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Apache Spark Tutorial with Java Examples
This Apache Spark Tutorial with Java Examples course is designed to help you master big data processing using one of the most powerful distributed computing frameworks. Whether you are a beginner, working professional, or aspiring data engineer, this course provides a structured, hands-on approach to learning Spark with Java.
You will begin with Spark fundamentals, architecture, and setup, then progress to core concepts such as RDDs using JavaSparkContext, and DataFrames and Datasets using SparkSession. The course also covers Spark SQL, file processing (CSV, JSON, Parquet), and real-world data transformation techniques using Java APIs.
By the end of this course, you will confidently build scalable Spark applications, optimize performance, and solve real-world big data problems.
What You'll Learn
- Understand the fundamentals of Apache Spark and its role in big data processing
- Learn Spark architecture, including driver, executors, and cluster managers
- Set up Spark with Java and configure development environments (Maven)
- Create and configure applications using SparkSession and Java APIs
- Work with RDDs using JavaSparkContext for distributed data processing
- Understand transformations and actions in Spark RDDs
- Learn how Spark processes data in-memory for high performance
- Build and manipulate DataFrames using SparkSession
- Work with structured data using Datasets and Spark SQL
- Perform data transformations such as filtering, grouping, and aggregation
- Read and write data in multiple formats (CSV, JSON, Parquet)
- Execute SQL queries on large datasets using Spark SQL
- Understand lazy evaluation and execution planning in Spark
- Use caching and persistence to optimize Spark jobs
- Learn partitioning and parallel processing techniques
- Handle real-world data processing scenarios using Java
- Submit and run Spark applications using spark-submit
- Understand integration with Hadoop ecosystem (HDFS, YARN)
- Explore Spark’s core components like Spark SQL
- Build scalable, production-ready data pipelines using Java
Introduction to Apache Spark
- What is Apache Spark?
- Apache Spark Features
- Apache Spark Architecture
- Apache Spark Ecosystem
- Use Cases and Application of Spark
- Apache Spark vs MapReduce
- Quiz - Introduction
Setup Apache Spark Environment
- Install Spark on Mac
- Install Spark on Windows
- Run Spark from IntelliJ
- Run Spark First Program
SparkSession & SparkContext
- What is SparkSession
- Creating SparkSession
- SparkSession Methods
- What is JavaSparkContext?
- What does SparkContext do?
- JavaSparkContext Methods
- Quiz - Spark Session & Context
Resilient Distributed Datasets (RDDs)
- RDD Introduction
- RDD Advantages & Limitations
- Creating RDD from Parallelize
- Read Text Files into RDD
- How to Parallelize RDD
- Collect Data from RDD
- RDD Transformations
- RDD Actions
- RDD - Word Count Example
- Working with Key-Value RDDs
- Fault Tolerance in RDDs
- RDD Performance
- Quiz - Java Spark RDD
Cache, Persistence and Shared Variables
- Cache and Persistence
- Spark Persistence Levels
- Broadcast Shared Variable
- Accumulator Shared Variable
- Quiz - RDD Cache, Persistence & Shared Variables
DataFrame Introduction
- What is a DataFrame
- How DataFrame differ from RDD
- Create DataFrame
- DataFrame Transformations
- DataFrame Actions
- Spark Schema
- Quiz - DataFrame Introduction
DataFrame Narrow Transformation
- Select DataFrame Columns
- Filter DataFrame Rows
- Rename DataFrame Column
- Add & Update Columns of DataFrame
- Drop Column
- Spark map() and mapPartitions()
- Spark flatMap()
- Spark Union and UnionAll
- Quiz - DataFrame Narrow Transformations
DataFrame Wider Transformation
- Spark SQL Join Types
- Groupby and Aggregations
- Remove Duplicate Rows
- Pivot and Unpivot DataFrame
- Quiz - Wider Transformations
DataFrame Actions
- Spark Collect Data from DataFrame
- show, count, take, first, head
- Spark foreach and foreachPartition
- DataFrame reduce() Action
- Quiz - DataFrame Actions
Spark SQL Functions
- String Functions
- Date Functions
- Timestamp Functions
- Aggregate Functions
- Array Functions
- Map Functions
- Sort Functions
- Window Functions
- JSON Functions
- Quiz - SQL Functions
Advanced DataFrame Features
- Handling Missing Data in DataFrames
- Explain StructType & StructField
- UDF (user Defined Functions)
- DataFrame Cache and Persist
- Creating Views & Tables
- Quiz - Advanced DataFrame Techniques
Using SQL Queries
- Introduction to Spark SQL
- Creating and Managing Views
- Executing SQL Queries in Spark
- Aggregation, and Grouping
- Working with Joins in Spark SQL
- Using Built-in Functions in Spark SQL
- Combining SQL with DataFrame API
- Quiz - Using SQL Queries in Spark
Spark Read & Write CSV Files
- Spark Reading CSV Files
- Using Options in Reading CSV
- Using Custom Schema
- Handing Quotes and Special Characters
- Handling Date and Timestamp
- Write CSV File
- Quiz - Read & Write CSV Files
Working with Parquet Files
- Introduction to Parquet in Spark
- Write DataFrame to Parquet FIle
- Reading Parquet Files in Spark
- Schema Management and Evolution in Parquet
- Performance Optimization with Parquet
- Quiz - Spark with Parquet Files
Spark Dataset
- Introduction to Dataset
- Creating Datasets
- Encoders and Type Safety
- Dataset Transformations
- Actions on Datasets
- Working with Typed Aggregations
- Quiz - Spark Dataset