Unit 1: Big Data Foundations

• Overview of Big Data and Emerging Challenges
• The Hadoop Ecosystem: HDFS, YARN, and MapReduce
• Setting Up a Hadoop Cluster

Unit 2: Core Hadoop Components

• HDFS: Distributed Storage and File Management
• MapReduce: Parallel Processing for Large-Scale Data
• Hands-on Project: Processing Log Files with HDFS and MapReduce

Unit 3: Apache Spark for Big Data Processing

• Introduction to Apache Spark: RDDs, DataFrames, and Datasets
• Advanced Data Manipulation with Spark SQL
• Hands-on Project: Aggregating and Filtering the MovieLens Dataset

Unit 4: Advanced Spark Techniques

• Spark Streaming: Real-Time Data Processing with Kafka
• Machine Learning with Spark MLlib: Building a Recommender System
• Hands-on Project: Streaming and Predicting User Behavior

Unit 5: Data Visualization with Tableau

• Connecting Big Data Sources (HDFS, Spark SQL) to Tableau
• Building Interactive Dashboards and Reports
• Automating Data Publishing with the Tableau Hyper API
• Hands-on Project: Visualizing Top Movie Trends and Insights

Unit 6: Integrating Relational and Non-Relational Databases

• Using Hive and SQL to Query Big Data
• NoSQL with HBase: Managing Semi-Structured Data
• Hands-on Project: Migrating Data from MySQL to HDFS

Unit 7: Cloud-Based Big Data Solutions

• Introduction to Cloud Platforms: AWS, Google Cloud, Azure
• Implementing Big Data Solutions in the Cloud
• Hands-on Project: Deploying a Scalable Data Pipeline in a Cloud Environment

Unit 8: Data Ethics and Governance

• Principles of Data Governance
• Ethical Considerations in Big Data
• Compliance with Data Protection Regulations (e.g., GDPR)
• Case Studies on Ethical Data Use

Unit 9: Emerging Technologies in Big Data

• Integration of AI and Big Data
• Edge Computing and Its Applications
• Big Data in the Internet of Things (IoT)
• Hands-on Project: Analyzing IoT Data Streams

Unit 10: Final Capstone Project

Participants will design and implement a comprehensive big data pipeline:

1. Ingest Data using Kafka or Flume.
2. Process Data with Spark for real-time and batch analysis.
3. Implement Machine Learning models using Spark MLlib.
4. Deploy the solution on a cloud platform.
5. Visualize Data in Tableau dashboards.
6. Present findings, discuss scalability, performance, and business impact.

• Data analysts and aspiring data scientists looking to learn how to process Big Data using Apache Spark.
• Software engineers and programmers aiming to understand the broader Big Data ecosystem and use it for storing and analyzing massive datasets.
• Project, program, or product managers seeking a high-level understanding of Big Data architecture and components.

• Experience with Python programming, and machine learning, or successful completion of our Artificial Intelligence Professional Program.

• Build end-to-end big data solutions for ingesting, storing, processing, and visualizing large datasets.
• Process massive datasets using tools like Hadoop, Apache Spark, and Spark SQL.
• Apply machine learning techniques to create recommender systems and predictive models using Spark MLlib.
• Gain practical experience in real-time data processing with Spark Streaming and Kafka.
• Deploy big data solutions in cloud environments such as AWS, Google Cloud, or Azure.
• Create interactive dashboards to present data insights effectively using Tableau.

By completing this course, participants will gain hands-on experience and foundational knowledge to thrive in today’s data-driven industries.