Data warehouse Concepts and Basics

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Data warehouse Concepts and Basics

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Session objectives and scope

• Definition
• Fundamentals of data warehousing, how it works
  and what it serves
• Basic Elements of Data warehouse and generic
  architecture
• Models Star Schema, Snowflake Schema
• OLTP v/s OLAP
• DW v/s Data Mart
• What is ETL
• ETL Processes
• OLAP different types

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Ground rules

• Interactive
• Participation amongst all
• Please set your cell phones to vibration mode

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Data warehouse the definition

• A warehouse is place where goods are physically
  stocked, to facilitate smooth flow of business
  without any production downtime or crisis.

• In laymans word
• A data warehouse is read only database which
  copies/stores the data from the transactional
  database.

• Ralph Kimball
• A data warehouse is a structured extensible
  environment designed for the analysis of
  non-volatile data, logically and physically
  transformed from multiple source applications to
  align with business structure, updated and
  maintained for a long time period, expressed in
  simple business terms, and summarized for quick
  analysis.

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What is a data warehouse?

• A warehouse is a subject-oriented, integrated,
  time-variant and non-volatile collection of data
  in support of managements decision making
  process.
• English translation
• Subject-oriented data refers to data that
  provides information about a particular subject
  (e.g. customers, transactions, products, vendors,
  etc.) rather than a companys ongoing operations.
• Integrated data means that data is gathered from
  a variety of different sources, linked together,
  and then aggregated in one location (i.e. the
  warehouse) to establish a comprehensive dataset.
• Time-variant signifies that the data elements
  vary over time. For example, customer data in a
  warehouse should be updated as changes occur to
  customer accounts.
• Non-volatile implies that the data in the
  warehouse is stable. That is, new data is
  added to the warehouse but old data isnt
  removed. Maintaining old data provides managers
  with a consistent picture of the business as well
  as a historical perspective for analyzing trends.
  (Note Some data warehouses can be volatile.
  Based on storage constraints, only a certain
  number of periods of history are kept in the
  warehouse.)
• Data warehouse versus data mart?
• Data mart refers to a single-subject data
  warehouse.
• Data warehouses are typically enterprise in scope.

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Need of data warehouse

• Analysis of data

• Flexibility and broad scope

• Faster retrieval of data

• Subject-oriented information capture

• Future projections and top-level control

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Some other terms used in the Data Warehousing
world

• ODS (Operational Data Stores)
• An operational data store (ODS) is a type
  of database often used as an interim area for a
  data warehouse. Unlike a data warehouse, which
  contains static data, the contents of the ODS are
  updated through the course of business
  operations. An ODS is designed to quickly perform
  relatively simple queries on small amounts of
  data (such as finding the status of a customer
  order), rather than the complex queries on large
  amounts of data typical of the data warehouse. An
  ODS is similar to your short term memory in that
  it stores only very recent information in
  comparison, the data warehouse is more like long
  term memory in that it stores relatively
  permanent information.
• Data Warehouse
• A data warehouse is a central repository
  for all or significant parts of the data that an
  enterprise's various business systems collect.
  Typically, a data warehouse is housed on an
  enterprise mainframe server. Data from various
  online transaction processing (OLTP) applications
  and other sources is selectively extracted and
  organized on the data warehouse database for use
  by analytical applications and user queries. Data
  warehousing emphasizes the capture of data from
  diverse sources for useful analysis and access.
• Data Marts
• A data mart is a repository of data
  gathered from operational data and other sources
  that is designed to serve a particular community
  of knowledge workers. In scope, the data may
  derive from an enterprise-wide database or data
  warehouse or be more specialized. The emphasis of
  a data mart is on meeting the specific demands of
  a particular group of knowledge users in terms of
  analysis, content, presentation, and ease-of-use.
  Users of a data mart can expect to have data
  presented in terms that are familiar.

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Where does the data come from? Where does it
go?

