Database Reference
In-Depth Information
solutions to so many organizations that they are now struggling to keep
up with the volume of data and the types of questions that the users and
their customer community want to ask. That represents an incredible value
proposition: Users wanting more of your solution (instead of you having to
convince users of the utility of BI and solicit customers for it).
The data solutions are giving people and organizations the flexibility to
drive types of conversations as never before. That then allows developers
to take larger volumes of data that do not fit into traditional BI systems,
like a relational database-oriented or online analytical processing (OLAP)
cube or a report, and provide a platform where users can analyze all of
that data and come up with a subset that they know has real value and
real meaning for the organization. After they have identified that subset
of information, that subset can be moved into the traditional BI (Business
Intelligence) implementation, where the enterprise can begin to consume it
and work with it as they would any other data from their sales or customer
relationship management (CRM) or reporting system.
This flexibility and agility is something not seen before, but it is similar
in some ways to the concept of
self-service BI
. Many of self-service tools
(such as Power Query and the Power BI suite from Microsoft) work well
with Hadoop and the ecosystem that it runs on. With Hive, developers have
the opportunity to quickly publish data to their end users in a relational
abstraction format; data that users can query from desktop tools designed
to help them be more exploratory in their analysis. You will learn what
exploratory data analysis
means a little bit later in this chapter. For now,
just understand that instead of sitting down in the morning and running
theirnormalMondaymorningreport,wewantuserstobeabletogofurther.
We want users to be able not just to respond to numbers they see on a
page, but to be able to make that response a deeper query and a deeper
investigation than they can get today—quickly, at their fingertips, without
having to request changes or add additional infrastructure from the IT
organization.
New Visualization Impact
Resultant from these new types of analysis, methods, and practices for
visualizing data are a number of recently published topics on best practices
for data visualization. Among the valuable material in these topics, one
good point refers to nonrelational data (such as spatial data or data coming
