Big Data Fundamentals

Master the tools and techniques for handling massive datasets

When datasets grow beyond what traditional databases can handle, Big Data technologies step in. Learn to process, analyze, and extract insights from petabytes of information using distributed systems that power the world's largest applications.

What You'll Master

🌐
Distributed Systems

Process data across clusters of machines

🔥
Hadoop & Spark

Industry-standard frameworks for big data processing

📊
Data Lakes & Warehouses

Store and organize massive amounts of structured and unstructured data

⚡
Stream Processing

Analyze data in real-time as it arrives

🚀
Production-Scale Projects

Build data pipelines that handle billions of events per day

Why Big Data Skills Matter

Every day, the world generates quintillions of bytes of data from social media, IoT devices, financial transactions, and more. Companies that can harness this data gain competitive advantages through better decision-making, personalized experiences, and operational efficiency. Big Data engineers are among the most sought-after professionals in tech, commanding premium salaries to build the infrastructure that turns raw data into business intelligence.

Start Learning: Introduction to Big Data
Jump into Lesson 1 and begin your big data journey!

Course Index

  1. Introduction to Big Data
    What makes data "big" and why it matters
  2. Distributed Computing Basics
    How to process data across multiple machines
  3. Hadoop Ecosystem
    HDFS, MapReduce, and the Hadoop framework
  4. Apache Spark Fundamentals
    Fast, in-memory data processing at scale
  5. Data Storage Architectures
    Data lakes, warehouses, and lakehouses
  6. Data Processing Pipelines
    ETL, ELT, and batch processing workflows
  7. Stream Processing
    Real-time data with Kafka and Spark Streaming
  8. NoSQL for Big Data
    Cassandra, MongoDB, and HBase at scale
  9. Data Analytics at Scale
    Query engines and analytical processing
  10. Machine Learning on Big Data
    Train models on massive datasets with MLlib
  11. Cloud Big Data Services
    AWS, Azure, and GCP solutions for big data
  12. File Formats
    Big data file formats for massive datasets
  13. AWS Batch & AWS Glue
    Managed services for data processing on AWS
  14. AWS Lake Formation
    Creating, securing, and managing data lakes on Amazon S3
  15. Cloud Data Warehouses
    Snowflake, Redshift, and BigQuery comparison and features
  16. Data Orchestration & Workflow Management
    Apache Airflow, Prefect, Dagster, and cloud orchestration
  17. Apache Flink - Advanced Stream Processing
    Event time processing, stateful computations, exactly-once semantics
  18. Data Quality & Testing
    Great Expectations, AWS Deequ, schema validation, anomaly detection
  19. Lakehouse Architecture
    Delta Lake, Apache Iceberg, Apache Hudi comparison and features
  20. Distributed SQL Query Engines
    Presto/Trino, Impala, AWS Athena, query federation
  21. Change Data Capture (CDC)
    Debezium, AWS DMS, event sourcing, real-time synchronization
  22. Data Governance & Compliance
    Apache Atlas, AWS Glue Data Catalog, GDPR/CCPA, master data management
  23. Real-Time Analytics Engines
    Apache Druid, Pinot, ClickHouse for sub-second analytics on streaming data
  24. Apache Airflow Deep Dive
    DAG patterns, dynamic generation, XComs, sensors, executor strategies
  25. Spark Performance Tuning Deep Dive
    Catalyst, Tungsten, AQE, memory management, shuffle optimization
  26. Capstone: Fraud Detection Pipeline
    Real-time fraud detection with PySpark Streaming, Kafka, watermarks, and Delta Lake

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