• Data is selectively extracted from online
  transaction processing (OLTP) applications and
  various other sources and consolidated, cleansed,
  organized in a separate database which then
  becomes the warehouse.
• A data warehouse is typically located on an
  server different from the one that houses most
  enterprise-wide applications (e.g. payroll, email
  server, customer data portals, etc.)

Data Warehousing Lifecycle
Fragmented data sources (Metadata)
Extract, transform, load
Consolidate (Warehouse)
Export (Marts)
Assign Semantics
Access Analyze
Staging
Cleansing
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Hence..

• Data warehouse is a concept/architecture, not a
  product

• Set of hardware and software components used to
  analyze huge amount of data

• Intelligent way of managing data

• Data -gt Information -gt Knowledge -gt Decision -gt
  Control

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Step-wise break-up of warehousing activity
Source systems
Flow of information
Data warehouse storage
End user views
OLAP Server
Direction of analysis
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Generic Architecture
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Source Systems

• An operational system of record whose function is
  basically to capture the transactions of the
  business.
• A source system can be a legacy system in a
  mainframe environment or can be flat files or
  can also be from multiple RDBMS systems.
• The main priorities of the source system are
  uptime and availability.
• Source systems are not queried in the broad and
  unexpected ways as compared to that of the data
  warehouses which are typically queried.

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Data staging area

• A storage area and set of processes that clean,
  transform, combine, duplicate, archive, and
  prepare source data for use in the data
  warehouse.
• The data staging area is everything in between
  the source system and the presentation server.
• The staging area is dominated by the simple
  activities of sorting and sequential processing.
• The data staging area does not need to be based
  on relational technology.
• Data staging area does not provide query and
  presentation services.

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Semantic Layer illustration

• The semantic layer defines a set of logical
  relationships which translate complex data
  terminology into business terms that will be
  familiar to end-users (e.g. product, customer,
  revenue, etc.)
• For example, if a user wants to create a report
  showing product revenue generated from all
  customers for the past three years, the user
  simply chooses four objects customer, product,
  revenue and time.
• Thanks to the semantic layer, the user only has
  to be familiar with the business terminology
  relevant for his/her query. (There is no need to
  understand the back-end database structure.)

Semantic Layer acting as primary interface to DB
tables. Hiding the underlying data complexity
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Presentation layer illustration

• The presentation layer enables users to customize
  the way data (and data analysis) is presented to
  them.
• Through the presentation layer, users can create
  custom reports, access standard reports and
  access dashboards with drill-down capabilities
  as well as alerting functions.

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Summary of different layers

• The target physical machine on which the data
  warehouse data is organized and stored for direct
  querying by end users, report writers, and other
  applications.
• To Summarize, three very different systems are
  required for a data warehouse to function
• the source system
• the data staging area
• and the presentation server.
• The source system should be outside the DW.
• The data staging area is described as the initial
  storage and cleaning system for DW.
• The presentation server presents and stores in a
  dimensional framework.

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What is the process for developing a data
warehouse?
What are the goals for the data warehouse? (e.g.
improved customer insights)
Data should be cleaned to the extent possible.
Design the schemas, make pre-built total tables,
create semantic layer design, establish meta data
design, etc.
Include a business intelligence solution to
enable business user interaction.
This is a critical step because business users
often do not understand their requirements until
they interact with the system.
Incorporating end-user feedback ensures a robust
design that satisfies business requirements.
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What are the business benefits associated with
data warehousing?
Qualitative Benefits
Quantitative Benefits

• Data analysis
• Integrated data provides complete picture of
  enterprise
• Time-variant data enables easier trend analysis
• Subject-oriented data allows decision makers who
  can refine their queries
• Custom views of data at different levels of
  aggregation and along different dimensions
• Reduced complexity of user queries through
  semantics layer.
• Data quality
• Data integration provides single version of the
  truth
• Proactive data cleansing improves data quality
• Creation of a corporate data dictionary
  resolves differences in terminology
• Redirected resources
• Frees up IT resources from report creation
• Reduces traffic on On-Line Transaction Processing
  (OLTP) systems

