Database Reference
In-Depth Information
augment what is already stored in your existing data warehouse and that
could potentially make it much more interesting and valuable to your
traditional BI analyst. Our goal is to identify this data and provide an
integrated solution for your organization.
These hybrid solutions, shown in Figure 16.1 , including Hadoop, a data
warehouse, and a BI environment are the near future state of a typical
analytics environment. Although it makes sense to store log files, social
network feeds, user location data, telemetry data, and other big data sources
in Hadoop, this isn't the only thing that can benefit from this data. Your
traditionaldatawarehousecantakeinsomeofthisdatatoenhanceitsvalue.
Figure 16.1 The modern data warehouse
You might be thinking, “Okay, this sounds good, but how do I know which
data should be integrated into my current data warehouse?” Well, this is
where you trust your data scientists who have access to the data in Hadoop
and the subject matter experts who explore your current data warehouse
solution daily.
The data scientists will explore the data and look for relationships among
the new data being collected. They will also most likely be extracting some
of your subject-oriented warehouse data into Hadoop to help augment their
data sets. As they explore the data and find relationships in the data, they
will identify what is useful to the organization. It might sometimes be of use
one time and thus the analysis is done, the report is written, and lessons will
be learned. Many other times, though, they will find insights in the data that
can be reused and watched for on a regular basis. It is this data that they
have identified you want to operationalize into your solution.
 
 
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