Data warehouse Concepts and Basics

1. Data warehouse Concepts and Basics

2. 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

3. Ground rules • Interactive • Participation amongst all • Please set your cell phones to vibration mode

4. 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 layman’s 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.

5. What is a data warehouse? • “A warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s 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 company’s 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 isn’t 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.

6. 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

7. 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.

8. 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

9. 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 -> Information -> Knowledge -> Decision -> Control

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

11. Presentation Layer Warehouse Source 1 Semantic administration Layer Static Universes Report Data Mart Source 1 Catalogues Extract, Data ODS transform & Dashboards Warehouse load Source 3 • Consolidated to Data Mart one data MDB warehouse Source 4 • Operational and expanded Data Mart data Variable Report MDB MDB Source N transformation & load Data extraction, Portal Metadata Metadata Repository Sources (Mainframe Data Data access Operational Data Store Data marts / Non - Mainframe warehouse and analysis Generic Architecture

12. 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.

13. 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.

14. Source data End-user User Interface Semantic Layer Data Schema 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

15. 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.

16. 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.

17. Formulate data management strategy Clean data and populate warehouse Architect data warehouse Build prototype for end-user testing Test prototype with end-users Finalize warehouse design based on feedback 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.

18. 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

19. Data warehouse is a database that stores data • Although the data for the data warehouse resides in a database (Oracle, DB2, etc.), it should not be confused with an application/transactional database. Any changes to the data should take place in the applications. • The data in the data warehouse is specifically used for querying. It improves the speed of access to information upon which decisions are made. The data warehouse is going to contain clean data • Data Warehouse data is as clean as the data it receives from the Source Systems. • A perfectly clean data environment is likely to happen only over a period of time as we make process and systems changes within marketing and across other functional areas within the organization. The data warehouse is going to contain data from all sources so that they can be used any time if needed in the future This is also likely to happen over a period of time. The recommendation is to phase in the implementation of the data warehouse to get to this end goal. Data warehousing notions and reality… Notion Reality Check

20. 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 • BI Tools Training • System Training • UAT /Enhancements 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

21. Architecture Components & Providers

22. Architecture Components & Providers (cont’d)

23. 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.

24. 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

25. Star Schema D D Product Time F Sales Amount_sold Product_ID Customer_ID Channel_ID Date D D Channel Customer

26. 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

27. Difference in OLTP and OLAP OLTP OLAP • Captures transactional information necessary to run business operations • Need performance • More DML operations • OLTP systems often use fully normalized schemas to optimize update/insert/delete performance and data consistency • A typical OLTP operation accesses only a handful of records • OLTP systems usually store data for only a few weeks or months if needed • Analyze transaction information at an aggregate level to improve the decision-making process • Need flexibility and broad scope • Very rare DML operations • Denormalized or partially denormalized (star schema) to optimize query performance • A typical DW query scans thousands/millions of rows. • Considerable historical data is maintained for analysis

28. 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.

29. What is ETL ? • Can also be called as ETVL • Extraction • Transformation • Validation • Loading

30. Major ETL Tools • Informatica • IBM Datastage • Ab-initio • Microsoft DTS package • Oracle Express • SAS • Hummingbird

31. 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

32. 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

33. OLAP (OnLine Analytical Processing) • ROLAP (Relational OLAP) • MOLAP (Multidimensional OLAP) • DOLAP (Desktop OLAP) • HOLAP (Hybrid OLAP)

34. 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.

35. MOLAP (Multidimensional OLAP) • High performance, multidimensional data storage format • Data is stored on the OLAP server • Gives the best query performance • For small to medium-sized data sets

36. 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.

37. DOLAP (Desktop OLAP) • Stores in the form of cubes/micro-cubes on the desktop/client machine • BusinessObjects typically stores data in this format

38. Major OLAP/Reporting Tools • BusinessObjects • Microstrategy • Brio • Cognos

39. Region West CA OREast MA NY RegionWestEastState RegionCA WestOR WestMA EastNY East OLAP Dimensions v/s Relational Dimensions OLAP Relational

40. Defining a Cube Atlanta Chicago Market Dimension Denver Grapes Cherries Detroit Melons Products Dimension Apples Q4 Q1 Q2 Q3 Time Dimension

41. SalesFact Querying a Cube Atlanta Chicago MarketsDimension Denver Grapes Cherries Dallas Melons Apples ProductsDimension Q4 Q1 Q2 Q3 TimeDimension

42. 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

43. Thanks