Architecture and Infrastructure
Module 2
G.Anuradha
What is architecture?
• The structure that brings all the components
of a data warehouse together is known as the
architecture.
• Many factors affect the architecture of a DW
– Integrated data
– Data preparation and storing
– Data delivery
– Technology
• Comprehensive blueprint
Architecture in 3 major areas
• Data acquisition
• Data storage
• Information delivery
Distinguishing characteristics of
architecture
• Different Objectives and Scope
– For providing strategic information DW should have elaborate
architecture
– Scope depends on the sources used in the acquisition region
• Data Content
– Dealing with historical, read only data
• Complex Analysis and Quick Response
– Drill down, roll up, slice, dice, what if scenarios
• Flexible and Dynamic
– Design should be dynamic after designing as well
• Metadata-driven
– Every movement is trapped in it.
Test your
fundas
ACROSS
1. Business dimension(5)
6. Smaller than DW(8)
7. Combining data from different
operational systems(10)
8. Initial loading(7)
DOWN
2. Remove useful information
from operational data(10)
3. Monitoring the entire
function (10)
4. Historical(8)
5. Data about entire
warehouse(8)
Solution
Architecture supporting the flow of
data
Data Source
(internal & External)
Metadata
Storage mechanism
for data about data
Data Staging
Transformation
Cleansing
Integration of Data
Data Storage
Loading of data from Staging
Area
Storing for Information
Delivery
Information Delivery
Dependent data marts, MDDBs, Query and
reporting facilities
Management and control module
• Umbrella component having two important
functions
– Monitor all ongoing operations
– Problem recovery
List of services and functions-Data
Extraction
• Select data sources and determine the types of filters to be applied
to individual sources
• Generate automatic extract files from operational systems using
replication and other techniques
• Create intermediary files to store selected data to be merged later
• Transport extracted files from multiple platforms
• Provide automated job control services for creating extract files
• Reformat input from outside sources
• Reformat input from departmental data files, databases, and
spreadsheets
• Generate common application code for data extraction
• Resolve inconsistencies for common data elements from multiple
sources
List of services and functions-Data
Transformation
• Map input data to data for data warehouse repository
• Clean data, deduplicate, and merge/purge
• Denormalize extracted data structures as required by
the dimensional model of the data warehouse
• Convert data types
• Calculate and derive attribute values
• Check for referential integrity
• Aggregate data as needed
• Resolve missing values
• Consolidate and integrate data
List of functions and services-Data
staging
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Provide backup and recovery for staging area repositories
Sort and merge files
Create files as input to make changes to dimension tables
If data staging storage is a relational database, create and
populate database
Preserve audit trail to relate each data item in the data
warehouse to input source
Resolve and create primary and foreign keys for load tables
Consolidate datasets and create flat files for loading
through DBMS utilities
If staging area storage is a relational database, extract load
files
Data Storage
• loading the data from the staging area into the
data warehouse repository
• before loading data into the data ware the
metadata repository gets populated
• For top-bottom approach there could be
movements of data from the enterprise-wide
data warehouse repository to the repositories of
the dependent data marts
• For bottom-up approach data movements stop
with the appropriate conformed data marts
Information Delivery
• Information access in a data warehouse is
through online queries and interactive analysis
sessions
• data warehouse will also be producing regular
and ad hoc reports.
• data warehouse feeds data to proprietary
multidimensional databases (MDDBs) where
summarized data is kept as multidimensional
cubes of information
Data stores for information delivery
Function and services
• Provide security to control information access and monitor user access
• Allow users to browse data warehouse content by hiding internal
complexities
• Automatically reformat queries for optimal execution, from aggregate
tables as well
• Provide self-service report generation for users, consisting of a variety of
flexible options to create, schedule, and run reports
• Store result sets of queries and reports for future use
• Provide multiple levels of data granularity
• Provide event triggers to monitor data loading
• Make provision for the users to perform complex analysis through OLAP
• Enable data feeds to downstream, specialized decisions support systems
such as EIS and data mining
Summing up……
• Architecture is the structure that brings all the
components together.
• The architectural components support the
functioning of the data warehouse in the
three major areas of data acquisition, data
storage, and information delivery.
Infrastructure of DW
G.Anuradha
Infrastructure
 Elements that enable the architecture to be
implemented.
