This article compares monolithic data lake architecture with the decentralized data mesh approach. While data lakes centralize data for easier access, they face scalability challenges. Data mesh treats data as a product owned by domain teams, enhancing agility through four key principles: domain-oriented ownership, data as a product, self-serve infrastructure, and federated governance.
This article explores the world of Big Data, covering core concepts like the 4Vs (Volume, Velocity, Variety, Veracity), key technologies including Hadoop, Kafka, and Spark, and modern architectures such as Persistent Staging Areas and real-time processing systems. It provides a comprehensive overview of the technologies that emerged to address the challenges of modern data growth beyond traditional database capabilities.
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.
