Datawarehousing Interview Questions Part 1

Q. What's A Data Warehouse? A1. A Data warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated. This makes it much easier and more efficient to run queries over data that originally came from different sources". Another definition for data warehouse is: " A data warehouse is a logical collection of information gathered from many different operational databases used to create business intelligence that supports business analysis activities and decision-making tasks, primarily, a record of an enterprise's past transactional and operational information, stored in a database designed to favour efficient data analysis and reporting (especially OLAP)". Generally, data warehousing is not meant for current "live" data, although 'virtual' or 'point-to-point' data warehouses can access operational data. A 'real' data warehouse is generally preferred to a virtual DW because stored data has been validated and is set up to provide reliable results to common types of queries used in a business.  A2. Data Warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated....This makes it much easier and more efficient to run queries over data that originally came from different sources.  Typical relational databases are designed for on-line transactional processing (OLTP) and do not meet the requirements for effective on-line analytical processing (OLAP). As a result, data warehouses are designed differently than traditional relational databases. Q. What is ODS? 1. ODS means Operational Data Store.  2. A collection of operation or bases data that is extracted from operation databases and standardized, cleansed, consolidated, transformed, and loaded into an enterprise data architecture. An ODS is used to support data mining of operational data, or as the store for base data that is summarized for a data warehouse. The ODS may also be used to audit the data warehouse to assure summarized and derived data is calculated properly. The ODS may further become the enterprise shared operational database, allowing operational systems that are being reengineered to use the ODS as there operation databases. Q. What is a dimension table? A. A dimensional table is a collection of hierarchies and categories along which the user can drill down and drill up. it contains only the textual attributes. Q. What is a lookup table? A. A lookup table is the one which is used when updating a warehouse. When the lookup is placed on the target table (fact table / warehouse) based upon the primary key of the target, it just updates the table by allowing only new records or updated records based on the lookup condition. Q. Why should you put your data warehouse on a different system than your OLTP system?    A1. A OLTP system is basically " data oriented " (ER model) and not " Subject oriented "(Dimensional Model) .That is why we design a separate system that will have a subject oriented OLAP system...  Moreover if a complex querry is fired on a OLTP system will cause a heavy overhead on the OLTP server that will affect the daytoday business directly.    A2. The loading of a warehouse will likely consume a lot of machine resources. Additionally, users may create querries or reports that are very resource intensive because of the potentially large amount of data available. Such loads and resource needs will conflict with the needs of the OLTP systems for resources and will negatively impact those production systems. Q. What are Aggregate tables? A. Aggregate table contains the summary of existing warehouse data which is grouped to certain levels of dimensions. Retrieving the required data from the actual table, which have millions of records will take more time and also affects the server performance. To avoid this we can aggregate the table to certain required level and can use it. This tables reduces the load in the database server and increases the performance of the query and can retrieve the result very fastly. Q. What is Dimensional Modeling? Why is it important? A. Dimensional Modeling is a design concept used by many data warehouse designers to build their data warehouse. In this design model all the data is stored in two types of tables - Facts table and Dimension table. Fact table contains the facts/measurements of the business and the dimension table contains the context of measurements i.e., the dimensions on which the facts are calculated. Q. Why is Data Modeling Important? A. Data modeling is probably the most labor intensive and time consuming part of the development process. Why bother especially if you are pressed for time? A common response by practitioners who write on the subject is that you should no more build a database without a model than you should build a house without blueprints.  The goal of the data model is to make sure that the all data objects required by the database are completely and accurately represented. Because the data model uses easily understood notations and natural language, it can be reviewed and verified as correct by the end-users.  The data model is also detailed enough to be used by the database developers to use as a "blueprint" for building the physical database. The information contained in the data model will be used to define the relational tables, primary and foreign keys, stored procedures, and triggers. A poorly designed database will require more time in the long-term. Without careful planning you may create a database that omits data required to create critical reports, produces results that are incorrect or inconsistent, and is unable to accommodate changes in the user's requirements. Q. What is data mining? A. Data mining is a process of extracting hidden trends within a datawarehouse. For example an insurance dataware house can be used to mine data for the most high risk people to insure in a certain geographial area. Q. What is ETL? A. ETL stands for extraction, transformation and loading.  