem at hand.


Linked Lists:





Hash Tables:





Sparse Matrix:

These examples illustrate how various data structures play a crucial role in optimizing data management and improving the performance of both web and mobile applications. The choice of a specific data structure depends on the application’s requirements and the nature of the data being processed.