Databases Reference
In-Depth Information
6.4
Understanding the types of big data problems
There are many types of big data problems, each requiring a different combination of
NoSQL systems. After you've categorized your data and determined its type, you'll
find there are different solutions. How you build your own big data classification sys-
tem might be different from this example, but the process of differentiating data types
should be similar.
Figure 6.5 is a good example of a high-level big data classification system.
Big data
Read-mostly
Read-write
Image
Event-log
Documents
Graph
High availability
Transactions
Real-time
Batch
Full-text
Clickstream
Operational
Simple text
Annotations
Figure 6.5
A sample of a taxonomy of big data types. This chapter deals with read-mostly
problems. Chapter 8 has a focus on read/write big data problems that need high availability.
Let's take a look at some ways you classify big data problems and see how NoSQL sys-
tems are changing the way organizations use data.
Read-mostly
—Read-mostly data is the most common classification. It includes
data that's created once and rarely altered. This type of data is typically found in
data warehouse applications but is also identified as a set of non-
RDBMS
items
like images or video, event-logging data, published documents, or graph data.
Event data includes things like retail sales events, hits on a website, system log-
ging data, or real-time sensor data.
Log events
—When operational events occur in your enterprise, you can record it
in a log file and include a timestamp so you know when the event occurred. Log
events may be a web page click or an out-of-memory warning on a disk drive. In
the past, the cost and amount of event data produced were so large that many
organizations opted not to gather or analyze it. Today, NoSQL systems are
changing companies' thoughts on the value of log data as the cost to store and
analyze it is more affordable.
The ability to cost-effectively gather and store log events from all computers
in your enterprise has lead to
BI
operational intelligence
systems. Operational
intelligence goes beyond analyzing trends in your web traffic or retail transac-
tions. It can integrate information from network monitoring systems so you can
