LLT03
Multi-Dimensional Modeling Workshop for Data-Warehouse
The purpose of this course is to provide a solid background coupled with extensive practical exposure on modeling and designing business intelligence, data warehouse, big-data and data analytics solutions based on modern tried and tested techniques, practices and methodologies.
This course is designed for individuals interested in securing deep knowledge and hands-on experience in designing and modeling business intelligence, data warehouse and information analytics solutions. Targeted audience include data architects, database developers, data integrators, business analysts, system analysts as well as business managers, project managers, program managers and other project stakeholders interested in data analytics related initiatives.
Register for this course
| Module 1 |
Fundamentals of Business Intelligence, Data-Warehousing and Analytics
- Operational systems vs Analytical systems and their respective data models.
- Information transformation from transactional data into dimensional data to business metrics and KPIs.
- From reporting to intelligent analysis into smart systems
|
| Module 2 |
Understanding Data-Warehouse & Business Intelligence Life Cycle
- Understanding data-warehouse, business intelligence and analytics life-cycles, processes, artifacts, deliverables, roles and responsibilities.
- Understanding the project management life cycle (PMLC) and the software development life cycles (SDLC) including Agile methodologies for data-warehouse, business intelligence and analytics initiatives.
- Gathering and analyzing business requirements for business intelligence initiatives
|
| Module 3 |
Architectural Dimensional Modeling Patterns
- Architectural considerations for data-warehouse, business intelligence and analytics initiatives.
- Inmon Vs Kimball Vs Hybrid Architectural considerations
- Conformance matrix
- Cross functional (subject area) analysis
- Leveraging canonical models Vs Customized enterprise models
|
| Module 4 |
Business case and data review for work-shop and lab sessions
- Business case review
- Data review for identified business cases
|
| Module 5 |
Advanced design pattern considerations
- Atomic schema
- Star Schema
- Snow-flake Schema
- Star-flake Schema
- Star Cluster Schema
|
| Module 6 |
Advanced dimensional modeling design considerations
- Transaction schemas, Temporal models, Bi-Temporal models, Periodic Snapshots, Accumulating Snapshots, Fact-less Facts
- Dimensionality aggregations and granularity
- Lab-Session and hands-on for defining and creating core artifacts
|
| Module 7 |
Design patterns for Dimensions and Hierarchies
- Slowly Changing Type 1 to Type 5 dimensions
- Ragged, balanced, unbalanced and recursive hierarchies
|
| Module 8 |
Design patterns for Time
- Understanding ETL / Data-Integration Concepts
- Walk-through and hands-on of the entire ETL life cycle
|
| Module 9 |
Optimizing models and designs by BI platform
- IBM Cognos design considerations
- Microstrategy design considerations
- SAP Business Objects design considerations
- Tableau design considerations
- Microsoft Business Intelligence and Analytics
Optimizing models and designs by data engines
- In-Memory SAP Hana design considerations
- Netezza design considerations
- Hadoop design considerations
- Oracle Exadata design considerations
|
| Module 10 |
Corporate Performance Management
- KPI conformance, rollup and cross functional performance analysis
- Conformance to achieve enterprise performance management
- Sig sigma design considerations
|
| Module 11 |
Big Data design donsiderations
- Real time Big-Data models (HBase, Cassandra, MongoDB)
- Analytical models (Hive, Impala, Spark)
|
| Module 12 |
Data governance and data quality
- Modeling Conventions, Standards and Guidelines
- Data quality, completeness, accuracy, consistency
- Master data management
|
| Register for this course |
