Data Warehousing Overview

Data Warehousing Overview

• A data warehouse is a centralized repository that integrates and stores large volumes of structured, historical data from various sources within an organization.
• It's designed for business intelligence (BI) activities, like reporting, analysis, and decision-making.
• Data warehouses provide a consolidated view of organizational data, allowing users to analyze trends, identify patterns, and gain valuable insights to inform strategic and operational decisions.
• Data warehouses play a crucial role in business intelligence by offering decision-makers a unified and consistent view of historical data.

Key Characteristics of a Data Warehouse

• Subject-Oriented: Organized around specific business subjects (e.g., sales, finance, customer relations) to support analytical queries and reporting.
• Integrated Data: Data from disparate sources (databases, spreadsheets, external systems) is integrated and transformed to ensure consistency and coherence within the warehouse. ETL (Extract, Transform, Load) processes facilitate this integration.
• Time-Variant: Data is time-stamped, enabling the analysis of trends and changes over time for historical analysis and reporting.
• Non-Volatile: Data is not updated or deleted once loaded into the warehouse (unlike operational databases), ensuring a stable environment for analytical processing.
• Optimized for Query and Reporting: Structured and indexed for efficient querying and reporting. Often uses denormalized schemas like star or snowflake schemas to simplify and accelerate analytical queries.

Data Warehouse VS Database

• Purpose: Data warehouses are for analytical processing and business intelligence, optimized for complex queries and reporting. Databases are for transactional processing and day-to-day operations, focused on efficient data retrieval, insertion, and updating.
• Data Types: Data warehouses store historical, structured data, often from multiple sources. Databases store operational data (often real-time), primarily containing current information.
• Schema Design: Data warehouses use specialized schemas like star or snowflake schemas for efficient querying and reporting. Databases typically use normalized schemas to reduce redundancy and maintain data integrity in transactional processing.
• Data Integration: Data warehouses involve the integration of data from various sources using ETL processes. Databases typically focus on maintaining consistency within the operational context.
• Data Volatility: Data warehouses are non-volatile, historical data is rarely updated. Databases are volatile, data is frequently updated as part of ongoing transactions.
• Query Optimization: Data warehouses are optimized for complex queries. Databases are optimized for fast retrieval and updating of individual records.
• User Base: Data warehouses are primarily used by analysts, data scientists, and decision-makers for in-depth analysis. Databases are used by application developers, system administrators, and staff for operational support.
• Processing: Data warehouses use Online Analytical Processing (OLAP). Databases use Online Transactional Processing (OLTP).

Main Components of a Data Warehouse

• Data Sources: Systems or applications that generate and store data (e.g., operational databases, external data feeds, spreadsheets).
• ETL (Extract, Transform, Load) Processes: Responsible for extracting data from various sources, transforming it to conform to the data warehouse's structure and quality standards, and loading it into the warehouse.
• Data Warehouse Database: The central repository that stores the integrated, transformed data. Designed for analytical querying and reporting, often utilizing specialized database management systems.
• Data Marts: Subsets of the data warehouse focusing on specific business functions or departments to meet the needs of particular groups of users.
• OLAP (Online Analytical Processing) Servers: Enable interactive analysis and exploration of data in multidimensional ways. Employ slicing, dicing, drilling down, and complex analyses.

Data Warehouse Design

• Schema: A logical description of the entire Data Warehouse database structure. Data warehouses use schemas (Star, Snowflake, Fact Constellation).
• Star Schema: A central fact table surrounded by dimension tables. The fact table contains numerical measures, while dimension tables provide descriptive information. Each dimension is represented with one dimension table.
• Snowflake Schema: An expanded star schema where dimension tables are normalized into related tables to potentially save storage space.
• Fact Constellation Schema: A schema with multiple fact tables that are related. It's also known as a galaxy schema.

Data Mart

• A data mart is a smaller, specialized subset of a data warehouse, focusing on specific business areas, departments, or user groups. Often tailored for specific business needs, and optimized for particular users.

Data Warehouse Architecture Types

• Centralized Data Warehouse: A single, unified repository managing data from various sources for centralized business intelligence and decision-making.
• Data Marts: Smaller, specialized subsets of a Data Warehouse, focused on specific subjects.
• Federated Data Warehouse: An architecture integrating data from multiple independent data sources without physically consolidating the data, enabling distributed access and processing.
• Hybrid Data Warehouse: Combines elements of centralized and distributed architectures. May involve on-premises and cloud-based solutions.

Kimball Approach

• Emphasizes dimensional modeling (star or snowflake schemas) and a bottom-up development strategy, starting with data marts.
• Prioritizes the creation of data marts that address specific business requirements, and then integrates them into the complete warehouse.
• Utilizes ETL processes specifically designed for dimensional models.

Inmon Approach

• Focuses on the creation of a centralized Enterprise Data Warehouse (EDW) as the foundation, serving as the single repository for the entire organization.
• Employing a top-down development strategy, starting with the full enterprise-wide data warehouse.
• The approach begins with the enterprise-wide data warehouse and then focuses on developing data marts that meet specific business needs, which are subsets of the EDW.

Data Architecture vs. Data Modeling

• Data architecture provides the high-level structure and considerations of how data is handled and managed within the organization. Data modeling designs the specific and detailed structure of data within the database.

This quiz covers the fundamental concepts of data warehousing, including its purpose, characteristics, and role in business intelligence. Get ready to explore how data warehouses consolidate historical data for reporting and analysis, aiding decision-making in organizations.