This article discusses data integration, focusing on combining data from multiple sources into a unified view for better analysis. It contrasts ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) methodologies, explaining their approaches to data handling. The article also highlights three types of ETL pushdown techniques and the concept of data orchestrators.
This article explores data loading methodologies, including batch and streaming approaches, and various loading strategies such as full and incremental loads. It also examines Change Data Capture (CDC) techniques and the layered architecture of modern data warehouses, from raw data ingestion to presentation marts.
This article examines various hierarchy types in data modeling, including Fixed Depth Positional Hierarchies, Slightly Ragged Hierarchies, and Ragged Hierarchies. Fixed Depth Hierarchies feature clear many-to-one relationships, such as product to brand, allowing for easy navigation and quick queries. The article discusses strategies for managing ragged hierarchies through the use of bridge tables and pathstring attributes to simplify analysis and improve performance.
This article explores the importance of Data Modeling as a foundational blueprint for organizing information within a business, aiding in the development of a data warehouse. It emphasizes the role of a Logical Data Model (LDM) in establishing frameworks for business intelligence and analytics, ensuring data consistency, quality, and effective communication. The article also contrasts Online Transactional Processing (OLTP) and Online Analytical Processing (OLAP) models and summarizes the transition from a Logical to a Physical Data Model (PDM) for enhanced database performance.
This article outlines a structured approach to Data Design, detailing essential steps for creating effective data models that align with business needs. It covers gathering business requirements, conducting collaborative workshops, and following the Four-Step Dimensional Design Process. The article also features a use case for modeling data in a Fast Food outlet and discusses managing changes in dimension data using Slowly Changing Dimensions (SCDs).
