Big Data and AI

Introduction One of the most fascinating aspects of Business Intelligence (BI) is the constant evolution of tools and processes. This dynamic environment provides BI professionals with exciting opportunities to build and enhance existing systems. In this post, we explore several modern data storage and processing patterns that BI professionals encounter, and how they relate to data warehouses , data marts , and data lakes . Data Warehouses: A Foundation for BI Systems A data warehouse is a specialized database that consolidates data from multiple source systems, ensuring consistency, accuracy, and efficient access. Historically, data warehouses were built on single machines that stored and computed relational data. With the rise of cloud technologies and the explosion of data volume, new storage and computation patterns have emerged. Data Marts: A Subset for Specific Needs A data mart is a subject‑oriented subset of a larger data warehouse. Because BI projects often fo...

Data is the fuel of the digital economy, but not all data is equally useful or accessible. To make data-driven decisions, you need to store, organize and analyze your data in a way that suits your business needs and goals. One way to do that is to use a data mart . A data mart is a subset of a data warehouse that focuses on a specific business area, department or topic. It provides targeted data to defined users, enabling fast access to critical insights. In this post, we’ll explain what a data mart is, how it differs from a data warehouse and a data lake, and the benefits and challenges of using a data mart. What Is a Data Warehouse? A data warehouse is a centralized repository that stores historical and current data from across an organization. It supports business intelligence (BI) and analytics applications, enabling complex queries, reporting, and advanced analytics. It follows the ETL (extract-transform-load) process and stores structured data fro...