Data Processing & Algorithms
Master advanced data structures, streaming algorithms, and high-performance data processing techniques
Data Processing Examples
From streaming algorithms to advanced data structures
Stream Processing Engine
Parse large JSON/CSV files without loading them entirely into memory
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Bloom Filter Implementation
Build a probabilistic data structure for fast membership testing
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SQL-Like Query Engine
Create a basic query engine that can parse and execute SQL-like statements
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Merkle Tree Builder
Implement a Merkle tree for data integrity verification (blockchain concept)
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Real-Time Log Processor
Build a log tailing system with filtering, alerts, and pattern detection
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LRU Cache Implementation
Create an efficient Least Recently Used cache with O(1) operations
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Data Pipeline Framework
Build a configurable ETL pipeline with transformation stages
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Data Processing Mastery Path
Progress from basic algorithms to advanced distributed processing
Basic data structures and algorithms
Stream processing and memory efficiency
Advanced structures and query engines
Distributed processing and scaling
Prerequisites
- Strong understanding of data structures and algorithms
- Knowledge of Big O notation and complexity analysis
- Understanding of hash functions and hash tables
- Experience with file I/O and stream processing
- Basic understanding of database concepts
What You'll Learn
- Memory-efficient stream processing techniques
- Probabilistic data structures and their applications
- Query engine design and optimization
- Real-time data processing and event streaming
- Cache algorithms and performance optimization
Advanced Data Structures
Key data structures you'll implement and understand deeply
Bloom Filter
Probabilistic membership testing
Merkle Tree
Data integrity verification
LRU Cache
Efficient caching strategy
B+ Tree
Database indexing structure
Trie
Prefix tree for string operations
Skip List
Probabilistic search structure
Consistent Hash
Distributed system partitioning
Count-Min Sketch
Frequency estimation
📊 Performance Focus
These examples emphasize memory efficiency, algorithmic complexity, and real-world performance considerations. You'll learn to process large datasets efficiently, implement space-optimized data structures, and build systems that scale to handle massive amounts of data.
Ready to Process Big Data?
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