Loading content
Data & Business Intelligence encompasses the strategies, technologies, and practices for analyzing and utilizing data to support business decisions.
This segment covers modeling data for analytics and reporting, including shared terminology and definitions. It includes semantic layers, fact and dimension models, metric and attribute definitions, and rules for consistency and reuse. The focus is on translating raw data into understandable, stable meaning structures, independent of specific visualizations or operational data quality enforcement.
A workshop aimed at enhancing data modeling skills within an organization.
A method for continuously adapting and improving database schemas with minimal disruptions.
Semantic mapping is a method for organizing information through logical connections.
A canonical data model describes a standardized data structure for the integration and exchange of information between different systems.
Columnar databases store data in columns instead of rows, leading to faster queries and more efficient analytics.
A pragmatic approach to modeling data warehouses that structures data into fact and dimension tables for efficient analysis and query performance.
A logical data model describes the structure and relationships of data within a specific domain.
A physical data model defines the physical storage and structure of data in a database.
Schema Definition Languages are essential tools for describing data schemas and assist in software development through validation.
The semantic layer enhances data accessibility.
DataGrip is a powerful SQL development environment for database administrators and developers.
Erwin Data Modeler is a powerful data modeling tool that helps organizations visualize and manage their data architecture.
dbt is an open-source data modeling tool that transforms data warehouses.