Overview
This lecture introduced the fundamentals of data warehousing and business intelligence (BI), covering key concepts, architectures, tools, and a practical demonstration using Talend.
Need for Business Intelligence
- Business intelligence (BI) transforms raw data into useful information for informed decision-making and business growth.
- BI relies on data warehousing to integrate and prepare data for analysis and visualization.
- Successful companies use BI to plan, strategize, analyze performance, and execute business actions effectively.
Data Warehousing Fundamentals
- A data warehouse (DW) is a central repository consolidating data from multiple sources or databases.
- Data in a DW is separated from operational systems to preserve data integrity and enable historical analysis.
- The ETL process (Extract, Transform, Load) moves data from source systems into the DW for analysis.
- End users, such as managers and analysts, access the DW for reports, insights, and trend analysis.
Advantages and Properties of Data Warehousing
- Enables strategic analysis by integrating and organizing subject-oriented, historical data.
- Improves data accuracy, consistency, and speed of analysis compared to operational databases.
- Four core properties: subject-oriented, integrated, time-variant (supports historical comparison), non-volatile (data is stable and rarely updated).
- Data warehouse is a custom-built strategy, not an off-the-shelf product.
Key Terminologies
- OLTP (Online Transaction Processing): Supports transactional databases for real-time, high-volume operations.
- OLAP (Online Analytical Processing): Enables complex analytical queries on historical DW data.
- ETL (Extract, Transform, Load): The process pipeline to migrate, clean, and format data for the DW.
- Data Mart: A smaller, subject-specific subset of a DW, often department-focused (e.g., sales, marketing).
- Metadata: Data about data, such as source, structure, location, and transformation rules, critical for DW management.
Data Warehouse Architecture
- Data flows from multiple sources through ETL to a staging area, then into the DW.
- The DW contains raw, aggregated, and metadata, powering BI tools and user access.
- Data marts provide secure, subject-specific access for distinct teams.
- Architecture ensures data integration, security, and ready access for business analysis.
Demonstration Summary (using Talend)
- Example: Import customer and transaction data from Oracle DB to Talend.
- Used ETL process to join tables, filter for low-purchase customers, and export results to Excel/database outputs.
- Showed schema mapping, lookup joins, row filtering, data output, and managing rejected records.
Key Terms & Definitions
- Business Intelligence (BI) — Converting raw data into actionable business information.
- Data Warehouse (DW) — Central, historical data repository for analysis.
- ETL — Extract, transform, and load process for data warehousing.
- OLTP — Supports transactional, real-time data operations.
- OLAP — Supports analytical processing on historical data.
- Data Mart — Department-specific subset of a data warehouse.
- Metadata — Information that describes and manages data sources, transformations, and storage.
Action Items / Next Steps
- Review and practice ETL processes using Talend or similar ETL tools.
- Prepare for the next session focusing on schema designs, dimensions, and fact tables.
- Optional: Explore additional readings on Star, Snowflake, and Galaxy schema architectures.