What’s New in SSAS
SQL Server 2005 was the big
jump into completely redeploying Analysis Services—from the
architecture, to the development environment, to the multidimensional
languages supported, and even to the wizard-driven deployments. SQL
Server 2008 R2 raises this core work up a few more
notches with enhancements at almost every part of SSAS and with the
addition of major scaleout capabilities. Following are some of the top
new features and enhancements:
Microsoft has improved and streamlined the Cube Designer.
Several subtle enhancements have been made around the Dimension and Aggregation Designers.
You can now create attribute relationships with the new Attribute Relationship Designer.
You can use subspace computations to optimize performance for your Multidimensional Expressions (MDX) queries.
Multidimensional OLAP (MOLAP) enables write-back capabilities that support high-performance “what if” scenarios.
A
shared read-only Analysis Services database between several Analysis
Services servers enables you to “scale out” easily and efficiently.
You
are able to use localized analytical data in native languages,
including translation capabilities and automatic currency conversions.
A highly compressed and optimized data cache is maintained automatically.
Backup performance is optimized.
SQL Server PowerPivot for Excel is a new feature.
The master data hub in SQL Server 2008 R2 helps manage your master data services more efficiently.
And, last, but not least,
Understanding SSAS and OLAP
Because OLAP is at the
heart of SSAS, you need to understand what it is and how it solves the
requirements of decision makers in a business. As you might already
know, data warehousing requirements typically include all the
capability needed to report on a business’s transactional history, such
as sales history. This transactional history is often organized into
subject areas and tiers of aggregated information that can support some
online querying and usually much more batch reporting. Data warehouses
and data marts typically extract data from online transaction
processing (OLTP) systems and serve data up to these business users and
reporting systems. In general, these are all called decision support
systems (DSS), or BI systems, and the latency of this data is
determined by the business requirements it must support. Typically,
this latency is daily or weekly, depending on the business needs, but
more and more, we are seeing more real-time (or near-real-time)
reporting requirements.
OLAP falls squarely into the
realm of BI. The purpose of OLAP is to provide for a mostly online
reporting environment that can support various end user reporting
requirements. Typically, OLAP representations are of OLAP cubes. A cube
is a multidimensional representation of basic business facts that can
be accessed easily and quickly to provide you with the specific
information you need to make a critical decision. It is useful to note
that a cube can be composed of from 1 to N
dimensions. However, remember that the business facts represented in a
cube must exist for all the dimensions being defined for the fact. In
other words, all dimensional values (that is, intersections) have to be
present for a fact value to be stored in the cube.
Figure 1 illustrates the Sales_Units
historical business fact, which is the intersection of time, product,
and geography dimensional data. For a particular point in time
(February 2010), for a particular product (IBM laptop model 451D), and
in a particular country (France), the sales units were 996 units. With
an OLAP cube, you can easily see how many of these laptop computers
were sold in France in February 2010.
Basically, cubes enable you
to look at business facts via well-defined and organized dimensions
(time, product, and geography dimensions, in this example). Note that
each of these dimensions is further organized into hierarchical
representations that correspond to the way data is looked at from the
business point of view. This provides for the capability to drill down
into the next level from a higher, broader level (like drilling down
into a specific country’s data within a geographic region, such as
France’s data within the European geographic region).
SSAS
directly supports this and other data warehousing capabilities. In
addition, SSAS allows a designer to implement OLAP cubes using a
variety of physical storage techniques that are directly tied to data
aggregation requirements and other performance considerations. You can
easily access any OLAP cube built with SSAS via the Pivot Table
Service, you can write custom client applications by using MDX with OLE
DB for OLAP or ActiveX Data Objects Multidimensional (ADO MD), and you
can use a number of third-party “OLE DB for OLAP” compliant tools.
Microsoft utilizes
something called the Unified Dimensional Model (UDM) to conceptualize
all multidimensional representations in SSAS. It is also worth noting
that many of the leading OLAP and statistical analysis software vendors
have joined the Microsoft Data Warehousing Alliance and are building
front-end analysis and presentation tools for SSAS. The data mining
capabilities that are part of SSAS provide a new avenue for organized
data discovery. This includes using SQL Server DMX.
