Database Reference
In-Depth Information
Chapter 4. Operational Intelligence
The first use cases we'll explore lie in the realm of
operational intelligence
, the techniques
of converting transactional data to actionable information in a business setting. Of course, the
starting point for any of these techniques is getting the raw transactional data
into
your data
store. Our first use case,
Storing Log Data
, deals with this part of the puzzle.
Once you have the data, of course, the first priority is to generate actionable reports on that
data, ideally in real time with the data import itself. We address the generation of these reports
in real time in
Pre-Aggregated Reports
.
Finally, we'll explore the use of more traditional batch aggregation in
Hierarchical Aggrega-
tion
to see how MongoDB can be used to generate reports at multiple layers of your analytics
hierarchy.
Storing Log Data
The starting point for any analytics system is the raw “transactional” data. To give a feel for
this type of problem, we'll examine the particular use case of storing event data in MongoDB
that would traditionally be stored in plain-text logfiles. Although plain-text logs are accessible
and human-readable, they are difficult to use, reference, and analyze, frequently being stored
on a server's local filesystem in an area that is generally inaccessible to the business analysts
who need these data.
Solution Overview
The solution described here assumes that each server generating events can access the Mon-
goDB instance and has read/write access to some database on that instance. Furthermore, we
assume that the query rate for logging data is significantly lower than the insert rate for log
data.
