Big Data & AI: The Complete Crash Course (Hadoop, Spark, & Deep Learning)

Understand the entire ecosystem of Big Data and Artificial Intelligence in under 10 minutes. This video explains the fundamental concepts of data engineering, storage architectures, and how machine learning models are trained.

Perfect for computer science students, software engineers, and anyone preparing for system design interviews.

📚 What You Will Learn:

The Scale of Data: Understanding Zettabytes and the 5 Vs of Big Data (Volume, Velocity, Variety, Veracity, Value).

Storage & Architecture: How Hadoop and HDFS store massive datasets using Master-Slave architecture.

Processing Power: The difference between MapReduce (Disk-based) and Apache Spark (In-memory).

Database Types: SQL vs. NoSQL (Key-Value, Document, Columnar, Graph).

System Design Theory: The CAP Theorem (Consistency vs. Availability).

The AI Layer: The relationship between AI, Machine Learning (Supervised/Unsupervised), and Deep Learning.

⏱️ Timestamps: 0:00 - Introduction to Big Data 0:54 - The 5 Vs Explained (Volume, Velocity, Variety...) 2:41 - HDFS & Hadoop Architecture 3:46 - MapReduce vs. Apache Spark 4:59 - NoSQL Databases Explained 5:29 - The CAP Theorem 6:28 - AI vs. Machine Learning vs. Deep Learning 7:27 - 3 Types of Machine Learning

Keywords: Big Data Engineering, System Design Interview, Data Science Basics, Computer Science Concepts.


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