ETL provide developers with an interface for designing source-to-target mappings, ransformation and job control parameter. · Extraction Take data from an external source and move it to the warehouse pre-processor database.  · Transformation Transform data task allows point-to-point generating, modifying and transforming data.  · Loading Load data task adds records to a database table in a warehouse. Q. What does level of Granularity of a fact table signify? A. Granularity The first step in designing a fact table is to determine the granularity of the fact table. By granularity, we mean the lowest level of information that will be stored in the fact table. This constitutes two steps:  Determine which dimensions will be included. Determine where along the hierarchy of each dimension the information will be kept. The determining factors usually goes back to the requirements Q. What is the Difference between OLTP and OLAP? A. Main Differences between OLTP and OLAP are:-  1. User and System Orientation  OLTP: customer-oriented, used for data analysis and querying by clerks, clients and IT professionals.  OLAP: market-oriented, used for data analysis by knowledge workers( managers, executives, analysis).  2. Data Contents  OLTP: manages current data, very detail-oriented.  OLAP: manages large amounts           data, provides facilities for summarization and aggregation, stores information at different levels of granularity to support decision making process.  3. Database Design  OLTP: adopts an entity relationship(ER) model and an application-oriented database design.  OLAP: adopts star, snowflake or fact constellation model and a subject-oriented database design.  4. View  OLTP: focuses on the current data within an enterprise or department.  OLAP: spans multiple versions of a database schema due to the evolutionary process of an organization; integrates information from many organizational locations and data stores Q. What is SCD1 , SCD2 , SCD3? A. SCD Stands for Slowly changing dimensions.  SCD1: only maintained updated values.  Ex: a customer address modified we update existing record with new address.  SCD2: maintaining historical information and current information by using  A) Effective Date B) Versions C) Flags or combination of these  SCD3: by adding new columns to target table we maintain historical information and current information. Q. Why are OLTP database designs not generally a good idea for a Data Warehouse? A. Since in OLTP,tables are normalised and hence query response will be slow for end user and OLTP doesnot contain years of data and hence cannot be analysed. Q. What is BUS Schema? A. BUS Schema is composed of a master suite of confirmed dimension and standardized definition if facts. Q. What are the various Reporting tools in the Market? A. 1. MS-Excel 2. Business Objects (Crystal Reports) 3. Cognos (Impromptu, Power Play) 4. Microstrategy 5. MS reporting services 6. Informatica Power Analyzer 7. Actuate 8. Hyperion (BRIO) 9. Oracle Express OLAP 10. Proclarity Q. What is Normalization, First Normal Form, Second Normal Form , Third Normal Form? A. 1.Normalization is process for assigning attributes to entities–Reducesdata redundancies–Helps eliminate data anomalies–Produces controlledredundancies to link tables  2.Normalization is the analysis offunctional dependency between attributes / data items of userviews?It reduces a complex user view to a set of small andstable subgroups of fields / relations  1NF:Repeating groups must beeliminated, Dependencies can be identified, All key attributesdefined,No repeating groups in table  2NF: The Table is already in1NF,Includes no partial dependencies–No attribute dependent on a portionof primary key, Still possible to exhibit transitivedependency,Attributes may be functionally dependent on non-keyattributes  3NF: The Table is already in 2NF, Contains no transitivedependencies Q. What is Fact table? A. Fact Table contains the measurements or metrics or facts of business process. If your business process is "Sales" , then a measurement of this business process such as "monthly sales number" is captured in the Fact table. Fact table also contains the foriegn keys for the dimension tables. Q. What are conformed dimensions?    A1. Conformed dimensions mean the exact same thing with every possible fact table to which they are joined Ex:Date Dimensions is connected all facts like Sales facts,Inventory facts..etc  A2. Conformed dimentions are dimensions which are common to the cubes.(cubes are the schemas contains facts and dimension tables)  Consider Cube-1 contains F1,D1,D2,D3 and Cube-2 contains F2,D1,D2,D4 are the Facts and Dimensions here D1,D2 are the Conformed Dimensions Q. What are the Different methods of loading Dimension tables? A. Conventional Load: Before loading the data, all the Table constraints will be checked against the data.  