Understanding Data Warehouses: Models, Design, and Usage
A decision support database that is maintained separately from the organization’s operational database
Support information processing by providing a solid platform of consolidated, historical data for analysis.
“A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process
Subject-Oriented
Organized around major subjects, such as customer,
product, sales
Focusing on the modeling and analysis of data for
decision makers, not on daily operations or transaction
processing
Provide a simple and concise view around particular
subject issues by excluding data that are not useful in
the decision support process
Integrated
Constructed by integrating multiple, heterogeneous data
sources
relational databases, flat files, on-line transaction
records
Data cleaning and data integration techniques are
applied.
Ensure consistency in naming conventions, encoding
structures, attribute measures, etc. among different
data sources
E.g., Hotel price: currency, tax, breakfast covered, etc.
When data is moved to the warehouse, it is
converted.
Time Variant
The time horizon for the data warehouse is significantly
longer than that of operational systems
Operational database: current value data
Data warehouse data: provide information from a
historical perspective (e.g., past 5-10 years)
Every key structure in the data warehouse
Contains an element of time, explicitly or implicitly
But the key of operational data may or may not
contain “time element
Nonvolatile
A physically separate store of data transformed from the
operational environment
Operational update of data does not occur in the data
warehouse environment
Does not require transaction processing, recovery,
and concurrency control mechanisms
Requires only two operations in data accessing:
initial loading of data and access of data
Data Warehouse vs. Heterogeneous DBMS
Traditional heterogeneous DB integration: A query driven approach
Build wrappers/mediators on top of heterogeneous databases
When a query is posed to a client site, a meta-dictionary is used
to translate the query into queries appropriate for individual
heterogeneous sites involved, and the results are integrated into
a global answer set
Complex information filtering, compete for resources
Data warehouse: update-driven, high performance
Information from heterogeneous sources is integrated in advance
and stored in warehouses for direct query and analysis
Why a Separate Data Warehouse?
High performance for both systems
DBMS— tuned for OLTP: access methods, indexing, concurrency
control, recovery
Warehouse—tuned for OLAP: complex OLAP queries,
multidimensional view, consolidation
Different functions and different data:
missing data: Decision support requires historical data which
operational DBs do not typically maintain
data consolidation: DS requires consolidation (aggregation,
summarization) of data from heterogeneous sources
data quality: different sources typically use inconsistent data
representations, codes and formats which have to be reconciled
Note: There are more and more systems which perform OLAP
analysis directly on relational databases
Three Data Warehouse Models
Enterprise warehouse
collects all of the information about subjects spanning
the entire organization
Data Mart
a subset of corporate-wide data that is of value to a
specific groups of users. Its scope is confined to
specific, selected groups, such as marketing data mart
Independent vs. dependent (directly from warehouse) data mart
Virtual warehouse
A set of views over operational databases
Only some of the possible summary views may be
materialized
Extraction, Transformation, and Loading (ETL)
Data extraction
get data from multiple, heterogeneous, and external
sources
Data cleaning
detect errors in the data and rectify them when possible
Data transformation
convert data from legacy or host format to warehouse
format
Load
sort, summarize, consolidate, compute views, check
integrity, and build indicies and partitions
Refresh
propagate the updates from the data sources to the
warehouse
Metadata Repository
Meta data is the data defining warehouse objects. It stores:
Description of the structure of the data warehouse
schema, view, dimensions, hierarchies, derived data defn, data
mart locations and contents
Operational meta-data
data lineage (history of migrated data and transformation path),
currency of data (active, archived, or purged), monitoring
information (warehouse usage statistics, error reports, audit trails)
The algorithms used for summarization
The mapping from operational environment to the data warehouse
Data related to system performance
warehouse schema, view and derived data definitions
Business data
business terms and definitions, ownership of data, charging policies
Conceptual Modeling of Data Warehouses
Modeling data warehouses: dimensions & measures
Star schema: A fact table in the middle connected to a
set of dimension tables
Snowflake schema: A refinement of star schema
where some dimensional hierarchy is normalized into a
set of smaller dimension tables, forming a shape
similar to snowflake
Fact constellations: Multiple fact tables share
dimension tables, viewed as a collection of stars,
therefore called galaxy schema or fact constellation
Typical OLAP Operations
Roll up (drill-up): summarize data
by climbing up hierarchy or by dimension reduction
Drill down (roll down): reverse of roll-up
from higher level summary to lower level summary or
detailed data, or introducing new dimensions
Slice and dice: project and select
Pivot (rotate):
reorient the cube, visualization, 3D to series of 2D planes
Other operations
drill across: involving (across) more than one fact table
drill through: through the bottom level of the cube to its
back-end relational tables (using SQL)
Design of Data Warehouse: A Business
Analysis Framework
Four views regarding the design of a data warehouse
Top-down view
allows selection of the relevant information necessary for the
data warehouse
Data source view
exposes the information being captured, stored, and
managed by operational systems
Data warehouse view
consists of fact tables and dimension tables
Business query view
sees the perspectives of data in the warehouse from the view
of end-user
Data Warehouse Design Process
Top-down, bottom-up approaches or a combination of both
Top-down: Starts with overall design and planning (mature)
Bottom-up: Starts with experiments and prototypes (rapid)
From software engineering point of view
Waterfall: structured and systematic analysis at each step before
proceeding to the next
Spiral: rapid generation of increasingly functional systems, short
turn around time, quick turn around
Typical data warehouse design process
Choose a business process to model, e.g., orders, invoices, etc.
Choose the grain (atomic level of data) of the business process
Choose the dimensions that will apply to each fact table record
Choose the measure that will populate each fact table record
Data Warehouse Usage
Three kinds of data warehouse applications
Information processing
supports querying, basic statistical analysis, and reporting
using crosstabs, tables, charts and graphs
Analytical processing
multidimensional analysis of data warehouse data
supports basic OLAP operations, slice-dice, drilling, pivoting
Data mining
knowledge discovery from hidden patterns
supports associations, constructing analytical models,
performing classification and prediction, and presenting the
mining results using visualization tools
OLAP Server Architectures
Relational OLAP (ROLAP)
Use relational or extended-relational DBMS to store and manage
warehouse data and OLAP middle ware
Include optimization of DBMS backend, implementation of
aggregation navigation logic, and additional tools and services
Greater scalability
Multidimensional OLAP (MOLAP)
Sparse array-based multidimensional storage engine
Fast indexing to pre-computed summarized data
Hybrid OLAP (HOLAP) (e.g., Microsoft SQLServer)
Flexibility, e.g., low level: relational, high-level: array
Specialized SQL servers (e.g., Redbricks)
Specialized support for SQL queries over star/snowflake schemas
From Tables and Spreadsheets to
Data Cubes
A data warehouse is based on a multidimensional data model
which views data in the form of a data cube
A data cube, such as sales, allows data to be modeled and viewed in
multiple dimensions
Dimension tables, such as item (item_name, brand, type), or
time(day, week, month, quarter, year)
Fact table contains measures (such as dollars_sold) and keys
to each of the related dimension tables
In data warehousing literature, an n-D base cube is called a base
cuboid. The top most 0-D cuboid, which holds the highest-level of
summarization, is called the apex cuboid. The lattice of cuboids
forms a data cube.