Chapter 1

Introduction to Big Data Ecosystem

Hadoop framework overview and history

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Hadoop Distributed File System (HDFS) architecture

medium • 2 min read

MapReduce programming model

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YARN (Yet Another Resource Negotiator)

medium • 4 min read

Hadoop Ecosystem tools (Sqoop, Flume, Hive, Pig)

medium • 5 min read

Chapter 2

Apache Spark Framework

Spark architecture and Resilient Distributed Datasets (RDDs)

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Transformations (map, filter) vs Actions (reduce, collect)

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Spark DataFrames and Spark SQL

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Catalyst Optimizer and Tungsten execution engine

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Performance tuning in Spark (Caching, Partitioning)

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Chapter 3

Machine Learning with Spark (MLlib)

Overview of Spark MLlib library

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Building ML Pipelines in Spark

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Feature extraction, transformation, and selection in Spark

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Distributed classification and regression with Spark ML

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Clustering large datasets using Spark K-Means

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Chapter 4

Real-Time Streaming Analytics

Batch processing vs Stream processing

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Apache Kafka architecture (Producers, Consumers, Brokers, Topics)

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Spark Structured Streaming basics

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Window operations in streaming data

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Applications of real-time analytics (Fraud detection, IoT)

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Chapter 5

Cloud Analytics and Big Data

Big Data Services on AWS (EMR, Redshift, Athena)

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Big Data Services on Azure (Synapse, Databricks) and GCP (BigQuery)

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Serverless architectures for data analytics

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Designing scalable data lakes

medium • 4 min read

Data governance and security in cloud big data

medium • 5 min read