The Big Data & Apache Spark Mastery

This course is a complete, end-to-end Big Data and Apache Spark program designed for anyone who wants to build a strong career in Data Engineering.


You will gain a solid understanding of Big Data fundamentals, Hadoop ecosystem, Python with Data Structures, and Apache Spark, progressing from basics to advanced optimization techniques. The course emphasizes writing efficient, scalable Spark applications, understanding Spark internals, and applying best practices used in production environments.

Along with technical skills, this program also helps you become job-ready by guiding you on resume building and LinkedIn profile optimisation, so you can confidently present your skills to recruiters.

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Course Curriculum

Follow this structured learning path to master the fundamentals

1

Introduction to Big Data & Hadoop

Learn the fundamentals of big data and hadoop (With Hands-On Practice and Assignments)

  • Data fundamentals including data types, measurement units, and Big Data characteristics
  • • Structured, semi-structured, and unstructured data with real-world context
  • • Monolithic vs distributed systems, nodes, and scaling strategies
  • • Hadoop evolution, cluster architecture, and core ecosystem components
  • • HDFS internals covering data storage, request–response flow, and block size tuning
  • • DataNode failure handling, heartbeat mechanism, and cluster health monitoring
  • • NameNode failure, rack awareness, and fault-tolerant design principles
  • • Edge node role and hands-on Hadoop practical implementation
  • • MapReduce processing engine including map and reduce phases and internals
  • • Reducers, combiners, use-cases, and assignments with interview-focused questions and notes
2

Python and Data Structures

Understand how data structures work internally to design efficient and scalable solutions. (With Hands-On Practice and Assignments)

  • Python Foundations and Execution Model
  • Python Development Environment Setup
  • Variables Memory Model and Core Data Types
  • Output Formatting Imports and Python Keywords
  • Operators Input Handling and Type Casting
  • Decision Control and Conditional Logic
  • Iterative Constructs and Flow Control
  • List Data Structure and Operations
  • Advanced List Techniques and Comprehensions
  • String Processing and Manipulation
  • Tuple Set and Dictionary Essentials
  • Functional Programming Concepts in Python
  • Recursion and Backtracking Concepts
  • Exception Handling and Error Management
  • Decorators Generators and Iterators
  • Object Oriented Programming Principles
  • File handling in python
  • Searching Algorithms and Logic Building
  • Sorting Algorithms
  • Advanced Data Structures Using Python
  • Stack - implementation of stack
  • Queue - Dequeue and Priority queue
  • Linked List -Singly, doubly and Circular
  • 300+ programs covered
3

Apache Spark - Introduction

Learn how Apache Spark actually works under the hood and process data efficiently at scale (With Hands-On Practice and Assignments)

  • Apache Spark Overview and Architecture
  • Why Spark and Spark vs MapReduce
  • Spark Data Storage and Execution Model
  • RDD Fundamentals and Core Characteristics
  • Lazy Evaluation and Execution Planning
  • Immutability and Fault Tolerance in Spark
  • Resilient Distributed Dataset Explained
  • DAG and Lineage Mechanism
  • Pair RDD and Key Value Processing
  • Spark Context and Application Lifecycle
  • Creating Spark Context Programmatically
  • RDD Creation Techniques and Data Sources
  • RDD Partitioning Strategy and Defaults
  • Understanding and Inspecting RDD Partitions
  • Parallelize RDD vs File Based RDD Partitioning
  • Complex RDD Transformations and Processing Patterns
  • Spark UI Deep Dive and Debugging Techniques
  • Shared Variables Broadcast and Accumulators
  • Spark Program Execution on Cluster
  • Client Cluster and Local Deployment Modes
  • Driver and Executor Roles in Spark
  • Data Shuffling and Performance Impact
  • Transformations Narrow vs Wide
  • Actions and Execution Triggers
  • Jobs Stages and Tasks Creation Internals
  • Map vs MapPartitions Processing
  • ReduceByKey vs Reduce Internals
  • ReduceByKey vs GroupByKey Performance Analysis
4

Apache Spark - Structured API

A deep dive into Spark’s structured APIs to build scalable ETL pipelines, handle diverse file formats, and manage production-ready data systems