Direct load:(Faster Loading) All the Constraints will be disabled. Data will be loaded directly.Later the data will be checked against the table constraints and the bad data won't be indexed. Q. What is conformed fact? A. Conformed dimensions are the dimensions which can be used across multiple Data Marts in combination with multiple facts tables accordingly Q. What are Data Marts? A. Data Marts are designed to help manager make strategic decisions about their business.  Data Marts are subset of the corporate-wide data that is of value to a specific group of users.  There are two types of Data Marts: 1.Independent data marts – sources from data captured form OLTP system, external providers or from data generated locally within a particular department or geographic area. 2.Dependent data mart – sources directly form enterprise data warehouses. Q. What is a level of Granularity of a fact table? A. Level of granularity means level of detail that you put into the fact table in a data warehouse. For example: Based on design you can decide to put the sales data in each transaction. Now, level of granularity would mean what detail are you willing to put for each transactional fact. Product sales with respect to each minute or you want to aggregate it upto minute and put that data. Q. How are the Dimension tables designed? A. Most dimension tables are designed using Normalization principles upto 2NF. In some instances they are further normalized to 3NF.  Find where data for this dimension are located.  Figure out how to extract this data.  Determine how to maintain changes to this dimension (see more on this in the next section). Q. What are non-additive facts? A. Non-Additive: Non-additive facts are facts that cannot be summed up for any of the dimensions present in the fact table. Q. What type of Indexing mechanism do we need to use for a typical datawarehouse? A. On the fact table it is best to use bitmap indexes. Dimension tables can use bitmap and/or the other types of clustered/non-clustered, unique/non-unique indexes.  To my knowledge, SQLServer does not support bitmap indexes. Only Oracle supports bitmaps. Q. What Snow Flake Schema? A. Snowflake Schema, each dimension has a primary dimension table, to which one or more additional dimensions can join. The primary dimension table is the only table that can join to the fact table. Q. What is real time data-warehousing? A. Real-time data warehousing is a combination of two things: 1) real-time activity and 2) data warehousing. Real-time activity is activity that is happening right now. The activity could be anything such as the sale of widgets. Once the activity is complete, there is data about it.  Data warehousing captures business activity data. Real-time data warehousing captures business activity data as it occurs. As soon as the business activity is complete and there is data about it, the completed activity data flows into the data warehouse and becomes available instantly. In other words, real-time data warehousing is a framework for deriving information from data as the data becomes available. Q. What are slowly changing dimensions? A. SCD stands for Slowly changing dimensions. Slowly changing dimensions are of three types  SCD1: only maintained updated values.  Ex: a customer address modified we update existing record with new address.  SCD2: maintaining historical information and current information by using  A) Effective Date B) Versions C) Flags or combination of these scd3: by adding new columns to target table we maintain historical information and current information Q. What are Semi-additive and factless facts and in which scenario will you use such kinds of fact tables? A. Snapshot facts are semi-additive, while we maintain aggregated facts we go for semi-additive.  EX: Average daily balance  A fact table without numeric fact columns is called factless fact table.  Ex: Promotion Facts  While maintain the promotion values of the transaction (ex: product samples) because this table doesn’t contain any measures. Q. Differences between star and snowflake schemas? A. Star schema - all dimensions will be linked directly with a fat table. Snow schema - dimensions maybe interlinked or may have one-to-many relationship with other tables. Q. What is a Star Schema? A. Star schema is a type of organising the tables such that we can retrieve the result from the database easily and fastly in the warehouse environment.Usually a star schema consists of one or more dimension tables around a fact table which looks like a star,so that it got its name. Q. What is a general purpose scheduling tool? A. The basic purpose of the scheduling tool in a DW Application is to stream line the flow of data from Source To Target at specific time or based on some condition. Q. What is ER Diagram? A. The Entity-Relationship (ER) model was originally proposed by Peter in 1976 [Chen76] as a way to unify the network and relational database views.  Simply stated the ER model is a conceptual data model that views the real world as entities and relationships. A basic component of the model is the Entity-Relationship diagram which is used to visually represents data objects.  Since Chen wrote his paper the model has been extended and today it is commonly used for database design For the database designer, the utility of the ER model is:  it maps well to the relational model. The constructs used in the ER model can easily be transformed into relational tables. it is simple and easy to understand with a minimum of training. Therefore, the model can be used by the database designer to communicate the design to the end user.  