Category: data_warehouse
Data Retention: Understanding the Maximum Retention Period in Kinesis Streams and Extension Methods
Understanding the retention period for data stored in Kinesis Streams is crucial for managing data lifecycle and ensuring compliance with…
Idempotency in AWS Kinesis Streams
In this comprehensive guide, we delve into the concept of idempotency in AWS Kinesis Streams, uncovering top-tier techniques and best…
Learn Data Warehousing
1. Introduction to Data Warehousing Definition and Overview Importance and Benefits Data Warehouse vs. Database 2. Data Warehouse Basics Key…
Exploring Software Components in Data Warehouse Infrastructure
In the intricate tapestry of data warehousing, the software components comprising the infrastructure serve as the bedrock upon which the…
Exploring Hardware Considerations for Data Warehouse Infrastructure
In the realm of data warehousing, the infrastructure forms the foundation upon which the entire ecosystem operates. Selecting the appropriate…
Integrating Data Warehouses and Data Lakes in Modern Architectures
In the ever-evolving landscape of data management, the integration of Data Warehouses (DW) and Data Lakes (DL) has emerged as…
Unveiling Data Warehouse Architectures : Lambda and Kappa Architectures
In the realm of data warehousing, architects and engineers often find themselves at a crossroads, deliberating over the most suitable…
Exploring Data Loading Strategies in ETL Processes
Data loading is the final stage in the Extract, Transform, Load (ETL) process, where transformed data is loaded into the…
Data Transformation Logic in ETL Processes
Data transformation is a pivotal stage in the Extract, Transform, Load (ETL) process, where raw data is refined, cleansed, and…
Mastering Data Extraction: Techniques for Setting up an ETL Process
Data extraction is the first step in the Extract, Transform, Load (ETL) process, involving retrieving data from diverse sources. This…
