Introduction to OLTP and OLAP in Business Intelligence

Introduction to OLTP and OLAP

• OLTP (Online Transaction Processing) focuses on transaction-oriented applications
• Used for everyday operations like order processing, sales, and financial transactions
• Characteristics include high transaction volume, fast response times, and a normalized database structure
• OLAP (Online Analytical Processing) is designed for analytical queries and decision making
• Helps with multidimensional analysis of business data for insights and trends
• OLAP uses a denormalized database structure and data aggregation for complex queries

BI Definitions & Concepts

• Business Intelligence (BI) refers to technologies, processes, and applications for collecting, integrating, analyzing, and presenting business data
• The goal of BI is to support better decision making
• Components of BI include data warehousing, data mining, dashboards, and reporting tools

Key Concepts in BI

• Data Warehousing: A centralized repository for historical data
• Analytics: Using data analysis techniques to identify patterns
• Visualization: Uses charts, graphs, and dashboards to present data
• Performance Management: Measuring and optimizing organizational performance

Business Applications of BI

• Customer Relationship Management (CRM): Understanding customer behavior and preferences
• Supply Chain Management (SCM): Optimizing logistics and inventory
• Financial Analysis: Budgeting, forecasting, and profitability analysis
• Marketing: Campaign performance tracking and segmentation
• Human Resources: Workforce analytics and employee performance evaluation

BI Framework

• Data Sources: Includes operational systems, external sources, and unstructured data
• ETL (Extract, Transform, Load): Processes raw data into usable formats
• Data Warehousing: Stores integrated and historical data
• BI Tools and Applications: Used for reporting, dashboards, and analytics by business users, managers, and executives

Role of Data Warehousing in BI

• Centralizes data from various sources
• Supports historical analysis and trend identification
• Ensures data consistency and reliability
• Enables faster query performance by optimizing data for analysis

BI Infrastructure Components

• BI Process involves data collection, integration, storage, analysis, reporting, and decision making
• BI Technology includes ETL tools, data warehousing, OLAP engines, and visualization tools
• BI Roles and Responsibilities include analysts, architects, developers, and data engineers managing data integration, report creation, and decision support

Extraction, Transformation, and Loading (ETL)

• Extraction: Retrieving data from various sources
• Transformation: Cleaning and converting data into a required format
• Loading: Storing the processed data in a data warehouse or repository

Concepts of Data Integration

• Need: Organizations often use diverse systems resulting in fragmented data, so integration consolidates data for unified analysis
• Advantages: Increased decision-making, improved operational efficiency, and consistency across applications

Introduction to SSIS (SQL Server Integration Services)

• A Microsoft tool used for data integration and workflow automation
• Facilitates ETL operations.

Introduction to Data Quality and Data Profiling Concepts

• Data Quality: Refers to accuracy, completeness, consistency, and reliability of data
• Data Profiling: Analyzing data to understand its structure and content
• Used for identifying anomalies, ensuring data accuracy, and preparing for integration

Power Query

• A data connection and transformation tool used in Excel and Power BI
• Features data import from multiple sources, user-friendly data transformation, and automated data cleaning processes

Different Views in Power BI

• Allows creation of reports using visualizations
• Data View enables viewing and manipulating tabular data
• Model View allows management of relationships between tables

Cardinality in Power BI

• Refers to the uniqueness of data values in a column
• Types include High Cardinality (many unique values) and Low Cardinality (few unique values)
• Crucial in defining relationships between tables in a Power BI model

Introduction to Data and Dimension Modeling

• Data Modeling: Structuring data for efficient analysis
• Dimension Modeling: Organizing data into dimensions (attributes) and facts (measurable data)

Multidimensional Data Model

• Organizes data into cubes with dimensions and facts
• Enables slicing, dicing, and drilling down operations for detailed analysis

ER Modeling vs. Multidimensional Modeling

• ER Modeling: Used in OLTP systems, focusing on relational structure
• Multidimensional Modeling: Used in OLAP systems, focusing on analytical queries involving facts and dimensions

Concepts of Dashboards

• Definition: A visual interface displaying key performance indicators (KPIs)
• Uses: Support real-time monitoring, trend analysis, and quick decision making
• Features: Include interactive visualizations, customizable layouts, and data drill-down capabilities