Data Warehouse Views 👓🌆

Designing a Data Warehouse involves looking at it from different angles. Depending on whether you are a developer, an owner, or a user, you see the warehouse differently.

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1. Top-Down View

This view represents the overall Business Goals and strategic direction. It is the "Why" behind the warehouse.

• Decision Support Definition: Clearly identifying which high-level business decisions (e.g., global expansion, budget cuts) the warehouse will support.
• User Population Mapping: Defining who will use the system—executives, analysts, or operational managers—and what specific information they need.

• Resource Planning: Estimating the budget, time, and human talent required to build and maintain the project.

• Outcome KPI Selection: Deciding which Key Performance Indicators (like Year-over-Year Profit) will be the primary measure of success.
• Scope & Boundaries: Determining which departments (e.g., only Sales and Marketing) will be included in the first phase of the DW project.

2. Data Source View

This is the Technical View from the bottom. It focuses on where the "raw materials" come from.

• Operational Source Analysis: Auditing existing RDBMS systems (like Oracle or MySQL) that handle daily transactions.
• External Data Feeds: Identifying third-party data sources such as stock market APIs, weather feeds, or competitor price scrapers.
• Unstructured Source Handling: Planning for data from spreadsheets (Excel), PDF reports, and plain text log files.
• Data Reliability Check: Assessing the "Cleanliness" of source data—identifying which apps have high error rates or missing values.
• Refresh Frequency: Determining how often data needs to be pulled from each source (Real-time vs. Daily vs. Weekly).

3. Data Warehouse View

This view focuses on the Internal Structural Logic of the warehouse. It is where the architect works.

• Fact Table Design: Defining the central "Numbers" that are being tracked (e.g., Total Sales Amount, Unit Quantity).
• Dimension Table Design: Creating the "Context" for the facts (e.g., Product Type, Customer Region, Specific Date).
• Schema Selection: Deciding between a "Star Schema" (simple) or a "Snowflake Schema" (complex) for organizing these tables.
• Data Mart Design: Planning smaller, specialized subsets of the warehouse for specific departments (e.g., a "Sales Data Mart").
• Storage Optimization: Using specialized techniques like "Indexing" and "Partitioning" to ensure that queries on billions of rows stay fast.

4. Business Query View

This is the End-User Perspective. It is how the warehouse "looks" to a manager.

• Semantic Layer: Translating technical table names (like f_sls_01) into human-friendly terms (like Total Gross Sales).
• Dashboard Configuration: Designing the visual interface with charts, maps, and gauges for at-a-glance monitoring.
• Ad-Hoc Query Capability: Giving power users the ability to ask their own "What-If" questions without needing to write SQL code.
• Access Control & Privacy: Ensuring a manager can only see data for their own region or department to maintain security.
• Report Personalization: Allowing users to save their favorite filters, date ranges, and layout preferences for daily use.

Summary

• Top-Down: Decision-making needs and goals.
• Data Source: Technical sources of raw data.
• DW View: Internal schema (Facts and Dimensions).
• Business Query: The interface and reports for the final user.

Quiz Time! 🎯

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