Components of Apache Spark

Apache Spark isn’t just one tool—it’s a modular platform made up of powerful libraries, all running on a single execution engine.

This modular design allows Spark to handle diverse workloads like batch processing, real-time streaming, machine learning, and graph processing.

Let’s explore Spark’s major components:

Spark SQL – Structured Data Processing – Use SQL queries, DataFrames , or Datasets to work with structured data.

Key Features:

Multi-Source Support → Works with JSON, CSV, Parquet, ORC, JDBC, Hive, Delta Lake, and more.

Query Optimization → Built-in Catalyst Optimizer and Tungsten Engine for fast execution.

BI Tool Integration → Compatible with Tableau, Power BI, Apache Superset, etc.

Use Case:Running ad-hoc queries on large datasets stored in Azure Data Lake or Hive, without a dedicated SQL engine.

Spark MLlib – Machine Learning at Scale – A scalable ML library built into Spark for end-to-end machine learning workflows.

Key Features:

Algorithms → Classification, Regression, Clustering, Recommendation

Pipelines → Seamlessly connect preprocessing, model training, and evaluation

Interoperability → Works with tools like XGBoost , TensorFlow, H2O

Use Case:Predicting customer churn or detecting fraud in real-time financial transactions using behavioral data.

Structured Streaming – Real-Time Data Processing – Stream processing built on Spark SQL that feels like batch processing—but handles live data.

Key Features:

Fault-Tolerant → Supports micro-batching and continuous processing

Unified API → Same codebase for both streaming and batch

Flexible Sources → Kafka, Kinesis, S3, HDFS, Delta Lake, MySQL, and more

Use Case:Monitoring server logs, clickstream behavior , or IoT sensor streams for real-time alerts and insights.

GraphX – Graph Analytics & Computation – Analyze relationships between entities—great for social networks, logistics, or maps.

Key Features:

Build flexible graph data structures

Includes algorithms like PageRank, Connected Components, Triangle Counting

Optimized for distributed graph processing

Use Case:Detecting communities in a social network, or calculating optimal paths in a transportation system.

Component	Purpose	What It Does
Spark Core	Foundation engine	Handles distributed task scheduling, memory management, and fault recovery for all workloads
Spark SQL	Structured data processing	Query relational data using SQL or DataFrames ; optimizes queries automatically
Spark MLlib	Machine learning library	Provides scalable algorithms for classification, regression, clustering, and recommendations
Structured Streaming	Real-time processing	Treats live data streams as unbounded tables; enables SQL on streaming data
GraphX	Graph processing	Analyzes relationships and networks; includes PageRank and community detection algorithms

💡 Did You Know?

• Structured Streaming uses Spark SQL’s engine behind the scenes, so you can stream with SQL!

• GraphX can scale to billions of edges, making it ideal for analyzing large-scale networks like Twitter or LinkedIn.

• MLlib moved from RDD-based APIs ( spark.mllib ) to DataFrame -based APIs (spark.ml) for better performance and usability.

📚 Study Notes

Key Features of Spark

• Spark SQL enables SQL querying on distributed data with BI tool integration.

• MLlib simplifies ML workflows using built-in pipelines and scalable algorithms.

• Structured Streaming treats real-time data as an unbounded table for seamless processing.

• GraphX enables parallel graph computation on massive datasets using standard Spark APIs.