Data Warehousing and Dimensional Modeling

These flashcards cover essential vocabulary relevant to dimensional modeling and data mining within data warehousing.

Data Warehouse

A centralized repository for storing large amounts of data, optimized for query and analysis.

Dimensional Modeling

A data modeling technique used to design data warehouses with optimized data retrieval.

Fact Table

Central table in a dimensional model that stores measurable business data.

Dimension Table

Table that contains attributes related to the facts, used to categorize facts.

Star Schema

A type of database schema that consists of a central fact table surrounded by dimension tables.

Snowflake Schema

A more complex schema compared to star schema which normalizes dimension tables into multiple related tables.

Galaxy Schema

Also known as fact constellation schema, contains multiple fact tables sharing common dimension tables.

Fact Constellation Schema

A schema that includes multiple fact tables and associated dimension tables.

Conformed Dimension

Dimension that has the same meaning across different fact tables.

Outrigger Dimension

A dimension that connects different dimension tables to provide additional attributes.

Shrunken Dimension

A reduced granularity of a dimension to optimize performance.

Role-Playing Dimension

A dimension that has multiple valid relationships with other tables.

Degenerate Dimension

Dimension that is derived from fact table attributes and does not have its own dimension table.

Junk Dimension

A dimension that combines multiple low cardinality attributes to reduce complexity.

Swappable Dimension

Dimension with multiple versions that can be swapped at query time.

Step Dimension

Dimension that identifies the step in a process, with each step assigned a number.

Attributes

Characteristics of a dimension that describe the data and can be used for filtering.

Data Mining

The process of analyzing large datasets to identify patterns and insights for decision-making.

Classification

Data mining technique used to categorize data based on predefined labels.

Clustering

Grouping similar data points in data mining without prior labels.

Association Rule Mining

Technique to find relationships between variables in large datasets.

Anomaly Detection

Identifying data points that deviate from expected patterns in data mining.

Regression Analysis

Predicting numeric values based on historical data.

Text Mining

Analyzing unstructured text data to extract meaningful information.

Neural Networks

Complex models that learn patterns from large datasets in data mining.

SQL

Structured Query Language used for managing and querying relational databases.

Data Segmentation

Dividing a dataset into distinct subgroups for analysis.

Fraud Detection

Analyzing data to uncover and prevent fraudulent activities.

Risk Assessment

Evaluating potential risks based on data analysis.

Compliance Analysis

Ensuring adherence to regulations through data examination.

Data Integration

Combining data from different sources into a coherent data set.

Performance Analysis

Evaluating performance metrics to inform business decisions.

Customer Segmentation

Categorizing customers into groups for targeted analysis.

OLAP Systems

Online Analytical Processing systems designed for complex queries and data analysis.

Fact Metrics

Quantifiable measurements that represent business performance.

Dimension Attributes

Qualitative fields that provide context to the facts in a data model.

Database Normalization

Organizational process of structuring a relational database to reduce redundancy.

Data Retrieval Optimization

Techniques to improve the speed and efficiency of data access.

Data Analysis Tools

Software applications used for performing extensive data analysis.

Historical Data

Past data utilized for forecasting and predictive analysis.

Financial Risk Assessment

Analyzing data to identify potential financial threats to an organization.

Data Visualization

The graphical representation of information and data to communicate insights.

Business Intelligence

Technologies and strategies for analyzing business data to enhance decision-making.

Granularity

The level of detail represented in data.

Query Complexity

An assessment of how intricate a database query is to execute.

Data Governance

Managing the availability, usability, integrity, and security of data used in an organization.

Operational Data Store (ODS)

Database designed for day-to-day operations and immediate data access.