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.
| Module 1 | Fundamentals of Business Intelligence, Data-Warehousing and AnalyticsOperational 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 CycleUnderstanding 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 PatternsArchitectural considerations for data-warehouse, business intelligence and analytics initiatives.Inmon Vs Kimball Vs Hybrid Architectural considerationsConformance matrixCross functional (subject area) analysisLeveraging canonical models Vs Customized enterprise models |
| Module 4 | Business case and data review for work-shop and lab sessionsBusiness case reviewData review for identified business cases |
| Module 5 | Advanced design pattern considerationsAtomic schemaStar SchemaSnow-flake SchemaStar-flake SchemaStar Cluster Schema |
| Module 6 | Advanced dimensional modeling design considerationsTransaction schemas, Temporal models, Bi-Temporal models, Periodic Snapshots, Accumulating Snapshots, Fact-less FactsDimensionality aggregations and granularityLab-Session and hands-on for defining and creating core artifacts |
| Module 7 | Design patterns for Dimensions and HierarchiesSlowly Changing Type 1 to Type 5 dimensionsRagged, balanced, unbalanced and recursive hierarchies |
| Module 8 | Design patterns for TimeUnderstanding ETL / Data-Integration ConceptsWalk-through and hands-on of the entire ETL life cycle |
| Module 9 | Optimizing models and designs by BI platformIBM Cognos design considerationsMicrostrategy design considerationsSAP Business Objects design considerationsTableau design considerationsMicrosoft Business Intelligence and AnalyticsOptimizing models and designs by data enginesIn-Memory SAP Hana design considerationsNetezza design considerationsHadoop design considerationsOracle Exadata design considerations |
| Module 10 | Corporate Performance ManagementKPI conformance, rollup and cross functional performance analysisConformance to achieve enterprise performance managementSig sigma design considerations |
| Module 11 | Big Data design donsiderationsReal time Big-Data models (HBase, Cassandra, MongoDB)Analytical models (Hive, Impala, Spark) |
| Module 12 | Data governance and data qualityModeling Conventions, Standards and GuidelinesData quality, completeness, accuracy, consistencyMaster data management |