IBM Data Architecture Professional Certificate Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Introduction to Data Architecture
Estimated time: 6 hours
- Role of a data architect
- Core architectural principles
- Career pathways in data architecture
- Analyze real-world use cases for data architecture roles
Module 2: Relational Databases and Data Modeling
Estimated time: 12 hours
- Entity-Relationship (ER) modeling
- Normalization and schema design
- Relational database concepts
- Design schemas using data modeling tools
Module 3: Working with SQL and Db2
Estimated time: 12 hours
- Writing SQL queries
- Using joins and aggregations
- Database functions in SQL
- Query relational databases using IBM Db2
Module 4: Data Warehousing and Analytics
Estimated time: 12 hours
- Star and Snowflake schema design
- OLAP systems and BI integration
- Lakehouse architecture concepts
- Build analytics pipelines and explore lakehouse patterns
Module 5: Data Integration and ETL Pipelines
Estimated time: 12 hours
- Data ingestion techniques
- ETL vs ELT workflows
- Using IBM DataStage and Apache NiFi
- Build ETL workflows with cloud-native tools
Module 6: NoSQL Databases
Estimated time: 6 hours
- Key-value, document, column, and graph databases
- Use cases for NoSQL systems
- Working with MongoDB and Redis
Module 7: Cloud Data Architecture with IBM
Estimated time: 12 hours
- Cloud-native storage solutions
- IBM Cloud Pak for Data
- Data governance in cloud environments
- Design scalable cloud data architectures
Module 8: Capstone Project
Estimated time: 12 hours
- Design an end-to-end data architecture
- Apply data modeling and architectural principles
- Implement using SQL, NoSQL, and cloud tools
Prerequisites
- Familiarity with basic database concepts
- Basic understanding of cloud computing
- Comfort with intermediate technical concepts
What You'll Be Able to Do After
- Design robust data architecture frameworks
- Model and implement relational and NoSQL databases
- Build and manage ETL pipelines and data integration workflows
- Construct data warehouses and lakehouse solutions
- Design scalable, cloud-based data systems using IBM tools