• Process-specific cost reduction reduces costs
  incurred due to lack of information or wrong
  information. For example
• Lack of integrated data available for forecasting
  can lead to carrying too much/little inventory
• Lack of timely information on defaults in credit
  cards can cause increased bad debts on other
  loans/debt
• Process-specific growth opportunity reduces
  missed growth opportunities due to lack of
  information. For example
• Lack of integrated view of customer buying
  behavior impeded cross selling
• Infrastructure cost reduction
• Automation of existing manual process reduces IT
  involvement and monitoring.
• Reduction in data marts and associated hardware
  when implementing an enterprise data warehouse
• Rationalization of disparate sets of tools used
  for reporting

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Data warehousing notions and reality
Notion
Reality Check
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Data Warehouse End-to-End Development Process
Discover
Architect
Construct
Validate
Deploy
Production Support

• Study Business Drivers/validate vision phase
• Understand the vision for analytics
• Drill down to business capabilities, with focus
  on a particular analytics (could be sales
  analytics, marketing analytics and so on)
• Prioritize overall enterprise analytics needs and
  understand at high level
• Study As Is Analytics landscape phase
• Current BI capabilities, tools, technologies
• Determine root cause for existing problems
• Study environment complexity Data sources,
  diversity, data quality issues, technical
  infrastructure
• Current BI governance set up
• Define robust BI Solution Framework phase
• Scaleable data architecture to provide data
  backbone for all analytics needs
• Optimal approach/methodology for iterative build
• Reporting/BI strategy that meets canned, ad hoc
  power user needs for analytics
• High level roadmap with key milestones for
  realizing the enterprise information
  architecture.

• Data Model Design
• - Relational
• - Multi-Dimensional
• Capacity Planning
• Performance profiling
• Detailed Architecture definition
• Prototype

• Database creation
• Unit testing and UAT plans
• Create Manuals
• -Operation Manual
• -System Manual
• -Installation Notes
• -User Manual

• Prepare for System Testing
• Functional Test Plan
• Performance Test Plan
• Data Quality Test Plan
• Execute System Test
• Functional Test Plan
• Performance Test Plan
• Data Quality Test Plan
• Performance Tuning
• Extracts, Loads
• Quality Check
• Transformation
• Aggregation
• Caching of Reports
• Design usage profiling
• User Training

Prepare deployment/ Roll out plan Migrate to
production environment Set up end user desktop
environment Set up Help Desk Roll Out
solution

• Batch operations
• Periodic Run of operational Batch
• Error logging, reporting and rectification
• Minor Enhancements

• Design
• Dimensions and
• Measures
• Aggregation/Analysis
• Distribution/Security
• Operational Batch

• Configure
• BI tool Meta Data
• Users/access Rights
• Report Distribution
• Operational Batch
• Program Aggregation/
• Analysis

• Design
• Extract/ Data Quality
• Data Transformation
• Operational Batch

• Configure ETL tool
• Metadata
• Program ETL
• Configure Operational
• Batch

• Design of Metadata
• Integration of Metadata
• Publish Data Dictionary

• Metadata
• Integrate Metadata
• Publish Metadata

• Design
• Partitions, Indexes
• Execution plans .