In addition, the model can be used as a design plan by the database developer to implement a data model in a specific database management software. Q. Which columns go to the fact table and which columns go the dimension table? A. The Primary Key columns of the Tables(Entities) go to the Dimension Tables as Foreign Keys.  The Primary Key columns of the Dimension Tables go to the Fact Tables as Foreign Keys. Q. What are modeling tools available in the Market? A. Here are a number of data modeling tools  Tool Name Company Name Erwin Computer Associates Embarcadero Embarcadero Technologies Rational Rose IBM Corporation Power Designer Sybase Corporation Oracle Designer Oracle Corporation Q. Name some of modeling tools available in the Market? A. These tools are used for Data/dimension modeling  1. Oracle Designer 2. ERWin (Entity Relationship for windows) 3. Informatica (Cubes/Dimensions) 4. Embarcadero 5. Power Designer Sybase Q. How do you load the time dimension? A. Time dimensions are usually loaded by a program that loops through all possible dates that may appear in the data. It is not unusual for 100 years to be represented in a time dimension, with one row per day. Q. Explain the advanatages of RAID 1, 1/0, and 5. What type of RAID setup would you put your TX logs. A. Transaction logs write sequentially and don't need to be read at all. The ideal is to have each on RAID 1/0 because it has much better write performance than RAID 5.  RAID 1 is also better for TX logs and costs less than 1/0 to implement. It has a tad less reliability and performance is a little worse generally speaking.  RAID 5 is best for data generally because of cost and the fact it provides great read capability. Q. What are the vaious ETL tools in the Market? A. Various ETL tools used in market are:  1. Informatica 2. Data Stage 3. MS-SQL DTS(Integrated Services 2005) 4. Abinitio 5. SQL Loader 6. Sunopsis 7. Oracle Warehouse Bulider 8. Data Junction Q. What is VLDB?    A1. VLDB stands for Very Large DataBase.  It is an environment or storage space managed by a relational database management system (RDBMS) consisting of vast quantities of information.  A2. VLDB doesn’t refer to size of database or vast amount of information stored. It refers to the window of opportunity to take back up the database.  Window of opportunity refers to the time of interval and if the DBA was unable to take back up in the specified time then the database was considered as VLDB. Q. What are Data Marts? A. A data mart is a focused subset of a data warehouse that deals with a single area(like different department) of data and is organized for quick analysis Q. What are the steps to build the datawarehouse ? A. Gathering bussiness requiremnts Identifying Sources Identifying Facts Defining Dimensions Define Attribues Redefine Dimensions & Attributes Organise Attribute Hierarchy & Define Relationship Assign Unique Identifiers Additional convetions:Cardinality/Adding ratios Q. What is Difference between E-R Modeling and Dimentional Modeling.? A. Basic diff is E-R modeling will have logical and physical model. Dimensional model will have only physical model.  E-R modeling is used for normalizing the OLTP database design.  Dimensional modeling is used for de-normalizing the ROLAP/MOLAP design. Q. Why fact table is in normal form? A. Basically the fact table consists of the Index keys of the dimension/ook up tables and the measures.  so whenever we have the keys in a table .that itself implies that the table is in the normal form. Q. What are the advantages data mining over traditional approaches? A. Data Mining is used for the estimation of future. For example, if we take a company/business organization, by using the concept of Data Mining, we can predict the future of business interms of Revenue (or) Employees (or) Cutomers (or) Orders etc.  Traditional approches use simple algorithms for estimating the future. But, it does not give accurate results when compared to Data Mining. Q. What are the vaious ETL tools in the Market? Various ETL tools used in market are:  Informatica Data Stage Oracle Warehouse Bulider Ab Initio Data Junction Q. What is a CUBE in datawarehousing concept? A. Cubes are logical representation of multidimensional data.The edge of the cube contains dimension members and the body of the cube contains data values. Q. What is data validation strategies for data mart validation after loading process ? A. Data validation is to make sure that the loaded data is accurate and meets the business requriments.  Strategies are different methods followed to meet the validation requriments Q. What is the datatype of the surrgate key ? A. Datatype of the surrgate key is either inteeger or numaric or number Q. What is degenerate dimension table? A. Degenerate Dimensions : If a table contains the values, which r neither dimesion nor measures is called degenerate dimensions.Ex : invoice id,empno Q. What is Dimensional Modelling? A. Dimensional Modelling is a design concept used by many data warehouse desginers to build thier datawarehouse. In this design model all the data is stored in two types of tables - Facts table and Dimension table. Fact table contains the facts/measurements of the business and the dimension table contains the context of measuremnets ie, the dimensions on which the facts are calculated. Q. What are the methodologies of Data Warehousing.? A. Every company has methodology of their own. But to name a few SDLC Methodology, AIM methodology are stardadly used. Other methodologies are AMM, World class methodology and many more. Q. What is a linked cube? A. Linked cube in which a sub-set of the data can be analysed into great detail. The linking ensures that the data in the cubes remain consistent. Q. What is the main difference between Inmon and Kimball philosophies of data warehousing? A. Both differed in the concept of building teh datawarehosue..  