Introduction

In the early days of computing, businesses struggled to keep up with the flood of data. They had few options for storing and analyzing data, hindering their ability to make informed decisions. As technology improved, businesses recognized the value of data and needed a way to make sense of it. This led to the birth of data warehousing, coined by Bill Inmon in the 1980s. Inmon’s approach was focused on structured, relational data for reporting and analysis. Early data warehouses were basic but set the stage for more advanced solutions as businesses gained access to more data. Today, new technologies like Big Data and data lakes have emerged to help deal with the increasing volume and complexity of data. The data lakehouse combines the best of data lakes and warehouses for real-time processing of both structured and unstructured data, allowing for advanced analytics and machine learning. While the different chapters of this book cover all aspects of modern data warehousing, this chapter specifically focuses on the transformation of data warehousing techniques from past to present to future, and how it impacts building a modern data warehouse.