Optimize the warehouse database, ETL scripts

• custom components
• Extracts, data Quality
• Transformations
• Aggregation, Analysis

• Construct Custom Components
• Extracts, Transformations
• Quality Checks
• Aggregation, Analysis

-Discover phase and Architect phase can be
treated as Requirement Analysis and Design phase
respectively -Construct phase refers to the Build
phase
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Architecture Components Providers
Component Providers
Source Systems - Operational or legacy systems of record whose function it is to capture the transactions of the business. The data in these systems can be in many formats from flat files to hierarchical, and relational databases. Infinite possibilities
Data Warehouse Database - The data warehouse database collects and stores integrated sets of historical, non-volatile data from multiple operational systems and feeds them to one or more data marts. It becomes the one source of the truth for all shared data. Teradata SQL Server Netezza Oracle etc
Data Marts - All data marts extract data from the data warehouse. The data marts contain subject specific information supporting the requirements of the end users in individual business units. Data marts can provide rapid response to end-user requests using aggregates stored in the data mart. SQL Server Oracle
Extract Transform Load (ETL) - ETL tools are used to extract data from data sources, cleanse the data, perform data transformations, and load the target data warehouse. The ETL tool is also used to load the data marts. The ETL tool maintains a metadata repository of all source to target transformations, this information is available to end users for research and problem resolution. Informatica SAS Hummingbird etc.
Metadata Repository - Offers a way to understand what information is available, where it comes from, where it is stored, the transformation performed on the data. Technical metadata can be generated and maintained by an ETL tool as part of the specification of the extraction, transformation and load process. The repository can also capture the operational statistics during the operation of the ETL process. Computer Associates Allen Systems Group
Data Modeling Tool - Used to specify logical and physical models for data warehouse tables, and to create meta data for these tables, which can then can be imported to the central meta data. Erwin Embarcadero
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Architecture Components Providers (contd)
Component Providers
Data Cleansing Tool - Used to remove errors and inconsistencies from data being imported to a data warehouse. First Logic
Semantic Layer - A logical layer of defined 'business objects', which are objects with business terminology that are mapped to the respective tables and columns in the data warehouse. This layer enables end-users to ask questions to the data warehouse in relevant business terminology. (See following slides for illustration) Business Objects Cognos Hyperion
Presentation Layer - Provides functionality for users to specify queries, create arbitrary reports, and to analyze their own data using drill-down and On-Line Analytical Processing (OLAP) functions (See following slides for illustrations) Business Objects Cognos Microstrategy
Warehouse Administration - Tools used to monitor end user activity inside the warehouse and/or the data marts, monitor loads times to the warehouse and ensure that the database is optimized for performance. Ambeo Bradmark Technology, Inc. Embarcadero Technologies, Inc.
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Dimensional Model

• A specific discipline for modeling data that is
  an alternative to entity relationship (E/R)
  modeling.
• Required to make database simple and
  understandable
• Gives user the ability to visualize data
• The main components of a dimensional model are
  fact tables and dimension tables.
• A fact table is the primary table in each
  dimensional model that is meant to contain
  measurements of the business.
• A dimension table is one of a set of companion
  tables to a fact table. Each dimension is defined
  by its primary key that serves as the basis for
  referential integrity with any given fact table
  to which it is joined.

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DW Schema Models

• We have two schema models which are suitable for
  Data Warehousing in most of the cases.
• Star Schema A star schema is a set of tables
  comprised of a single, central fact table
  surrounded by de-normalized dimensions. Each
  dimension is represented in a single table.
• Snowflake Schema If you normalize the star
  schema dimensions to separate tables and link
  them together, you will have a snowflake schema

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Star Schema
D
D
Product
Time
F

Sales
Amount_sold Product_ID Customer_ID Channel_ID Date
D
D
Channel
Customer
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Snowflake Schema
D
Product

• Defines hierarchies by using multiple dimension
  tables
• Is more normalized than a single table dimension
• Required if hierarchical/ level-wise details are
  to be displayed on reports

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Difference in OLTP and OLAP
OLTP
OLAP

1. Captures transactional information necessary to
   run business operations
2. Need performance
3. More DML operations
4. OLTP systems often use fully normalized schemas
   to optimize update/insert/delete performance and
   data consistency
5. A typical OLTP operation accesses only a handful
   of records
6. OLTP systems usually store data for only a few
   weeks or months if needed

