šŸ—‚ļø Data Structures for Data Engineering

Efficient data structures are crucial for building performant data pipelines and systems.

Essential Data Structures

Arrays and Lists

  • Fixed Size Array: O(1) access, fixed memory
  • Dynamic Array: O(1) amortized insertion, grows as needed

Linked Lists

  • Singly Linked List: O(1) insertion/deletion, O(n) access
  • Doubly Linked List: Bidirectional traversal
  • Circular Linked List: Last node points to first

Hash Tables

  • Hash Map: O(1) average lookup, handling collisions
  • Hash Set: Unique values, O(1) operations
  • Hash Function: Maps keys to indices

Trees

  • Binary Tree: Each node has 0-2 children
  • Binary Search Tree: Ordered left < parent < right
  • AVL Tree: Self-balancing, O(log n) guaranteed
  • B-Tree: Multi-way tree, optimized for disk access
  • Trie: Prefix tree for string operations

Heaps

  • Min-Heap: Parent <= children
  • Max-Heap: Parent >= children
  • Operations: O(log n) insertion/deletion, O(1) peek

Graphs

  • Adjacency Matrix: O(1) edge lookup, O(VĀ²) space
  • Adjacency List: O(E) space, better for sparse graphs

Advanced Structures

Skip List

  • Probabilistic alternative to balanced trees
  • O(log n) operations on average
  • Simpler implementation than balanced trees

Bloom Filter

  • Probabilistic data structure
  • O(1) insertion and lookup
  • False positives possible, no false negatives

Segment Tree

  • Efficient range queries and updates
  • O(log n) operations
  • Used in competitive programming

Balanced BSTs

  • Red-Black Tree: Path-balanced
  • AVL Tree: Height-balanced
  • Splay Tree: Recently accessed items fast

Choosing the Right Structure

Task Best Structure Time
Fast lookup Hash Table O(1)
Sorted access BST O(log n)
Priority queue Heap O(log n)
Prefix search Trie O(k)
Range queries Segment Tree O(log n)
Set operations Bloom Filter O(1)

Implementation Considerations

Memory Usage

  • Avoid unnecessary copying
  • Use references instead of values
  • Consider cache locality for performance

Time Complexity

  • Analyze worst, average, best cases
  • Amortized analysis for dynamic structures
  • Trade-off between space and time

Practical Tips

  1. Use built-in data structures (Python lists, dicts)
  2. Understand underlying implementations
  3. Choose based on access patterns
  4. Profile before optimizing
  5. Document assumptions

Last updated: April 12, 2026
Difficulty: Intermediate
Prerequisites: Basic programming