According to Kimball ...  Kimball views data warehousing as a constituency of Data marts. Data marts are focused on delivering business objectives for departments in the organization. And the data warehouse is a conformed dimension of the data marts. Hence a unified view of the enterprise can be obtain from the dimension modeling on a local departmental level.    Inmon beliefs in creating a data warehouse on a subject-by-subject area basis. Hence the development of the data warehouse can start with data from the online store. Other subject areas can be added to the data warehouse as their needs arise. Point-of-sale (POS) data can be added later if management decides it is necessary.  i.e., Kimball--First DataMarts--Combined way ---Datawarehouse  Inmon---First Datawarehouse--Later----Datamarts Q. What is Data warehosuing Hierarchy? A. Hierarchies Hierarchies are logical structures that use ordered levels as a means of organizing data. A hierarchy can be used to define data aggregation. For example, in a time dimension, a hierarchy might aggregate data from the month level to the quarter level to the year level. A hierarchy can also be used to define a navigational drill path and to establish a family structure.  Within a hierarchy, each level is logically connected to the levels above and below it. Data values at lower levels aggregate into the data values at higher levels. A dimension can be composed of more than one hierarchy. For example, in the product dimension, there might be two hierarchies--one for product categories and one for product suppliers.  Dimension hierarchies also group levels from general to granular. Query tools use hierarchies to enable you to drill down into your data to view different levels of granularity. This is one of the key benefits of a data warehouse.  When designing hierarchies, you must consider the relationships in business structures. For example, a divisional multilevel sales organization.  Hierarchies impose a family structure on dimension values. For a particular level value, a value at the next higher level is its parent, and values at the next lower level are its children. These familial relationships enable analysts to access data quickly.  Levels A level represents a position in a hierarchy. For example, a time dimension might have a hierarchy that represents data at the month, quarter, and year levels. Levels range from general to specific, with the root level as the highest or most general level. The levels in a dimension are organized into one or more hierarchies.  Level Relationships Level relationships specify top-to-bottom ordering of levels from most general (the root) to most specific information. They define the parent-child relationship between the levels in a hierarchy.  Hierarchies are also essential components in enabling more complex rewrites. For example, the database can aggregate an existing sales revenue on a quarterly base to a yearly aggregation when the dimensional dependencies between quarter and year are known. Q. What is the main differnce between schema in RDBMS and schemas in DataWarehouse....? A. RDBMS Schema * Used for OLTP systems * Traditional and old schema * Normalized * Difficult to understand and navigate * Cannot solve extract and complex problems * Poorly modelled DWH Schema * Used for OLAP systems * New generation schema * De Normalized * Easy to understand and navigate * Extract and complex problems can be easily solved * Very good model Q. What is hybrid slowly changing dimension? A. Hybrid SCDs are combination of both SCD 1 and SCD 2.  It may happen that in a table, some columns are important and we need to track changes for them i.e capture the historical data for them whereas in some columns even if the data changes, we don't care.  For such tables we implement Hybrid SCDs, where in some columns are Type 1 and some are Type 2. Q. What are the different architecture of datawarehouse? A. There are two main things  1. Top down - (bill Inmon) 2.Bottom up - (Ralph kimbol) Q. What is incremintal loading?  Q. What is batch processing?  Q. What is crass reference table?  Q. What is aggregate fact table? A. Incremental loading means loading the ongoing changes in the OLTP.  Aggregate table contains the [measure] values ,aggregated /grouped/summed up to some level of hirarchy. Q. What is junk dimension? what is the difference between junk dimension and degenerated dimension? A. Junk dimension: Grouping of Random flags and text Attributes in a dimension and moving them to a separate sub dimension. Degenerate Dimension: Keeping the control information on Fact table ex: Consider a Dimension table with fields like order number and order line number and have 1:1 relationship with Fact table, In this case this dimension is removed and the order information will be directly stored in a Fact table inorder eliminate unneccessary joins while retrieving order information.. Q. What are the possible data marts in Retail sales.? A. Product information,sales information Q. What is the definition of normalized and denormalized view and what are the differences between them? A. Normalization is the process of removing redundancies.  