1. Analyze transaction information at an aggregate
   level to improve the decision-making process
2. Need flexibility and broad scope
3. Very rare DML operations
4. Denormalized or partially denormalized (star
   schema) to optimize query performance
5. A typical DW query scans thousands/millions of
   rows.
6. Considerable historical data is maintained for
   analysis

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Data warehouse v/s Data Mart

• Data mart - A logical subset of the complete data
  warehouse.
• A data mart is a complete pie-wedge of the
  overall data warehouse pie.
• A data warehouse is made up of the union of all
  its data marts.
• A data warehouse is fed from the staging area.
• Every data mart must be represented by a
  dimensional model and, within a single data
  warehouse, all such data mart must be built from
  dimensions and facts.

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What is ETL ?

• Can also be called as ETVL
• Extraction
• Transformation
• Validation
• Loading

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Major ETL Tools

• Informatica
• IBM Datastage
• Ab-initio
• Microsoft DTS package
• Oracle Express
• SAS
• Hummingbird

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Few ETL process considerations

• Tracking dimensions SCDs can be difficult to
  maintain during incremental/delta updates of the
  dimension tables.
• Change Data Capture This is the ability to
  identify records in source systems which have
  been modified since last extraction. The source
  system should capture the change and ETL should
  use the change captured to extract relevant
  records
• Artificial keys generation of keys when
  inserting records into tables having artificial
  keys need to be taken care of
• Aggregation of data This is required for
  population of aggregation-level fact tables and
  when more hierarchical representation of data is
  required at report-level
• Scheduling Time based and conditional
  scheduling ease the administration of ETL process

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SCDs (Slowly Changing Dimensions)

• It is a concept of extracting recently modified
  data.
• Generally 3 types
• Type1 Keep most recent values in the target
• Type2 Keep full history of changes in the
  target
• 2.1 Keep version number in separate column
• 2.2 Mark the current dimension record with a
  flag
• 2.3 Mark the dimension records with their
  effective date range
• Type3 Keep the current and previous values in
  the target

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OLAP (OnLine Analytical Processing)

• ROLAP (Relational OLAP)
• MOLAP (Multidimensional OLAP)
• DOLAP (Desktop OLAP)
• HOLAP (Hybrid OLAP)

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ROLAP (Relational OLAP)

• With ROLAP data remains in the original
  relational tables, a separate set of relational
  tables is used to store and reference aggregation
  data. ROLAP is ideal for large databases or
  legacy data that is infrequently queried.

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MOLAP (Multidimensional OLAP)

1. High performance, multidimensional data storage
   format
2. Data is stored on the OLAP server
3. Gives the best query performance
4. For small to medium-sized data sets

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HOLAP (Hybrid OLAP)
HOLAP combines elements from MOLAP and ROLAP.
HOLAP keeps the original data in relational
tables but stores aggregations in a
multidimensional format.
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DOLAP (Desktop OLAP)

• Stores in the form of cubes/micro-cubes on the
  desktop/client machine
• BusinessObjects typically stores data in this
  format

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Major OLAP/Reporting Tools

• BusinessObjects
• Microstrategy
• Brio
• Cognos

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OLAP Dimensions v/s Relational Dimensions
OLAP
Relational
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Defining a Cube
Atlanta
Chicago
Market Dimension
Denver
Grapes
Cherries
Detroit
Melons
Products Dimension
Apples
Q4
Q1
Q2
Q3
Time Dimension
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Querying a Cube
Atlanta
Chicago
Markets Dimension
Denver
Grapes
Cherries
Dallas
Melons
Apples
Products Dimension
Q4
Q1
Q2
Q3
Time Dimension
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References

• www.datawarehouse.com
• Building the data warehouse W.H.Inmon
• White papers published by Informatica and
  BusinessObjects
• www.dmreview.com
• Infosys Knowledge repository - KShop

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Thanks