Page 114 -

Chapter 3 • Data Warehousing 113

Meanwhile, Teradata began shipping commercial products to solve this prob-
lem. Wells Fargo Bank received the first Teradata test system in 1983, a parallel RDBMS
(relational database management system) for decision support—the world’s first. By 1984,
Teradata released a production version of their product, and in 1986, Fortune magazine
named Teradata Product of the Year. Teradata, still in existence today, built the first data
warehousing appliance—a combination of hardware and software to solve the data ware-
housing needs of many. Other companies began to formulate their strategies, as well.
During this decade several other events happened, collectively making it the decade
of data warehousing innovation. For instance, Ralph Kimball founded Red Brick Systems
in 1986. Red Brick began to emerge as a visionary software company by discussing how
to improve data access; in 1988, Barry Devlin and Paul Murphy of IBM Ireland introduced
the term business data warehouse as a key component of business information systems.
In the 1990s a new approach to solving the islands-of-data problem surfaced. If the
1980s approach of reaching out and accessing data directly from the files and databases
didn’t work, the 1990s philosophy involved going back to the 1970s method, in which
data from those places was copied to another location—only doing it right this time;
hence, data warehousing was born. In 1993, Bill Inmon wrote the seminal book Building
the Data Warehouse. Many people recognize Bill as the father of data warehousing.
Additional publications emerged, including the 1996 book by Ralph Kimball, The Data
Warehouse Toolkit, which discussed general-purpose dimensional design techniques to
improve the data architecture for query-centered decision support systems.
In the 2000s, in the world of data warehousing, both popularity and the amount of
data continued to grow. The vendor community and options have begun to consolidate.
In 2006, Microsoft acquired ProClarity, jumping into the data warehousing market. In
2007, Oracle purchased Hyperion, SAP acquired Business Objects, and IBM merged with
Cognos. The data warehousing leaders of the 1990s have been swallowed by some of
the largest providers of information system solutions in the world. During this time, other
innovations have emerged, including data warehouse appliances from vendors such as
Netezza (acquired by IBM), Greenplum (acquired by EMC), DATAllegro (acquired by
Microsoft), and performance management appliances that enable real-time performance
monitoring. These innovative solutions provided cost savings because they were plug-
compatible to legacy data warehouse solutions.
In the 2010s the big buzz has been Big Data. Many believe that Big Data is going to
make an impact on data warehousing as we know it. Either they will find a way to coex-
ist (which seems to be the most likely case, at least for several years) or Big Data (and
the technologies that come with it) will make traditional data warehousing obsolete. The
technologies that came with Big Data include Hadoop, MapReduce, NoSQL, Hive, and so
forth. Maybe we will see a new term coined in the world of data that combines the needs
and capabilities of traditional data warehousing and the Big Data phenomenon.

Characteristics of Data Warehousing
A common way of introducing data warehousing is to refer to its fundamental character-
istics (see Inmon, 2005):
• Subject oriented. Data are organized by detailed subject, such as sales, products, or
customers, containing only information relevant for decision support. Subject orienta-
tion enables users to determine not only how their business is performing, but why. A
data warehouse differs from an operational database in that most operational databases
have a product orientation and are tuned to handle transactions that update the data-
base. Subject orientation provides a more comprehensive view of the organization.
• Integrated. Integration is closely related to subject orientation. Data warehouses
must place data from different sources into a consistent format. To do so, they must

M03_SHAR9209_10_PIE_C03.indd 113 1/25/14 7:35 AM