Читать книгу Data Lakes For Dummies - Alan R. Simon - Страница 52

If your data warehouse is really showing its age, your best bet is to hold a nice retirement party in the company cafeteria with cake and ice cream for everyone and with a few speeches about how wonderful the data warehouse has served the company’s enterprise-wide reporting and business intelligence mission over the years. (Okay, you can probably skip the cake and ice cream, as well as the cafeteria party itself.)

Then you can do the same thing for your data warehouse that you do for any of your creaky, brittle data marts. Build a new set of data feeds from your source applications and systems into the data lake. Then within your data lake, rebuild the data models that your data warehouse used to support business intelligence and reporting alongside machine learning and other advanced analytics (see Figure 2-6).

FIGURE 2-6: Migrating your data warehouse into your new data lake.

Your old data warehouse contents were likely stored in a dimensional model such as a star schema or a snowflake schema. Inside a data lake, the equivalent models might also be dimensional. Alternatively, you could be using a columnar database such as Amazon Redshift. You can still use a visualization tool such as Tableau or a classic business intelligence tool such as MicroStrategy, but your database design will differ from your old data warehouse.