Denormalization is the process of allowing redundancies. Q. What is meant by metadata in context of a Datawarehouse and how it is important? A. Meta data is the data about data; Business Analyst or data modeler usually capture information about data - the source (where and how the data is originated), nature of data (char, varchar, nullable, existance, valid values etc) and behavior of data (how it is modified / derived and the life cycle ) in data dictionary a.k.a metadata. Metadata is also presented at the Datamart level, subsets, fact and dimensions, ODS etc. For a DW user, metadata provides vital information for analysis / DSS. Q. Differences between star and snowflake schemas? A. Star schema A single fact table with N number of Dimension  Snowflake schema Any dimensions with extended dimensions are know as snowflake schema Q. Difference between Snow flake and Star Schema. What are situations where Snow flake Schema is better than Star Schema to use  and when the opposite is true? A. Star schema contains the dimesion tables mapped around one or more fact tables.  It is a denormalised model.  No need to use complicated joins.  Queries results fastly.  Snowflake schema  It is the normalised form of Star schema.  contains indepth joins ,bcas the tbales r splitted in to many pieces.We can easily do modification directly in the tables.  We hav to use comlicated joins ,since we hav more tables .  There will be some delay in processing the Query . Q. What is VLDB? A. The perception of what constitutes a VLDB continues to grow. A one terabyte database would normally be considered to be a VLDB. Q. What's the data types present in bo?n what happens if we implement view in the designer n report A. Three different data types: Dimensions,Measure and Detail.  View is nothing but an alias and it can be used to resolve the loops in the universe. Q. Can a dimension table contains numeric values? A. Yes.But those datatype will be char (only the values can numeric/char) Q. What is the difference between view and materialized view? A. View - store the SQL statement in the database and let you use it as a table. Everytime you access the view, the SQL statement executes.  Materialized view - stores the results of the SQL in table form in the database. SQL statement only executes once and after that everytime you run the query, the stored result set is used. Pros include quick query results. Q. What is surrogate key ? where we use it expalin with examples A1. surrogate key is a substitution for the natural primary key.  It is just a unique identifier or number for each row that can be used for the primary key to the table. The only requirement for a surrogate primary key is that it is unique for each row in the table.  Data warehouses typically use a surrogate, (also known as artificial or identity key), key for the dimension tables primary keys. They can use Infa sequence generator, or Oracle sequence, or SQL Server Identity values for the surrogate key.  It is useful because the natural primary key (i.e. Customer Number in Customer table) can change and this makes updates more difficult.  Some tables have columns such as AIRPORT_NAME or CITY_NAME which are stated as the primary keys (according to the business users) but ,not only can these change, indexing on a numerical value is probably better and you could consider creating a surrogate key called, say, AIRPORT_ID. This would be internal to the system and as far as the client is concerned you may display only the AIRPORT_NAME.  A2. Adapted from response by Vincent on Thursday, March 13, 2003  Another benefit you can get from surrogate keys (SID) is :  Tracking the SCD - Slowly Changing Dimension.  Let me give you a simple, classical example:  On the 1st of January 2002, Employee 'E1' belongs to Business Unit 'BU1' (that's what would be in your Employee Dimension). This employee has a turnover allocated to him on the Business Unit 'BU1' But on the 2nd of June the Employee 'E1' is muted from Business Unit 'BU1' to Business Unit 'BU2.' All the new turnover have to belong to the new Business Unit 'BU2' but the old one should Belong to the Business Unit 'BU1.'  If you used the natural business key 'E1' for your employee within your datawarehouse everything would be allocated to Business Unit 'BU2' even what actualy belongs to 'BU1.'  If you use surrogate keys, you could create on the 2nd of June a new record for the Employee 'E1' in your Employee Dimension with a new surrogate key.  This way, in your fact table, you have your old data (before 2nd of June) with the SID of the Employee 'E1' + 'BU1.' All new data (after 2nd of June) would take the SID of the employee 'E1' + 'BU2.'  You could consider Slowly Changing Dimension as an enlargement of your natural key: natural key of the Employee was Employee Code 'E1' but for you it becomes Employee Code + Business Unit - 'E1' + 'BU1' or 'E1' + 'BU2.' But the difference with the natural key enlargement process, is that you might not have all part of your new key within your fact table, so you might not be able to do the join on the new enlarge key -> so you need another id. Q. What is ER Diagram? The Entity-Relationship (ER) model was originally proposed by Peter in 1976 [Chen76] as a way to unify the network and relational database views.  Simply stated the ER model is a conceptual data model that views the real world as entities and relationships. A basic component of the model is the Entity-Relationship diagram which is used to visually represents data objects.  