A great deal of hype recently has been directed toward the data scientists who use powerful algorithms and visualization tools to develop new ways of analyzing business data and find new insights.  This is challenging, creative work, but by itself a new model or report only provides a one-time benefit.  There is an increasingly important new role that has received much less attention than it deserves: that of the data engineer who can take a new model or algorithm and automate it, making it repeatable and accessible to non-expert users such as managers and customers.  These unsung heroes create analytical systems, also called "data products", that are critical for organizations to reap ongoing benefits from their data assets.

In A Data Engineer's Manual, we dive into a hierarchy of fundamental knowledge you'll need to understand and work on data products.  We will explore "data in the wild", that is, what forms it takes and how it is communicated over the Internet; learn about the roles played by different types of databases---relational, dimensional, and NoSQL; and examine how new data technologies change analytics workflows and deliver value to the business.