The document explains that a data warehouse is an architectural construct designed to provide historical and current decision support information through various technologies and components. It distinguishes between operational databases and data warehouses in terms of data organization, access methods, and user types, and categorizes data warehousing processes and models, including multi-dimensional data models and various schemas such as star, snowflake, and fact constellation. Additionally, it discusses performance considerations, including parallel architectures and the importance of optimizing speed and scalability.

1. Shikha Gautam
Asst.Professor

2.  The Data warehouse is an environment, not a product.
 It is an architectural construct of an information system that
provides users with current and historical decision support
information that is hard to access or present in traditional
operational data store.
 Data warehousing is a blend of technologies and components
aimed at effective integration of operation database into an
environment that enables strategic use of data.
 These technologies include relational and multi-dimensional
database management system, client/ server architecture,
meta-data modeling and repositories, graphical user interface
etc.

3. “A data warehouse is a subject-oriented, integrated, time-variant,
and nonvolatile collection of data in support of management’s
decision-making process.”—W. H. Inmon
 Data warehousing is the process of constructing and using
data warehouses.
 Focusing on the modeling and analysis of data for decision makers,
not on daily operations or transaction processing.

4.  Organized around major subjects, such as customer, product,
sales.
 Provide a simple and concise view around particular subject.

5.  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.

6.  A physically separate store of data transformed from the
operational 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

7. DBMS— tuned for OLTP: access methods, indexing,
concurrency control, recovery.
Warehouse—tuned for OLAP: complex OLAP queries,
multidimensional view, consolidation.

8. OLTP OLAP
users clerk, IT professional knowledge worker
function day to day operations decision support
DB design application-oriented subject-oriented
data current, up-to-date
detailed, flat relational
isolated
historical,
summarized, multidimensional
integrated, consolidated
usage repetitive ad-hoc
access read/write
index/hash on prim. key
lots of scans
unit of work short, simple transaction complex query
# records accessed tens millions
#users thousands hundreds
DB size 100MB-GB 100GB-TB
metric transaction throughput query throughput, response

9. Data Warehouse: A Multi-Tiered Architecture
Data
Warehouse
Extract
Transform
Load
Refresh
OLAP Engine
Analysis
Query
Reports
Data mining
Monitor
&
Integrator
Metadata
Data Sources Front-End Tools
Serve
Data Marts
Operational
DBs
Other
sources
Data Storage
OLAP Server

23.  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

24. time,item
time,item,location
time, item, location, supplier
all
time item location supplier
time,location
time,supplier
item,location
item,supplier
location,supplier
time,item,supplier
time,location,supplier
item,location,supplier
0-D(apex) cuboid
1-D cuboids
2-D cuboids
3-D cuboids
4-D(base) cuboid

25.  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.

26.  Data warehouse have different schemas as:
1. Star schema: A fact table in the middle connected
to a set of dimension tables.

27. time_key
day
day_of_the_week
month
quarter
year
time
location_key
street
city
state_or_province
country
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_type
item
branch_key
branch_name
branch_type
branch

28. 2. 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.

29. time_key
day
day_of_the_week
month
quarter
year
time
location_key
street
city_key
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_key
item
branch_key
branch_name
branch_type
branch
supplier_key
supplier_type
supplier
city_key
city
state_or_province
country
city

30. 3. Fact constellations: Multiple fact tables share dimension
tables, viewed as a collection of stars, therefore called
galaxy schema.

31. 31
time_key
day
day_of_the_week
month
quarter
year
time
location_key
street
city
province_or_state
country
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_type
item
branch_key
branch_name
branch_type
branch
Shipping Fact Table
time_key
item_key
shipper_key
from_location
to_location
dollars_cost
units_shipped
shipper_key
shipper_name
location_key
shipper_type
shipper

32.  Distributive: if the result derived by applying the function to n
aggregate values is the same as that derived by applying the
function on all the data without partitioning.
▪ E.g., count(), sum(), min(), max()
 Algebraic: if it can be computed by an algebraic function with
M arguments (where M is a bounded integer).
 E.g., avg(), min N(), standard_deviation()
 Holistic: if there is no constant bound on the storage size
needed to describe a sub-aggregate.
▪ E.g., median(), mode(), rank()

33. There are following seven components of a Data
Warehouse:
 Data Warehouse Database
 Sourcing, Acquisition, Cleanup andTransformation
Tools
 Meta Data
 Access (Query)Tools :The query tool allows executives
and other users real-time access to the DataWarehouse
database for query generation, result displays, reports
and data exports.
 Data Marts
 Data Warehouse Administration and Management
 Information Delivery System

34. 1. Business Considerations- Identifying and establishing
information requirements.
2. Design Considerations- Data Content, Metadata, Data
Distribution, Tools, Performance consideration.
3. Technical Considerations- Hardware Platform, DBMS that
supports the warehouse, Communication infrastructure,
hardware platform and software to support the metadata
repository, Systems management framework.
4. Implementation Considerations- Access Tools, Data
Extraction, Cleanup, Transformation, and Migration etc.

35. The size of a data warehouse rapidly approaches to the
search for better performance. This search aims to pursue
two goals:
 Speed-up: the ability to execute the same request on
the same amount data in less time.
 Scale-up: the ability to obtain the same performance
on the same request as the database size increases.
Doubling the number of processors cuts the response
time in half (linear speed-up) or provides the same
performance on twice as much data (linear scale-up).

36.  The goals of linear performance and scalability can be
satisfied by parallel hardware architectures, parallel
operating systems, and parallel DBMSs.
 Parallel hardware architectures are based on Multi-
processor systems designed as a Shared-memory model
(symmetric multiprocessors), Shared-disk model or
distributed-memory model (MPP and Clusters of SMPs).
Parallelism can be achieved in two different ways:
1. Horizontal Parallelism (Database is partitioned across
different disks)
2. Vertical Parallelism (occurs among different tasks – all
components query operations i.e. scans, join, sort)
3. Data Partitioning.

37.  Shared memory -- processors share a
common memory.
 Shared disk -- processors share a common
disk.
 Shared nothing -- processors share neither a
common memory nor common disk.
 Hierarchical -- hybrid of the above
architectures.Also known as Combined
Architecture .

38.  ORACLE – Oracle supports Parallel Database processing with
its add-on.
 Informix –Support Shared-Memory, Shared-Disk, and
Shared-Nothing Models.
 IBM – DB2 Parallel Edition (DB2 PE), DB2 Universal
Database.
 Sybase – Sybase implemented its parallel DBMS functionality in
a product called SYBASE MPP.
 Other RDBMS Products : i. NCR Teradata ii. Tandem NonStop
SQL/MP.