Since Chen wrote his paper the model has been extended and today it is commonly used for database design For the database designer, the utility of the ER model is:  it maps well to the relational model. The constructs used in the ER model can easily be transformed into relational tables. it is simple and easy to understand with a minimum of training. Therefore, the model can be used by the database designer to communicate the design to the end user.  In addition, the model can be used as a design plan by the database developer to implement a data model in a specific database management software. Q. What is aggregate table and aggregate fact table ... any examples of both? A. Aggregate table contains summarised data. The materialized view are aggregated tables.  for ex in sales we have only date transaction. if we want to create a report like sales by product per year. in such cases we aggregate the date vales into week_agg, month_agg, quarter_agg, year_agg. to retrive date from this tables we use @aggrtegate function. Q. What is active data warehousing? A. An active data warehouse provides information that enables decision-makers within an organization to manage customer relationships nimbly, efficiently and proactively. Active data warehousing is all about integrating advanced decision support with day-to-day-even minute-to-minute-decision making in a way that increases quality of those customer touches which encourages customer loyalty and thus secure an organization's bottom line. The marketplace is coming of age as we progress from first-generation "passive" decision-support systems to current- and next-generation "active" data warehouse implementations Q. Why do we override the execute method is struts? Plz give me the details? A. As part of Struts FrameWork we can decvelop the Action Servlet,ActionForm servlets(here ActionServlet means which class extends the Action class is called ActionServlet and ActionFome means which calss extends the ActionForm calss is called the Action Form servlet)and other servlets classes.  In case of ActionForm class we can develop the validate().this method will return the ActionErrors object.In this method we can write the validation code.If this method return null or ActionErrors with size=0,the webcontainer will call the execute() as part of the Action class.if it returns size > 0 it willnot be call the execute().it will execute the jsp,servlet or html file as value for the input attribute as part of the attribute in struts-config.xml file. Q. What is the difference between Datawarehousing and BusinessIntelligence? A. Data warehousing deals with all aspects of managing the development, implementation and operation of a data warehouse or data mart including meta data management, data acquisition, data cleansing, data transformation, storage management, data distribution, data archiving, operational reporting, analytical reporting, security management, backup/recovery planning, etc. Business intelligence, on the other hand, is a set of software tools that enable an organization to analyze measurable aspects of their business such as sales performance, profitability, operational efficiency, effectiveness of marketing campaigns, market penetration among certain customer groups, cost trends, anomalies and exceptions, etc. Typically, the term “business intelligence” is used to encompass OLAP, data visualization, data mining and query/reporting tools.Think of the data warehouse as the back office and business intelligence as the entire business including the back office. The business needs the back office on which to function, but the back office without a business to support, makes no sense. Q. What is the difference between OLAP and datawarehosue? A. Datawarehouse is the place where the data is stored for analyzing  where as OLAP is the process of analyzing the data,managing aggregations,  partitioning information into cubes for indepth visualization. Q. What is fact less fact table? where you have used it in your project? Factless table means only the key available in the Fact there is no mesures availalabl Q. Why Denormalization is promoted in Universe Designing? A. In a relational data model, for normalization purposes, some lookup tables are not merged as a single table. In a dimensional data modeling(star schema), these tables would be merged as a single table called DIMENSION table for performance and slicing data.Due to this merging of tables into one large Dimension table, it comes out of complex intermediate joins. Dimension tables are directly joined to Fact tables.Though, redundancy of data occurs in DIMENSION table, size of DIMENSION table is 15% only when compared to FACT table. So only Denormalization is promoted in Universe Desinging. Q. What is the difference between ODS and OLTP? A. ODS:- It is nothing but a collection of tables created in the Datawarehouse that maintains only current data  where as OLTP maintains the data only for transactions, these are designed for recording daily operations and transactions of a business Q. What is the difference between datawarehouse and BI? A. Simply speaking, BI is the capability of analyzing the data of a datawarehouse in advantage of that business. A BI tool analyzes the data of a datawarehouse and to come into some business decision depending on the result of the analysis. Q. Is OLAP databases are called decision support system ??? true/false? True Q. Explain in detail about type 1, type 2(SCD), type 3 ? A. Type-1  Most Recent Value  Type-2(full History)  i) Version Number  ii) Flag  iii) Date  Type-3  Current and one Perivies value Q. What is snapshot? A. You can disconnect the report from the catalog to which it is attached by saving the report with a snapshot of the data. However, you must reconnect to the catalog if you want to refresh the data. Q. What is the difference between datawarehouse and BI? A. Simply speaking, BI is the capability of analyzing the data of a datawarehouse in advantage of that business. A BI tool analyzes the data of a datawarehouse and to come into some business decision depending on the result of the analysis. Q. What are non-additive facts in detail? A. A fact may be measure, metric or a dollar value. Measure and metric are non additive facts.  