Advanced Streaming Big Data with Spark - eLearning

Advanced Streaming Big Data with Spark - eLearning

Step into real-time data processing with the Streaming Big Data with Spark Training, designed to help you build high-performance, scalable data pipelines that process information as it happens. This course introduces you to Apache Spark’s streaming capabilities, enabling you to work with continuous data flows for modern analytics and decision-making systems.

You’ll learn how to process large-scale streaming data using Spark Streaming and Structured Streaming, integrate real-time data sources, and build fault-tolerant, scalable pipelines. The course also covers key big data concepts and practical use cases across industries such as finance, IoT, and e-commerce.

By the end of the training, you’ll be able to design and implement real-time data processing systems that power next-generation analytics applications.

Key Features

• Course and material in English
• Intermediate - Advanced level
• 9 Hours of On-Demand Videos
• 38 Guided Hands-on Exercises
• 13 Auto-Graded Assessments
• 33 Recall Quizzes
• 3 Real-world projects
• 25+ hours recommended study time
• 1 Year access to the learning platform
• Program completion certification included

Learning Outcomes

• Gain a complete understanding of Spark runtime architecture
• Perform essential DataFrame operations and functions in Spark
• Learn the fundamentals of stream processing with Spark
• Explore direct integration of Spark Streaming with Apache Kafka
• Work with Spark Streaming using Amazon Kinesis
• Understand and apply sliding window operations in stream processing

Target Audience

• Data engineers working with real-time data systems
• Big data professionals and Spark developers
• Software engineers transitioning into data engineering roles
• Data scientists interested in streaming analytics
• Backend developers building data-intensive applications
• IT professionals working with large-scale distributed systems
• Anyone interested in real-time data processing and Spark

Prerequisites

• Basic understanding of programming (Java, Scala, or Python preferred)
• Familiarity with big data concepts and distributed systems
• Basic knowledge of data processing or analytics workflows
• Understanding of databases and SQL (helpful but not mandatory)
• No prior Spark Streaming experience is required.

Course Content

The Spark Runtime

• Understanding the Spark RDD  
• Understanding the Spark DataFrame  
• Spark Runtime Architecture Overview 

ETL with Spark

• Map Transformations  
• The Transformations  
• Basic Actions  
• key-value pair Transformations  
• Join Operations 
• Numeric RDD Operations and Sampling Functions 
• Partitioning in Spark 
• Controlling Partitions in Spark  
• Using External Programs with Spark 

SparkSQL and DataFrames 

• Spark SQL Architecture 
• DataFrame API Overview 
• Creating DataFrames 
• DataFrame Data Model and Schemas 
• Basic DataFrame Operations 
• DataFrame Functions 
• Set Operations and Aggregations in DataFrames 
• DataFrame Storage and Output 
• DEMO Spark SQL and DataFrames

Introduction to Stream Processing with Spark

• Introduction to Spark Streaming 
• Introduction to DStreams
•  The DStream Operations 

Stateful processing with Spark Streaming

• The State Operations    
• Introduction to Event Sourcing  
• Demonstration of Stateful Streaming with Spark

Sliding Window Operations with Spark Streaming 

• Windowing Operations   
• Windowing Functions 
• DEMO Sliding Window Operations with Spark Streaming

Introduction to Structured Streaming   

• Structured Streaming Overview  
• Output Modes and Triggering with Structured Streaming 
• DEMO Introduction to Structured Streaming

Introduction to Apache Kafka 

• Apache Kafka Overview and Architecture   
• Messaging with Kafka   
• Demo: Local Installation of Apache Kafka 

Kafka Integration with Spark Streaming    

• Using Spark Streaming with Apache Kafka  

Using the Receiver Approach

• Demo: Local Installation of Apache Kafka
• Using the Direct Approach
• DEMO Spark Streaming with Apache Kafka using the Direct Approach

Kafka Integration with Structured Streaming 

• Structured Streaming and Kafka   
• Reading and Writing Data to Kafka using Structured Streaming  
• DEMO Kafka and Structured Streaming

Using Spark Streaming with Kinesis 

• Using the Amazon Kinesis Producer and Client Libraries 
• DEMO Intro to Amazon Kinesis

Using Spark Streaming with Kinesis 

• Using Spark Streaming with Amazon Kinesis   
• DEMO Using Spark Streaming with Amanzon Kinesis
• Using Structured Streaming with Amazon Kinesis 
• DEMO Using Structured Streaming with Amazon Kinesis

Additional Spark Streaming Integrations 

• Spark Streaming using MQTT  
• Spark Streaming and Apache Flume 
• Spark Streaming and Twitter  
• Spark Streaming and Snowflake  
• DEMO Structured Streaming with Snowflake

FAQ

Will there be any learning material beyond self-paced videos?

Absolutely! The on-demand learning experience goes beyond videos to provide a fully immersive learning environment, including:

• LEARN: Interactive recall quizzes, and real-world case studies to reinforce concepts
• ASSESS: Diagnostic, module-level, and final assessments to track your progress
• PRACTICE: Hands-on exercises with real-world simulations and Cloud Labs
• GAIN INSIGHTS: Real-time analytics and reports highlighting your learning progress, challenges, and suggested areas to revisit for mastering key skills

Can I pursue this course alongside my full-time job?

Yes! This course is designed for maximum flexibility. Delivered in a self-paced online format, it allows you to learn and upskill at your own convenience, making it easy to balance with your full-time job.

Is this course suitable for beginners?

It is best suited for learners with basic programming and some exposure to big data concepts.

Do I need Hadoop knowledge?

No, but familiarity with big data ecosystems can be helpful.

What is Spark Streaming used for?

It is used for processing real-time data streams such as logs, financial transactions, IoT data, and social media feeds.

Will I learn hands-on implementation?

Yes, the course focuses on practical Spark Streaming and real-time pipeline development.

What is the difference between batch and streaming processing?

Batch processes data in chunks, while streaming processes data continuously in real time.

Is Spark Streaming still relevant?

Yes, it is widely used in production systems for real-time analytics and is a core skill in data engineering.