Data marts—the place where individual departments have their customized analytic data

Applications—the operational applications where day-to-day transactions are run

ETL—the process by which application data are transformed into corporate data

ODS—operational data store—a hybrid structure where integrated data can be quickly accessed online

The archival facility—the process by which older data are removed from active analysis

The refine process—the process by which bulk data are processed and entered into active analysis

Bulk data warehouse—the data warehouse where the single version of the truth data with a low

probability of access is stored

The bulk data vault—the data vault where data with a low probability of access are stored where

rigorous data governance can be done

The data lake—the place where very large volumes of data are stored

The bulk data mart—the data mart built to manage large volumes of data

The data pond—the place where selective subsets of data are stored

Automated generation of data—the mechanism by which large volumes of data are generated

The landing zone—the place where large volumes of data are first touched by the system and are

available for processing

Data lake transformation—the process by which large volumes of data are edited and manipulated

Each of these components will be defined and discussed throughout this book. Each of
these components has their own properties. There is a distinct value to each of these
components.

Different Kinds of Data in the End State Architecture

There are many ways to understand the end-state architecture. One of the easiest ways to
understand the architecture is to examine the different kinds of data that are found in
different places.

Fig. 2.1.2 describes some of the different kinds of data found throughout the architecture.

Chapter 2.1: The End-State Architecture—The “World Map”