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Business Information Warehousing (BIW)
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4.3 ( 2241 Ratings )
BIW was a data warehousing and business intelligence concept and solution introduced by SAP before it evolved into more advanced and integrated offerings. It aimed to provide organizations with a centralized repository for collecting, storing, and analyzing business data from various sources. It’s important to note that the term BIW is now largely obsolete, as SAP has moved forward with more advanced and integrated solutions for data warehousing (SAP BW) and business intelligence (SAP BusinessObjects, SAP Analytics Cloud, etc.).
Overview
The Syllabus
Curriculum Designed by Experts
Module 1: Introduction to Data Warehousing and BI Concepts (4 hours)
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)
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)
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)
Introduction to Data Modeling
- Importance of Data Modeling in Data Warehousing
- Conceptual, Logical, and Physical Data Models
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
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)
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)
ETL Process Stages
- Extracting Data from Various Data Sources
- Data Transformation Techniques (Cleaning, Enriching, Aggregating)
- Loading Data into Data Warehouse and Data Marts
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)
Data Warehousing Architecture Overview
- Basic and Advanced Architectures (Two-tier, Three-tier, Cloud Data Warehouse)
- Data Warehouse Lifecycle (Planning, Design, Deployment, Maintenance)
Data Storage and Indexing Techniques
- Partitioning, Sharding, and Indexing Strategies
- Techniques for Storing and Accessing Large Datasets Efficiently
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)
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
Creating Reports and Dashboards
- Designing Reports Using BI Tools
- Building Dashboards to Visualize Business Metrics
- Advanced Reporting Techniques (Drill-down, Filters, Conditional Formatting)
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)
Introduction to Data Mining
- Data Mining Techniques: Classification, Clustering, Association
- Importance of Data Mining in BI and Decision-Making
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)
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)
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)
Building SAP BW Objects
- Designing InfoObjects, Key Figures, and Characteristics
- Creating InfoCubes, DSOs, and MultiProviders
- Implementing Data Extraction, Loading, and Transformation in SAP BW
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)
Optimizing Data Warehouse Performance
- Techniques for Query Optimization and Indexing
- Partitioning Strategies for Large Datasets
Data Warehouse Maintenance
- Monitoring and Maintaining Data Warehouse Performance
- Handling Data Warehouse Growth and Scalability
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)
Real-World Data Warehousing Case Studies
- Analysis of Successful Data Warehouse Implementations
- Understanding Challenges in Large-Scale Data Warehousing Projects
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
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Specialized Knowledge
Red Hat Linux is a popular distribution used in enterprise environments due to its stability, security, and support offerings. By gaining expertise in Red Hat Linux through a certification course, you position yourself as a specialist in this specific distribution, which can make you more attractive to employers who use or plan to use Red Hat Linux.
Job Opportunities
Holding a Red Hat certification opens up various job opportunities in roles such as Linux system administrator, DevOps engineer, cloud engineer, network engineer, and more. Many job postings in these fields specifically mention Red Hat certifications as desired or required qualifications.
Salary Potential
Professionals with Red Hat certifications often command higher salaries compared to their non-certified counterparts. Employers value certified individuals for their proven skills and expertise, leading to potentially higher earning potential.
Career Advancement
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Red Hat Linux Course Projects
Infrastructure Provisioning
And Configuration Management
Implementing automated infrastructure provisioning and configuration management using Ansible. This may include setting up servers, networking devices, and other infrastructure components using playbooks and roles.
Applications Deployment
And Orchestration
Automating the deployment and orchestration of applications across development, testing, and production environments. This could involve deploying web servers, databases. middleware, and other application components using Ansible
Continuous Integration
And Continuous Deployment
Integrating Ansible into CI/CD pipelines to automate software. build, test, and deployment processes. This may include automating the creation of build artifacts, running tests, and deploying applications to various environments.