Scale Beyond Traditional Limits
When datasets grow beyond what single machines can handle, big data technologies become essential. Learn the distributed computing principles that power Netflix, Uber, and LinkedIn's real-time analytics at massive scale.
Distributed Computing
Master the fundamentals of parallel processing, distributed storage, and fault-tolerant systems that handle petabytes of data across thousands of machines.
Real-Time Analytics
Build streaming data pipelines that process millions of events per second, enabling instant insights for fraud detection, recommendations, and monitoring.
Cloud Platforms
Deploy and manage big data solutions on AWS, Azure, and Google Cloud, leveraging managed services for scalable, cost-effective data processing.
Master the Big Data Ecosystem
Hadoop Ecosystem
- • HDFS Storage
- • MapReduce
- • YARN Resource Manager
- • Hive Data Warehouse
- • HBase NoSQL
Apache Spark
- • Spark Core RDDs
- • Spark SQL
- • Spark Streaming
- • MLlib Machine Learning
- • GraphX Analytics
Stream Processing
- • Apache Kafka
- • Apache Storm
- • Apache Flink
- • Amazon Kinesis
- • Event Hubs
Cloud Services
- • AWS EMR
- • Google BigQuery
- • Azure Synapse
- • Databricks
- • Snowflake
16-Week Advanced Curriculum
Phase 1: Big Data Foundations (Weeks 1-4)
Build understanding of distributed systems and big data principles
Distributed Systems Theory
- • CAP Theorem and Trade-offs
- • Consistency Models
- • Partitioning Strategies
- • Fault Tolerance Patterns
Hadoop Fundamentals
- • HDFS Architecture
- • MapReduce Programming
- • YARN Resource Management
- • Cluster Setup and Administration
📊 Project: Log Analysis System
Build a distributed system to analyze web server logs from multiple sources using Hadoop MapReduce.
Phase 2: Apache Spark Mastery (Weeks 5-8)
Master unified analytics engine for large-scale data processing
Spark Core & SQL
- • RDD Operations and Transformations
- • DataFrame and Dataset APIs
- • Spark SQL Optimization
- • Catalyst Query Engine
Advanced Spark Features
- • Spark Streaming Architecture
- • MLlib for Machine Learning
- • GraphX for Graph Analytics
- • Performance Tuning
🔥 Project: Real-time Fraud Detection
Implement a streaming fraud detection system processing credit card transactions in real-time using Spark Streaming.
Phase 3: Streaming & NoSQL (Weeks 9-12)
Master real-time data processing and NoSQL databases
Stream Processing
- • Apache Kafka Architecture
- • Kafka Connect and Streams
- • Apache Flink Programming
- • Event-Driven Architectures
NoSQL Databases
- • MongoDB Document Store
- • Cassandra Wide-Column
- • Redis In-Memory Cache
- • Elasticsearch Full-Text Search
📱 Project: IoT Analytics Platform
Build a complete IoT data pipeline processing sensor data from thousands of devices in real-time.
Phase 4: Cloud & Production (Weeks 13-16)
Deploy and manage big data solutions in production environments
Cloud Platforms
- • AWS EMR and Redshift
- • Google Cloud Dataflow
- • Azure HDInsight
- • Databricks Unified Platform
DevOps & Monitoring
- • Infrastructure as Code
- • Container Orchestration
- • Monitoring and Alerting
- • Data Pipeline Orchestration
🚀 Capstone: Enterprise Data Lake
Design and implement a complete enterprise data lake architecture handling multiple data sources with automated ETL pipelines.
Big Data Career Transformations
Career Outcomes
Master Enterprise-Scale Big Data
Join the elite group of big data professionals capable of architecting systems that process petabytes of data. This advanced program is designed for serious career advancement.
Next cohort starts July 1st, 2025
Limited to 25 students for personalized attention
Enterprise Big Data Solutions and Advanced Analytics
The exponential growth of data generation across industries has created unprecedented challenges and opportunities for organizations seeking competitive advantages through analytics. Our comprehensive big data program addresses the critical shortage of professionals capable of architecting and managing enterprise-scale data processing systems.
Modern big data solutions require sophisticated understanding of distributed computing principles, fault-tolerant architectures, and scalable processing frameworks. The curriculum encompasses both theoretical foundations and practical implementation experience, ensuring graduates can navigate the complexities of real-world big data environments.
Cloud platform integration has become essential for cost-effective big data operations, requiring professionals who understand both on-premises and cloud-native architectures. Our hands-on approach with leading cloud platforms prepares students for the hybrid environments common in enterprise settings.
Career advancement opportunities in big data engineering and architecture continue expanding as organizations recognize the strategic value of data-driven decision making at scale. The specialized skills developed in this program position graduates for leadership roles in the rapidly evolving data technology landscape.