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Idq New Log Files Definition of data warehousing? Data warehouse is a Subject oriented, Integrated, Time variant, Non volatile collection of data in support of management's decision making process. Subject Oriented Data warehouses are designed to help you analyze data. For example, to learn more about your company's sales data, you can build a warehouse that concentrates on sales. Using this warehouse, you can answer questions like "Who was our best customer for this item last year?" This ability to define a data warehouse by subject matter, sales in this case makes the data warehouse subject oriented. Integrated Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated. Nonvolatile Nonvolatile means that, once entered into the warehouse, data should not change. This is logical because the purpose of a warehouse is to enable you to analyze what has occurred. Time Variant In order to discover trends in business, analysts need large amounts of data. This is very much in contrast to online transaction processing (OLTP) systems, where performance requirements demand that historical data be moved to an archive. A data warehouse's focus on change over time is what is meant by the term time variant. 2. How many stages in Datawarehousing? Data warehouse generally includes two stages ü ETL ü Report Generation ETL Short for extract, transform, load, three database functions that are combined into one tool • Extract -- the process of reading data from a source database. • Transform -- the process of converting the extracted data from its previous form into required form • Load -- the process of writing the data into the target database. ETL is used to migrate data from one database to another, to form data marts anddata warehouses and also to convert databases from one format to another format. It is used to retrieve the data from various operational databases and is transformed into useful information and finally loaded into Datawarehousing system. 1 INFORMATICA 2 ABINITO 3 DATASTAGE 4. BODI 5 ORACLE WAREHOUSE BUILDERS Report generation In report generation, OLAP is used (i.e.) online analytical processing. It is a set of specification which allows the client applications in retrieving the data for analytical processing. It is a specialized tool that sits between a database and user in order to provide various analyses of the data stored in the database. OLAP Tool is a reporting tool which generates the reports that are useful for Decision support for top level management. 1. Business Objects 2. Cognos 3. Micro strategy 4. Hyperion 5. Oracle Express 6. Microsoft Analysis Services • Different Between OLTP and OLAP OLTP OLAP 1 Application Oriented Subject Oriented (subject in the (e.g., purchase order it is sense customer, product, item, functionality of an time) application) 2 Used to run business Used to analyze business 3 Detailed data Summarized data 4 Repetitive access Ad-hoc access 5 Few Records accessed at Large volumes accessed at a a time (tens), simple time(millions), complex query query 6 Small database Large Database 7 Current data Historical data 8 Clerical User Knowledge User 9 Row by Row Loading Bulk Loading 1 Time invariant Time variant 0 1 Normalized data De-normalized data 1 1 E – R schema Star schema 2 3. What are the types of datawarehousing? EDW (Enterprise datawarehousing) ü It provides a central database for decision support throughout the enterprise ü It is a collection of DATAMARTS DATAMART ü It is a subset of Datawarehousing ü It is a subject oriented database which supports the needs of individuals depts. in an organizations ü It is called high performance query structure ü It supports particular line of business like sales, marketing etc.. ODS (Operational data store) ü It is defined as an integrated view of operationaldatabase designed to support operational monitoring ü It is a collection of operational data sources designed to support Transaction processing ü Data is refreshed near real-time and used for business activity ü It is an intermediate between the OLTP and OLAP which helps to create an instance reports 4. What are the modeling involved in Data Warehouse Architecture? 