Data Warehouse Concepts - Data Warehouse Systems: Design and Implementation - page 76

mart is a specialized data warehouse targeted toward a particular functional
or departmental area in an organization. A data mart can be seen as a small,
local data warehouse. Data in a data mart can be either derived from an
enterprise data warehouse or collected directly from data sources.
Another component of the data warehouse tier is the metadata repository.
Metadata can be defined as “data about data.” Metadata has been tradi-
tionally classified into technical and business metadata. Business metadata
describes the meaning (or semantics) of the data and organizational rules,
policies, and constraints related to the data. On the other hand, technical
metadata describes how data are structured and stored in a computer
system and the applications and processes that manipulate such data.
In the data warehouse context, technical metadata can be of various
natures, describing the data warehouse system, the source systems, and the
ETL process. In particular, the metadata repository may contain information
such as the following:
￿ Metadata describing the structure of the data warehouse and the data
marts, both at the conceptual/logical level (which includes the facts,
dimensions, hierarchies, derived data definitions) and at the physical level
(such as indexes, partitions, and replication). In addition, these metadata
contain security information (user authorization and access control) and
monitoring information (such as usage statistics, error reports, and audit
trails).
￿ Metadata describing the data sources, including their schemas (at the
conceptual, logical, and/or physical levels), and descriptive information
such as ownership, update frequencies, legal limitations, and access
methods.
￿ Metadata describing the ETL process, including data lineage (i.e., tracing
warehouse data back to the source data from which it was derived), data
extraction, cleaning, transformation rules and defaults, data refresh and
purging rules, and algorithms for summarization.
3.4.3 OLAP Tier
The OLAP tier in the architecture of Fig. 3.5 is composed of an OLAP
server, which presents business users with multidimensional data from data
warehouses or data marts.
Most database products provide OLAP extensions and related tools
allowing the construction and querying of cubes, as well as navigation,
analysis, and reporting. However, there is not yet a standardized language
for defining and manipulating data cubes, and the underlying technology
differs between the available systems. In this respect, several languages are
worth mentioning. XMLA (XML for Analysis) aims at providing a common