BUSINESS INFORMATION WAREHOUSING (BIW)

Duration of Training : 40 hrs

Why Radical Technologies

Business Information Warehousing (BIW) syllabus, designed to cover essential concepts in data warehousing, business intelligence, and analytics. The course is structured to provide theoretical knowledge along with hands-on experience in using BI tools and techniques for effective decision-making.

Module 1: Introduction to Data Warehousing and BI Concepts (4 hours)

1. Overview of Data Warehousing
   • What is Data Warehousing?
   • Evolution of Data Warehousing and BI
   • Difference Between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing)
2. Components of a Data Warehouse
   • Data Sources and ETL (Extract, Transform, Load) Process
   • Data Staging, Data Integration, and Data Transformation
   • Data Marts, Operational Data Store (ODS), and Enterprise Data Warehouse (EDW)
3. Introduction to Business Intelligence (BI)
   • Definition and Importance of BI in Business
   • Key BI Concepts: Data Mining, Data Analysis, Reporting, and Dashboards
   • Tools and Technologies Used in BI (SAP BI, Power BI, Tableau, etc.)

Module 2: Data Modeling for Data Warehousing (6 hours)

1. Introduction to Data Modeling
   • Importance of Data Modeling in Data Warehousing
   • Conceptual, Logical, and Physical Data Models
2. Star and Snowflake Schema
   • Understanding Star Schema and its Components (Fact and Dimension Tables)
   • Designing a Star Schema for Data Warehousing
   • Difference Between Star Schema and Snowflake Schema
3. Dimensional Modeling
   • Principles of Dimensional Modeling
   • Fact Tables and Types of Facts (Additive, Semi-additive, Non-additive)
   • Dimension Tables and Slowly Changing Dimensions (SCD Types 1, 2, 3)

Module 3: ETL Process (8 hours)

1. Introduction to ETL (Extract, Transform, Load)
   • What is ETL and Why It’s Important for Data Warehousing?
   • ETL Tools and Technologies (Informatica, Talend, SSIS, SAP BODS)
2. ETL Process Stages
   • Extracting Data from Various Data Sources
   • Data Transformation Techniques (Cleaning, Enriching, Aggregating)
   • Loading Data into Data Warehouse and Data Marts
3. ETL Design and Implementation
   • Designing Efficient ETL Workflows
   • Scheduling ETL Jobs and Handling Data Errors
   • Optimizing ETL Processes for Performance and Scalability

Module 4: Data Warehousing Architecture and Design (6 hours)

1. Data Warehousing Architecture Overview
   • Basic and Advanced Architectures (Two-tier, Three-tier, Cloud Data Warehouse)
   • Data Warehouse Lifecycle (Planning, Design, Deployment, Maintenance)
2. Data Storage and Indexing Techniques
   • Partitioning, Sharding, and Indexing Strategies
   • Techniques for Storing and Accessing Large Datasets Efficiently
3. Metadata and Data Governance
   • Role of Metadata in Data Warehousing
   • Importance of Data Governance, Data Quality, and Compliance

Module 5: Business Intelligence and Analytics (6 hours)

1. Introduction to BI Reporting
   • Understanding BI Reporting Tools and Dashboards
   • Key Metrics and KPIs (Key Performance Indicators)
   • BI Tool Overview: SAP BusinessObjects, Power BI, Tableau
2. Creating Reports and Dashboards
   • Designing Reports Using BI Tools
   • Building Dashboards to Visualize Business Metrics
   • Advanced Reporting Techniques (Drill-down, Filters, Conditional Formatting)
3. Data Visualization Principles
   • Importance of Effective Data Visualization
   • Best Practices for Creating Graphs, Charts, and Interactive Reports

Module 6: Data Mining and Advanced Analytics (6 hours)

1. Introduction to Data Mining
   • Data Mining Techniques: Classification, Clustering, Association
   • Importance of Data Mining in BI and Decision-Making
2. Predictive Analytics and Machine Learning
   • Overview of Predictive Models and Algorithms
   • Applications of Machine Learning in BI (Regression, Decision Trees, Neural Networks)
   • Using Predictive Analytics Tools (Python, R, SAS)
3. Big Data and Cloud-based Data Warehousing
   • Introduction to Big Data Analytics and Hadoop Ecosystem
   • Cloud Data Warehousing Platforms (AWS Redshift, Google BigQuery, Azure Synapse)

Module 7: SAP Business Information Warehouse (SAP BW) (8 hours)

1. Introduction to SAP BW
   • Overview of SAP BW/4HANA and Key Features
   • SAP BW Architecture and Data Flows
   • Key Components: InfoProviders, InfoCubes, DataStore Objects (DSOs)
2. Building SAP BW Objects
   • Designing InfoObjects, Key Figures, and Characteristics
   • Creating InfoCubes, DSOs, and MultiProviders
   • Implementing Data Extraction, Loading, and Transformation in SAP BW
3. SAP BW Reporting
   • Designing Reports and Queries in SAP BW
   • Using SAP BW with SAP Business Explorer (BEx)
   • Integrating SAP BW with Other BI Tools (SAP Analytics Cloud, Lumira)

Module 8: Performance Optimization and Best Practices (4 hours)

1. Optimizing Data Warehouse Performance
   • Techniques for Query Optimization and Indexing
   • Partitioning Strategies for Large Datasets
2. Data Warehouse Maintenance
   • Monitoring and Maintaining Data Warehouse Performance
   • Handling Data Warehouse Growth and Scalability
3. Best Practices for BI and Data Warehousing
   • Ensuring Data Accuracy, Security, and Compliance
   • Industry Best Practices for BI and Data Warehousing Projects

Module 9: Case Studies and Capstone Project (6 hours)

1. Real-World Data Warehousing Case Studies
   • Analysis of Successful Data Warehouse Implementations
   • Understanding Challenges in Large-Scale Data Warehousing Projects
2. Capstone Project
   • Design and Implement a Full Data Warehousing Solution
   • End-to-End ETL, Data Modeling, BI Reporting, and Analytics
   • Presenting the Final Project to Peers and Instructors

Learn Business Information Warehousing(BIW) – Course in Pune with Training, Certification & Guaranteed Job Placement Assistance!

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