5. What are the types of Approach in DWH? Bottom up approach: first we need to develop data mart then we integrate these data mart into EDW Top down approach: first we need to develop EDW then form that EDW we develop data mart Bottom up OLTP ETL Data mart DWH OLAP Top down OLTP ETL DWH Data mart OLAP Top down ü Cost of initial planning & design is high ü Takes longer duration of more than an year Bottom up ü Planning & Designing the Data Marts without waiting for the Global warehouse design ü Immediate results from the data marts ü Tends to take less time to implement ü Errors in critical modules are detected earlier. ü Benefits are realized in the early phases. ü It is a Best Approach Data Modeling Types: ü Conceptual Data Modeling ü Logical Data Modeling ü Physical Data Modeling ü Dimensional Data Modeling 1. Conceptual Data Modeling ü Conceptual data model includes all major entities and relationships and does not contain much detailed level of information about attributes and is often used in the INITIAL PLANNING PHASE ü Conceptual data model is created by gathering business requirements from various sources like business documents, discussion with functional teams, business analysts, smart management experts and end users who do the reporting on the database. Data modelers create conceptual data model and forward that model to functional team for their review. ü Conceptual data modeling gives an idea to the functional and technical team about how business requirements would be projected in the logical data model. 2. Logical Data Modeling ü This is the actual implementation and extension of a conceptual data model. Logical data model includes all required entities, attributes, key groups, and relationships that represent business information and define business rules. 3. Physical Data Modeling ü Physical data model includes all required tables, columns, relationships, database properties for the physical implementation of databases. Database performance, indexing strategy, physical storage and demoralization are important parameters of a physical model. Logical vs. Physical Data Modeling Logical Data Model Physical Data Model Represents business information Represents the physical implementation and defines business rules of the model in a database. Entity Table Attribute Column Primary Key Primary Key Constraint Alternate Key Unique Constraint or Unique Index Inversion Key Entry Non Unique Index Rule Check Constraint, Default Value Relationship Foreign Key Definition Comment Dimensional Data Modeling ü Dimension model consists of fact and dimension tables ü It is an approach to develop the schema DB designs Types of Dimensional modeling ü Star schema ü Snow flake schema ü Star flake schema (or) Hybrid schema ü Multi star schema What is Star Schema? ü The Star Schema Logical database design which contains a centrally located fact table surrounded by at least one or more dimension tables ü Since the database design looks like a star, hence it is called star schema db ü The Dimension table contains Primary keys and the textual descriptions ü It contain de-normalized business information ü A Fact table contains a composite key and measures ü The measure are of types of key performance indicators which are used to evaluate the enterprise performance in the form of success and failure ü Eg: Total revenue , Product sale , Discount given, no of customers ü To generate meaningful report the report should contain at least one dimension and one fact table The advantage of star schema ü Less number of joins ü Improve query performance ü Slicing down ü Easy understanding of data. Disadvantage: ü Require more storage space Example of Star Schema: Snowflake Schema ü In star schema, If the dimension tables are spitted into one or more dimension tables ü The de-normalized dimension tables are spitted into a normalized dimension table Example of Snowflake Schema: ü In Snowflake schema, the example diagram shown below has 4 dimension tables, 4 lookup tables and 1 fact table. The reason is that hierarchies (category, branch, state, and month) are being broken out of the dimension tables (PRODUCT, ORGANIZATION, LOCATION, and TIME) respectively and separately. ü It increases the number of joins and poor performance in retrieval of data. ü In few organizations, they try to normalize the dimension tables to save space. ü Since dimension tables hold less space snow flake schema approach may be avoided. ü Bit map indexes cannot be effectively utilized Important aspects of Star Schema & Snow Flake Schema ü In a star schema every dimension will have a primary key. ü In a star schema, a dimension table will not have any parent table. ü Whereas in a snow flake schema, a dimension tablewill have one or more parent tables. ü Hierarchies for the dimensions are stored in the dimensional table itself in star schema. ü Whereas hierarchies are broken into separate tables in snow flake schema. These hierarchies help to drill down the data from topmost hierarchies to the lowermost hierarchies. Star flake schema (or) Hybrid Schema ü Hybrid schema is a combination of Star and Snowflake schema Multi Star schema ü Multiple fact tables sharing a set of dimension tables ü Confirmed Dimensions are nothing but Reusable Dimensions. ü The dimensions which u r using multiple times or in multiple data marts. ü Those are common in different data marts Measure Types (or) Types of Facts • Additive - Measures that can be summed up across all dimensions.
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