Streaming Algorithms: Handle Big Data with Limited Memory!

Описание: Dive into the world of Streaming Algorithms! Learn how to process massive, continuous data streams with limited memory. This beginner-friendly video explains the core concepts and common algorithms used to tackle data that just keeps flowing! 🌊

We'll explore what makes data streams unique, focusing on their single-pass nature and the need for real-time processing. Understand the critical memory constraints that drive the design of these clever algorithms. 🧠 Discover how memory usage stays manageable even as data explodes!

We cover key streaming problems like frequency estimation, finding heavy hitters, counting distinct elements, and quantiles estimation. Then we'll demystify popular algorithms like Count-Min Sketch, HyperLogLog, and Reservoir Sampling with easy-to-understand code examples. 💻

See how these space-efficient methods balance memory usage with accuracy, unlocking countless real-world applications in network monitoring, social media analytics, database systems, and the ever-growing world of IoT! 🚀

#StreamingAlgorithms #BigData #DataScience #Algorithms #DataStructures #ComputerScience #Programming #TechTutorial #codelucky
Chapters:
00:00 - Streaming Algorithms
00:14 - What is a Data Stream?
00:32 - Streaming Model Characteristics
00:53 - Memory Constraints
01:16 - Common Stream Problems
01:38 - Count-Min Sketch
01:58 - HyperLogLog Algorithm
02:25 - Reservoir Sampling
02:49 - Space-Time Trade-offs
03:11 - Applications & Summary
03:36 - Outro

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