Big Data Analytics

16 lectures

Lecture notes and study resources for this subject.

01Lecture 01 · Big Data Analytics — Course Introduction and Core Foundations

02Lecture 02 · Distributed Systems Architecture, Scaling Models, CAP & PACELC Theorems, Amdahl's and Gustafson's Laws, and NoSQL Paradigms

03Lecture 03 · Big Data Technology Landscape, Hadoop Architecture & HDFS Deep Dive

04Lecture 04 · MapReduce Architecture, HDFS Operations, Execution Lifecycle, and Optimization Techniques

05Lecture 05 · Distributed Resource Management, SQL-to-Hadoop Ingestion, and Streaming Event Collection

06Lecture 06 · Pig, Hive, and Data Transformation Frameworks

07Lecture 07 · Apache Hive Architecture, Metastore Management, Data Types, and Optimization Strategies

08Lecture 08 · Apache Hive Bucketing Resolution, Relational Joins, Subquery Pipelines, Apache ZooKeeper, and Apache HBase

09Lecture 09 · MongoDB Architecture, Document Modeling, CRUD Operations, Distributed Consistency, and Multi-Node Configuration

10Lecture 10 · Apache Spark DataFrames, Catalyst Optimizer, PySpark Operations, and Spark SQL

11Lecture 11 · Structured Streaming and Spark Streaming

12Lecture 12 · Spark Streaming Output Modes, Watermarking, and Machine Learning Fundamentals

13Lecture 13 · Machine Learning Landscape & Unsupervised Clustering

14Lecture 14 · Supervised Machine Learning: Naive Bayes, Decision Trees, and Random Forest

15Lecture 15 · Spark MLlib Architecture, Feature Engineering Pipelines, and Distributed Model Training

16Lecture 16 · Spark MLlib: A Framework for Machine Learning at Scale

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