Data Warehouse Architecture Overview

Data warehouse architecture is designed based on the specific use case, such as report generation, exploratory data analysis, automation, machine learning, or self-serve analytics.

General EDW architecture involves multiple components: data sources, staging areas, the enterprise data warehouse repository, data marts, analytics and BI tools, metadata, and ETL (Extract, Transform, Load) processes.

Data cubes are multidimensional data structures used in OLAP (Online Analytical Processing) for efficient data analysis. They have dimensions (coordinates) and facts (cells).

Slicing a data cube is like taking a cross-section of the cube to focus on a specific value or range within a dimension.

Dicing a data cube is like cutting a piece out of the cube by selecting specific values or ranges for multiple dimensions.

Drilling into data cubes allows you to dive deeper into subcategories within a dimension, providing more granular insights.

Vendor-specific reference architectures are adaptations of the general EDW model, tailored to specific vendors and technologies. They include interoperability, tool integrations, and tested cubes, rollups, and materialized views.

Cubes, rollups, and materialized views and tables help improve performance and efficiency in data warehouse systems by providing pre-calculated results for quicker retrieval.

A data cube is a way to represent data in a multi-dimensional format. This allows us to analyze data from different perspectives, such as by region, time period, or product category. Pivoting data cubes essentially means rearranging the data in the cube based on these different dimensions, allowing us to gain insights into the underlying patterns.

Rolling up in data cubes is the process of aggregating data along one or more dimensions. It involves calculating summary statistics for each group defined by these dimensions, essentially summarizing the data by collapsing it onto a smaller dimension.

A materialized view is a snapshot of a query result that is stored separately in a database. It allows for precomputed results, reducing query time and enhancing performance. Materialized views are particularly useful for frequently executed queries, as they avoid recalculating the same data repeatedly.

Facts are quantifiable data points, such as numbers or measurements. These are typically represented in fact tables, along with foreign keys to dimension tables. Facts might include dollar amounts, quantity sold, temperature readings, or a count of events.

Dimensions are attributes that provide context for the facts. They help us understand the facts by providing additional information. Dimensions often represent categories or classifications, such as product categories, locations, time periods, or customer demographics.

Fact tables are tables containing facts, including foreign keys to dimension tables. They represent the core data of a business process. Fact tables can hold both detailed level facts and aggregated facts, which can be used for reporting purposes.

Dimension tables contain the descriptive attributes used to categorize and provide context to the facts in a fact table. Common examples include product tables detailing product characteristics, employee tables outlining employee information, and geography tables outlining locations.

Accumulating snapshot fact tables are designed to capture a series of events during a business process. Each entry in the table represents a specific event, capturing important details about it. These tables are useful for analyzing the progression of a process and identifying key milestones.

Temporal tables store time-related information, recording the date and time attributes for specific events. They are crucial for understanding data trends over time and conducting time-series analysis.