What You'll Learn

  • Spark architecture and lazy evaluation paradigm
  • RDDs, DataFrames, and Datasets API
  • Spark SQL and distributed SQL queries
  • MapReduce operations: reduce, groupByKey, join
  • Spark MLlib for machine learning at scale
  • Spark Streaming for real-time data processing
  • Performance tuning and partitioning strategies
  • Deployment on Hadoop, YARN, and cloud clusters

Course Modules

1

Spark Basics & Architecture

Installation, RDD fundamentals, Spark context, and cluster architecture overview.

2

DataFrames & Spark SQL

Working with structured data, SQL queries on Datasets, and optimisations via Catalyst.

3

Transformations & Actions

Map, filter, reduce operations, aggregations, and lazy evaluation principles.

4

Advanced Data Processing

Joins, window functions, and complex aggregations across large datasets.

5

Machine Learning & Streaming

MLlib for classification, clustering, and real-time data with Spark Streaming.

5

Performance & Production

Partitioning strategies, caching, cluster deployment, and production optimisations.

Tools & Technologies

Apache Spark
PySpark
Hadoop
AWS/GCP/Azure
Databricks

Career Relevance

Data Engineer

Essential for building scalable distributed data pipelines and processing big data at scale.

Data Scientist

Recommended for large-scale machine learning projects and distributed computing.

Data Analyst

Optional but valuable for understanding advanced big data analytics.

Prerequisites

  • Advanced Python proficiency (functions, OOP, libraries)
  • Strong SQL and database knowledge
  • Understanding of distributed systems and big data concepts
  • Experience with command-line and Linux environments

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