Читать книгу Data Lakes For Dummies - Alan R. Simon - Страница 33

In the early 1990s, data warehousing was the newest and most popular game in town for analytical data management. By the mid-’90s, the concept of a data warehouse was adapted to a data mart — essentially, a smaller-scale data warehouse. The original idea behind a data mart called for the data warehouse feeding a subset of its data into one or more data marts — sort of a “wholesaler-retailer” model.

The first generation of data warehouse projects, especially very large ones, was hallmarked by a high failure rate. By the late ’90s, data warehouses were viewed as large, complex, and expensive efforts that were also very risky. A data mart, on the other hand, was smaller, less complex, and less expensive, and, thus, considered to be less risky.

The need for integrated analytical data was stronger than ever by the end of the ’90s. But just try to get funding for a data warehousing project! Good luck!

Time for plan B.

Data warehouses went out of style for a while. Instead, data marts became the go-to solution for analytic data. No matter how big and complex an environment was, chances are, you’d refer to it as a data mart rather than a data warehouse. In fact, the idea of an independent data mart sprung up, and the original architecture for a data mart — receiving data from a data warehouse rather than directly from source systems — became known as a dependent data mart.

Fast-forward a couple of decades, and it’s back to the future. First, big data sort of evolved into data lakes. Now you have analysts, consultants, and vendors complicating the picture with their own terminology. This won’t be the last time you’ll see shifting names and terminology in the world of analytic data, so stay tuned!