Foundational data architecture patterns - Making Sense of NoSQL - page 56

inventory specialists use these tools every day to track retail trends and make adjust-
ments to their inventory and deliveries. Due to the popularity of OLAP systems and
their empowerment of nonprogammers to create ad hoc queries, the probability is
low that the fundamental structures of OLAP and data warehouses systems will be
quickly displaced by NoSQL solutions. What will change is how tools such as Map-
Reduce will be used to create the aggregates used by the cubes. To be effective, OLAP
systems need tools that efficiently create the precomputed sums and totals. In a later
chapter, we'll talk about how NoSQL components are appropriate for performing
analysis on large datasets.
In the past 10 years, the use of open source OLAP tools such as Mondrian and Pen-
taho has allowed organizations to dramatically cut their data warehouse costs. In
order to be a viable ad hoc analysis tool, NoSQL systems must be as low-cost and as
easy to use as these systems. They must have the performance and scalability benefits
that current systems lack, and they must have the tools and interfaces that facilitate
integration with existing OLAP systems.
Despite the fact that OLAP systems have now become commodity products, the cost
of setting up and maintaining OLAP systems can still be a hefty part of an organiza-
tion's IT budget. The ETL tools to move data between operational and analytical sys-
tems still usually run on single processors, perform costly join operations, and limit
the amount of data that can be moved each night between the operational and analyt-
ical systems. These challenges and costs are even greater when organizations lack
strong data governance policies or have inconsistent category definitions. Though not
necessarily data architecture issues, they fall under enterprise semantics and standards
concerns, and should be taken to heart in both RDBMS and NoSQL solutions.
Standards watch: standards for OLAP
Several XML standards are associated with OLAP systems that promote portability of
your MDX applications between OLAP systems. These standards include XML for
Analysis (XMLA) and the Common Warehouse Metamodel (CWM) .
The XMLA standard is an XML wrapper standard for exchanging MDX statements
between various OLAP servers and clients. XMLA systems allow users to use many
different MDX clients such as JPivot against many different OLAP servers.
CWM is an XML standard for describing all components you might find in an OLAP
system including cubes, dimensions, measures, tables, and aggregates. CWM sys-
tems allow you to define your OLAP cubes in terms of a standardized and portable
XML file so that your cube definition can be exchanged between multiple systems.
In general, commercial vendors make it easy to import CWM data, but frequently
make it difficult to export this data. This makes it easy to start to use their products
but difficult to leave them. Third-party vendor products are frequently needed to pro-
vide high-quality translation from one system to another.