  • Apache Spark ecosystem and API layers
  • Structured APIs versus lower level APIs
  • DataFrame vs RDD vs Dataset comparison
  • Serialization and deserialization in Spark
  • SparkSession and application entry point
  • Creating SparkSession programmatically
  • Spark data types and schema fundamentals
  • DataFrame creation techniques and strategies
  • Empty DataFrame creation
  • DataFrame creation from RDDs and collections
  • Schema definition using StructType and StructField
  • Schema definition using DDL string approach
  • Nested DataFrame design
  • Nullable fields and schema enforcement
  • ETL pipeline design using Spark
  • Row based vs column based file formats
  • Internal working of common file formats
  • CSV file format internals
  • XML file format handling
  • JSON file format internals
  • Avro file format fundamentals
  • ORC file format internals
  • Parquet file format internals
  • Low level compression techniques in Spark
  • Bit packing, run length encoding, dictionary encoding and delta encoding
  • Reading JSON data using Spark DataFrames
  • Reading JSON from files directories and RDDs
  • Multiline JSON handling
  • Explicit schema definition while reading JSON
  • Flattening nested JSON structures
  • Reading CSV data using Spark
  • CSV read options and configurations
  • InferSchema drawbacks and performance impact
  • Explicit schema definition for CSV
  • Reading multiple CSV files and directories
  • Corrupt record handling strategies
  • Permissive FailFast and DropMalformed modes
  • Reading text and Excel files in Spark
  • Reading Excel by sheet and cell range
  • Reading Parquet ORC and Avro files in Spark
  • Handling multiple Parquet files with schema variations
  • to_avro and from_avro operations
  • Reading directory-based datasets
  • DataFrame Writer API and write operations
  • Write modes append overwrite errorIfExists and ignore
  • Partition level overwrite behavior
  • Schema evolution across Parquet ORC Avro JSON and CSV
  • Writing data to Excel with append and overwrite
  • Schema evolution in Excel
  • Spark SQL fundamentals
  • Spark tables and Hive table integration
  • Temporary views creation techniques
  • Local and global temporary views
  • Spark catalog and metadata management
  • EnableHiveSupport for Hive integration
  • Managed and external tables in Spark
  • Creating managed tables using SQL CTAS and SaveAsTable
  • Database creation and table management
  • Creating and managing external tables
  • Dropping external tables behavior
  • Creating tables from DataFrames
  • Compression codecs in Spark
  • LZO Snappy Gzip and Bzip2 compression
  • Comprehensive summary of Spark file formats
5

Apache Spark Transformation and SQL

Master PySpark DataFrame operations, SQL functions, joins, aggregations, and window analytics to build production-ready data transformations.

  • Column selection techniques and expressions
  • Column aliasing and expression handling
  • Null value handling and data cleanliness
  • Count variations and distinct metrics
  • Column level operators and conditional logic
  • Exploratory data analysis and dataset profiling
  • Date and time functions and transformations
  • Case when logic and derived columns
  • String operations and text processing
  • Row filtering and conditional selection
  • Sorting ordering and null handling strategies
  • Column manipulation and schema evolution
  • Type casting and literal value handling
  • Duplicate handling and data deduplication
  • Action operations and execution triggers
  • Date and timestamp conversion use cases
  • Aggregate functions and grouping strategies
  • Single and multi aggregation patterns
  • Approximate and statistical aggregations
  • Array and collection processing techniques
  • Explode and flatten operations
  • Array transformations and element access
  • Join fundamentals and join types in PySpark
  • Handling nulls and ambiguity in joins
  • Multi-column and multi-table joins
  • Join optimization scenarios and internals
  • Mathematical and utility functions
  • Schema inspection and metadata access
  • Set operations and complex DataFrame operations
  • Union unionByName intersect and minus
  • Conditional expressions and null-safe equality
  • User defined functions and function invocation
  • Window functions and analytical processing
  • Ranking distribution and frame-based analytics
6

Spark Optimisation Techniques and Internals

Master Spark performance tuning, resource optimization, and production-level troubleshooting with real-world scenarios

  • Types of Spark optimization application level and resource level
  • Spark cluster architecture and internal working
  • Optimizing Spark cluster configuration
  • Executor design fat executor vs thin executor
  • On heap vs off heap memory management
  • Selecting optimal number of executors cores and memory
  • Spark configuration and property setting methods
  • Static vs dynamic resource allocation
  • Memory distribution inside Spark executors
  • Java heap memory vs external memory
  • Total container memory calculation
  • Initial partition calculation strategy
  • Cluster resource analysis and OOM detection
  • Executor core and memory calculation for real workloads
  • Standardized formulas for core and memory planning
  • Scenario based Spark performance interview cases
  • Spark code level optimizations
  • Shuffle partition tuning strategies
  • Spark file layout and data organization
  • Repartition vs coalesce usage
  • Partition skew detection and handling
  • When to increase or decrease partitions
  • partitionBy vs bucketBy tradeoffs
  • Cache vs persist and Spark storage levels
  • Join strategies and join optimization in Spark
  • Fine tuning critical Spark configurations
  • Adaptive Query Execution AQE fundamentals
  • Spark execution plan and explain plan analysis
  • Fact and dimension modeling concepts
  • Slowly Changing Dimension SCD strategies
  • Monitoring and debugging Spark applications
  • Spark jobs not starting troubleshooting
  • Slow tasks and spark.task.cpus tuning
  • Optimizing slow aggregations
  • Optimizing slow joins
  • Optimizing slow read and write operations
  • Driver OOM error analysis and fixes
  • Executor OOM error handling
  • No space left on disk error resolution
  • Serialization error diagnosis
  • Data spill detection and mitigation
7

End-to-End Big Data Project

Learn the concepts with real-word resume showcasing project

  • Data ingestion
  • Transformation
  • Modeling
  • Insights
  • Visualization
8

Resume building and LinkedIn profile optimisation

A practical guide to optimize your LinkedIn presence and resumes to maximize shortlisting across all experience levels.

  • LinkedIn profile optimization guide
  • Sample resume template for freshers 0–2 years experience
  • Sample resume template for professionals with 3–5 years experience
  • Sample resume template for professionals with 6–8 years experience
  • Sample resume template for senior professionals with 9+ years experience

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