Big Data Architect Masters Program Course Syllabus

Overview: This master’s program is designed to provide a comprehensive, hands-on learning experience in big data architecture, covering core technologies and cloud platforms used in modern data ecosystems. With a balanced mix of theory and practical labs, the course spans approximately 12 weeks of intensive learning, including hands-on projects and a capstone. Learners will gain expertise in distributed systems, real-time processing, data warehousing, and NoSQL databases, culminating in a portfolio-ready project that demonstrates end-to-end architectural design skills. Lifetime access ensures flexibility for continuous learning.

Module 1: Big Data Hadoop Certification Training

Estimated time: 21 hours

• HDFS architecture and data storage principles
• MapReduce programming model and execution
• YARN resource management and scheduling
• Hive for SQL-based querying and data warehousing
• Pig for data flow scripting and HBase for NoSQL storage

Module 2: Apache Spark and Scala Certification Training

Estimated time: 14 hours

• Resilient Distributed Datasets (RDDs) and transformations
• DataFrames and Spark SQL for structured data processing
• MLlib for scalable machine learning
• Spark Streaming for real-time data pipelines

Module 3: Apache Kafka Certification Training

Estimated time: 7 hours

• Kafka architecture: brokers, topics, partitions
• Producers and consumers for event streaming
• Partitioning strategies and message durability

Module 4: Talend for Data Integration

Estimated time: 14 hours

• ETL fundamentals and data integration patterns
• Talend components and job design workflow
• Building batch data processing pipelines

Module 5: NoSQL Databases with Cassandra and MongoDB

Estimated time: 14 hours

• Apache Cassandra: data modeling, replication, consistency
• Querying large-scale data with CQL
• MongoDB: CRUD operations, indexing, aggregation
• Replication and sharding in MongoDB

Module 6: Cloud Data Platforms and Warehousing

Estimated time: 14 hours

• AWS Redshift architecture and data loading
• Amazon Redshift Spectrum for querying external data
• Azure Data Factory: data pipelines and activities
• Linked services and triggers for automation

Module 7: Capstone Project

Estimated time: 14 hours

• Design an end-to-end big data architecture
• Implement batch and real-time processing using Hadoop and Spark
• Integrate NoSQL databases and cloud services

Prerequisites

• Basic understanding of data engineering concepts
• Familiarity with Python or Java programming
• Exposure to distributed systems recommended

What You'll Be Able to Do After

• Design scalable big data architectures for enterprise use
• Build and manage batch and streaming data pipelines
• Integrate diverse data sources using ETL tools like Talend
• Deploy and manage NoSQL databases at scale
• Architect cloud-based data solutions on AWS and Azure