 Operational – help to keep the DW going
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People
Procedures
Training
Management software
 Physical
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Hardware components
Operating system
Network, network software
Features of Hardware & OS
 Hardware
 Scalability
 Vendor support
 Vendor stability
 OS
 Scalability
 Security
 Reliability
 Availability
 Preemptive multitasking
 Memory protection
Possible options
 Mainframes
 Old hardware
 Designed for OLTP
 Expensive
 Not easily scalable
 Open System Servers
 UNIX servers are most opted
 Robust
 Adapted for parallel processing
 NT Servers
 Medium-sized data warehouses
 Limited parallel processing
 Cost effective for small or medium DW
Platform Options
 A computing platform is the set hardware
components, operating system, network & network
software.
 Both Online Transaction Processing and Decision
Support Systems need a computing platform.
Single Platform Option
 All functions from back-end data extraction to front-
end query processing is performed on one platform.
 Data flows smoothly, no conversions required
 No middleware required
Limitations
 Legacy platform stretched to capacity
 Non-availability of tools
 Multiple legacy platforms
 Company’s migration policy
Hybrid Platform Option
 Eliminate s the drawbacks of single platform option
 Data extraction: Each source is extracted on its own
computing platform
 Initial reformatting & merging: The extracted file from
each source is reformatted & merged, on their respective
platforms
 Preliminary data cleansing: Verify extracted data for
missing values & data types.
 Transformation & Consolidation: Performed on the
platform where the staging area resides.
 Validation & Final Quality Check
 Creation of Load Images
Options for staging area
 Legacy platforms – when all data sources are on the
same platform, we can create a DW also on the same
 Data storage platform – the warehouse DBMS runs
here. This can be used for staging also.
 Separate optimal platform – a separate platform for
staging data
Server Hardware
 Server hardware is most important
 Scalability
 Query processing
Data movement options
Client/Server architecture for DW
Considerations on client
workstations
 Depends on type of users
 casual user-Web browser and HTML reports
 Analyst-more powerful workstation machine
 Practically feasible solution is a minimum
configuration on an appropriate platform that would
support a standard set of information delivery tools in
DW
Platform options as DW matures
Parallel processing
 Symmetric multiprocessing
 Clusters
 Massively parallel processing
 Cache-coherent Nonuniform Memory Architecture
Symmetric Multiprocessing
Clusters
Massively Parallel Processing
NUMA or ccNUMA
Database Software
 Many operations can be parallelized
 mass loading of data, full table scans, queries with
exclusion conditions, queries with grouping, selection
with distinct values, aggregation, sorting, creation of
tables using subqueries, creating and rebuilding
indexes, inserting rows into a table from other tables,
enabling constraints, star transformation
Types of parallelization
Software Tools
Summing up
 Infrastructure acts as the foundation supporting the
data warehouse architecture
 Data warehouse infrastructure consists of operational
infrastructure and physical infrastructure.
 Hardware and operating systems make up the
computing environment for the DW.
 Several options exist for the computing platforms
needed to implement the various architectural
components.
Summing up
 Selecting the server hardware is a key decision.
Invariably, the choice is one of the four parallel server
architectures.
 Current database software products are able to
perform interquery and intraquery parallelization.
 Software tools are used in the data warehouse for data
modeling, data extraction, data transformation, data
loading, data quality assurance, queries and reports,
and online analytical processing (OLAP).
 Tools are also used as middleware, alert systems,
 and for data warehouse administration.
METADATA
 Data dictionary or data catalog
 Contains data about the data in the DW like
 data structures
 files and addresses
 indexes
 Types of Metadata
 Operational
 Extraction & Transformational
 End-User
Need for a Metadata
 For using the DW
 For building the DW
 For administering the DW
 Automation of the DW
Metadata by functional areas
 Every DW process occurs in one of these 3 areas
 Data acquisition
 Data storage
 Information delivery
Data acquisition - metadata
Information Delivery – metadata
Types of Metadata
 Business metadata
 Portrays DW from the end user perspective
 Shows business names, not actual file names
 Less structured as compared to technical metadata
 Used by business analysts and other end users.
 Technical metadata
 Shows the actual structure and content of the DW
 Acts as a guide to build, maintain and administer the
DW
 Used the the data warehouse administrator, and other IT
staff working on the DW.
How to provide metadata
 Metadata requirements
 Sources
 Challenges
 Repository
 Integration and standards
 Implementation options