Presentation on theme: "Data Warehousing – An Introductory Perspective DWCC BBSR DWCC BBSR."— Presentation transcript:
Data Warehousing – An Introductory Perspective DWCC BBSR DWCC BBSR
Agenda Why Data Warehouse Definition and Architecture Terminology
The Business Need Business Decisions Are not made by Rolling Dices We Don’t know What we don’t know I think…. errrr, I guess so
Current Business Environment Competitive Ever Changing Chaotic Global Urgency to make decisions Competitive advantages stems from well informed decisions Based on an understanding of: Your Products Your Customers Preferences The Competition Your own company strengths
The Value Pyramid Each layer provides Value en route to a targeted business Outcome Business Outcomes Actions Decisions Knowledge Information Data Increased revenue Increased productivity Reduced costs Competitive advantage
Definitions A collection of integrated, subject oriented databases designed to support the DSS function where each unit of data is relevant at some moment of time (Inmon 1991) A copy of transaction data specifically structured to Query and Analysis (Kimball 1996) Data Warehouse is NOT a specific technology It is a series of processes, procedures and tools that help the enterprise understand more about itself, its products, its customers and the market it services. It is NOT possible to purchase a Data Warehouse But, it is possible to build one.
FEATURES Non Volatile - Used mainly for reporting purpose and it is independent of transactional data. Subject Orientation- All relevant data is stored together. Ex: Sales, Finance, Marketing, Customer data etc. Historical data- Can contain data of several years depending on company requirements. sachin_kambhoj :
Subject Orientation. Operational Datawarehouse AUTO HEALTH LIFE CASUALTY Customer Policy Premium Claims Applications Subjects
Goals and Applications Goals of a Data Warehouse Provide reliable, High performance access Consistent view of Data: Same query, same data. All users should be warned if data load has not come in. Slice and dice capability Quality of data is a driver for business re-engineering. Data Warehousing Applications: Customer Profitability Analysis Customer satisfaction and retention Buyer behavior. Pricing, Promotion Analysis Market research Inventory optimization
OLTP v/s Data Warehouse OLTP system runs the business, Data Warehouses tell you how to run the business CharacteristicOLTPData Warehouse OrientationTransactionAnalysis Data AccessRecord at a timeSet at a time UpdatesFrequent & Unscheduled Periodic & Scheduled Response timeSeconds requiredMinutes acceptable Concurrent usersManyFew AvailabilityGuaranteedAs needed Data structuresHighly normalizedOften de- normalized Data natureCurrenthistorical
If most of your business needs are To report on data in a single transaction processing system All the historical data you need are in the system Data in the system is clean Your hardware can support reporting against the live system data The structure of the system data is relatively simple Your firm does not have much interest in end user adhoc query/report tools Data warehousing may not be for your business!!
Modeling Constructs Entity Relationship Diagram Star schema Snow flake schema Within the implementation of a warehouse, several of these constructs may be integrated to form an optimal design
Entity Relationship Diagram Based on set theory and SQL Highly normalized Optimized for update and fast transaction turnaround Not suited for querying in a data warehouse environment diagrams like these are very difficult for users to visualize and memorize.
Star Schema A central fact table surrounded by a number of dimension tables. Dimensions are business entities on which calculations are done. They can be numeric or alphanumeric. Example: Product table comprising brand name, category, packaging type, size. Facts are numerical measurements of business with respect to dimensions.They are numeric and additive (summable across any combination) e.g. A sales fact table could contain time, product and store key along with dollars sold, units sold, dollars cost.
Snow Flake Schema Normalized version of the star schema with the addition of normalized dimension tables. Normalization helps to reduce redundancy in the dimension tables, but affects performance and user comprehension.
DW Terminology Granularity Granularity (or grain) defines the level of detail stored in the physical warehouse Low granularity indicates lot of detail while high granularity indicates less detail. Example: A commercial airline is building a data warehouse. What will the granularity be? Choice A: Each record represents a flight Choice B: Each record represents the customer on a flight There is no correct answer. To a large extent, the granularity depends on the business User’s exploitation needs. However, you should be aware that the granularity of data affects Volumes of Data, Data Maintenance, Indexing Level of Data Exploration Query and Reporting constraints
DW Terminology Metadata At all levels of the data warehouse, information is required to support the maintenance and use of the data warehouse. Metadata is data about data. There are two views of Metadata Business – are warehouse attributes and properties for use by business users Technical – describe data flow from Operational systems into the data warehouse OLAP Online Analytical processing Tool(s) for Analytical Reporting including Graphical capabilities.
DW Terminology OLAP Tools available for exploring the information built in a DW : Multi-dimensional On-line Analytical Processing (MOLAP) The data from data warehouse is queried and dumped periodically on to a server on local network to a data storage called Multi-dimensional Database (MDDB) provided by the OLAP tool. This MDDB forms a Data Mart which is then used for querying and reporting. Relational On-Line Analytical Processing (ROLAP) Refers to the ability to conduct OLAP analysis directly against a relational warehouse without any constraints on the number of dimensions, database size, analytical complexity, or number and type of users. Hybrid On-line Analytical Processing (HOLAP) An environment with a combination of MOLAP and ROLAP data storage. Summarized information is typically stored in an MDDB and detailed data is stored in a Relational environment.
Terminology Data Mart- Contains Data about a specific subject. Eg. Official data, Customer data, Campaign data etc. Metadata- Data about data. Describes the data stored in Data warehouse. Data Cubes- Central object of data containing information in a multidimensional structure. Data Cleansing- Regular cleaning of data. ETL- Extraction, Transformation and Loading of Data. Data Mining- A mechanism which uses intelligent algorithms to discover patterns, clusters and models from data.
Stages Heterogeneous Source Systems Operational Staging Area Data Warehouse Business Intelligence External Legacy Data Mining Query & Reporting OLAP Extraction, Transformation & Loading (ETL)
A Typical Data Warehouse Data Warehouse Detailed Data Data Mart Data Mart Data Mart Summarized Data Meta Data Facilitates in firing queries on detailed data. Data marts contain data specific to a subject.
MOLAP/ROLAP/HOLAP Query Tool by MDD Vendor Custom Loader Data Warehouse (RDBMS) OLAP Engine MDD Proprietary API Cubes MDD Proprietary API Rows SQL Rows MDD Database Storage Periodic, Manual Data Load
OLAP Terminology Analytical technique whereby the user navigates from the most summarized to the most detailed level. Region Month Product Region State District Location
OLAP Terminology Rotation Or Dicing Region MONThMONTh Product Month PRODUCtPRODUCt Region
OLAP Terminology Slicing Region MONThMONTh Product
Products and Vendors Data Warehouses Oracle Sybase DB2 OLAP tools Oracle Express Hyperion Essbase Data Mining Oracle Darwin IBM Intelligent Data Miner Querying & Reporting Oracle Discoverer Business Objects