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Data Warehouse Introduction to Data Warehouse
- 2. Introduction to Data Warehouse: • A Data Warehouse (DW) is a centralized repository designed for efficiently storing, managing, and analyzing large volumes of structured data collected from various sources. • It supports decision-making by enabling complex queries and data analysis. • Unlike operational databases that handle day-to-day transactions, data warehouses focus on historical data analysis and are optimized for read-intensive workloads
- 3. Benefits of Data Warehousing • Improved Decision-Making: Provides a comprehensive view of business operations for informed decision-making. • Enhanced Performance: Optimized for complex queries, improving analysis speed. • Data Consolidation: Integrates data from various sources, eliminating silos. • Scalability: Supports growing volumes of data and users. • Historical Insights: Enables trend analysis and predictive analytics. •
- 4. Historical Development of Data Warehousing: 1960s–1970s: Early Data Storage • Data stored in hierarchical and network databases like IBM’s IMS. • Introduction of the relational database model by Edgar F. Codd in 1970. 1980s: Birth of Data Warehousing • Bill Inmon introduced the concept of a data warehouse as subject-oriented, integrated, time- variant, and non-volatile. • IBM introduced early data warehousing ideas. • Challenges emerged in integrating data from multiple sources. 1990s: Adoption and Growth • ETL tools were developed for data integration. • OLAP (Online Analytical Processing) enabled multi-dimensional data analysis. • Ralph Kimball proposed a "bottom-up" approach using data marts. • Companies like Oracle, Teradata, and Microsoft launched data warehouse solutions. •
- 5. 2000s: Enhanced Performance • Introduction of Massive Parallel Processing (MPP) for large datasets. • Integration with Business Intelligence (BI) tools for better reporting. • Real-time analytics through Operational Data Stores (ODS). • Pre-configured solutions like Netezza simplified deployments. • 2010s: Cloud and Big Data • Cloud-based warehouses like Amazon Redshift and Snowflake gained popularity. • Hybrid architectures combining data lakes and data warehouses emerged. • Big data tools like Hadoop and Spark integrated with traditional warehouses. • Self-service analytics tools allowed non-technical users to analyze data. • •
- 6. • 2020s: Modern Innovations • Data Lakehouse combined data lakes’ flexibility with data warehouses’ structure. • AI and machine learning became common in data warehouses. • Serverless architectures reduced infrastructure management. • Emphasis on data governance and security due to regulations. •
- 8. Data Warehouse Models Enterprise Data Warehouse (EDW) • A centralized and comprehensive repository for the entire organization's data. • Characteristics: • Unified view of all data. • Supports long-term historical data storage. • Integrates data from multiple departments. • Use Cases: • Enterprise-wide reporting and analytics. • Strategic decision-making. • •
- 9. 2. Data Mart • A smaller, focused subset of a data warehouse tailored for specific business functions. • Characteristics: • Department-specific (e.g., sales, marketing, finance). • Quicker to implement and easier to manage. • Can be dependent (linked to an EDW) or independent. • Use Cases: • Departmental analytics and reporting. • Quick insights for specific teams. •
- 10. 3. Operational Data Store (ODS) • A database that integrates data from operational systems for real-time or near-real-time reporting. • Characteristics: • Stores current (not historical) data. • Frequently updated for operational use. • Acts as an intermediary between transactional systems and the data warehouse. • Use Cases: • Real-time dashboards and reporting. • Feeding fresh data into operational applications. •
- 11. Multitier Architecture in Data Warehousing • A multi-tier architecture, also known as n-tier architecture, is a software design pattern that divides an application into distinct layers or tiers, each responsible for specific functions. This separation enhances scalability, maintainability, and flexibility. • • Common Layers in Multi-Tier Architecture: • Presentation Layer (Client Tier): • Function: Manages user interactions and displays information. • Components: User interfaces such as web browsers or desktop applications. •
- 12. • Application Layer (Business Logic Tier): • Function: Processes user inputs, applies business rules, and manages application logic. • Components: Application servers or services that handle business processes. • Data Layer (Data Tier): • Function: Handles data storage and retrieval. • Components: Databases or data storage systems. •