Dollar value is additive fact. If we want to find out the amount for a particular place for a particular period of time, we can add the dollar amounts and come up with the total amount.  A non additive fact, for eg measure height(s) for 'citizens by geographical location' , when we rollup 'city' data to 'state' level data we should not add heights of the citizens rather we may want to use it to derive 'count' Q. What is a data warehouse?   A. A data warehouse is a collection of data marts representing historical data from different operations in the company. This data is stored in a structure optimized for querying and data analysis as a data warehouse. Table design, dimensions and organization should be consistent throughout a data warehouse so that reports or queries across the data warehouse are consistent. A data warehouse can also be viewed as a database for historical data from different functions within a company. Q. What is a data mart? A. A data mart is a segment of a data warehouse that can provide data for reporting and analysis on a section, unit, department or operation in the company, e.g. sales, payroll, production. Data marts are sometimes complete individual data warehouses which are usually smaller than the corporate data warehouse. Q. What are the benefits of data warehousing?   A. Data warehouses are designed to perform well with aggregate queries running on large amounts of data. The structure of data warehouses is easier for end users to navigate, understand and query against unlike the relational databases primarily designed to handle lots of transactions. Data warehouses enable queries that cut across different segments of a company's operation. E.g. production data could be compared against inventory data even if they were originally stored in different databases with different structures. Queries that would be complex in very normalized databases could be easier to build and maintain in data warehouses, decreasing the workload on transaction systems. Data warehousing is an efficient way to manage and report on data that is from a variety of sources, non uniform and scattered throughout a company. Data warehousing is an efficient way to manage demand for lots of information from lots of users. Data warehousing provides the capability to analyze large amounts of historical data for nuggets of wisdom that can provide an organization with competitive advantage. Q. What is OLAP?   A. OLAP stands for Online Analytical Processing. It uses database tables (fact and dimension tables) to enable multidimensional viewing, analysis and querying of large amounts of data. E.g. OLAP technology could provide management with fast answers to complex queries on their operational data or enable them to analyze their company's historical data for trends and patterns. Q. What is OLTP?   A. OLTP stands for Online Transaction Processing.   OLTP uses normalized tables to quickly record large amounts of transactions while making sure that these updates of data occur in as few places as possible. Consequently OLTP database are designed for recording the daily operations and transactions of a business. E.g. a timecard system that supports a large production environment must record successfully a large number of updates during critical periods like lunch hour, breaks, startup and close of work. Q. What are dimensions?   A. Dimensions are categories by which summarized data can be viewed. E.g. a profit summary in a fact table can be viewed by a Time dimension (profit by month, quarter, year), Region dimension (profit by country, state, city), Product dimension (profit for product1, product2). Q. What are fact tables?   A. A fact table is a table that contains summarized numerical and historical data (facts) and a multipart index composed of foreign keys from the primary keys of related dimension tables. Q. What are measures?   A. Measures are numeric data based on columns in a fact table. They are the primary data which end users are interested in. E.g. a sales fact table may contain a profit measure which represents profit on each sale. Q. What are aggregations?   A. Aggregations are precalculated numeric data. By calculating and storing the answers to a query before users ask for it, the query processing time can be reduced. This is key in providing fast query performance in OLAP. Q. What are cubes?   A. Cubes are data processing units composed of fact tables and dimensions from the data warehouse. They provide multidimensional views of data, querying and analytical capabilities to clients. Q. What is the PivotTable® Service?   A. This is the primary component that connects clients to the Microsoft® SQL Server™ 2000 Analysis Server. It also provides the capability for clients to create local offline cubes using it as an OLAP server. PivotTable® Service does not have a user interface, the clients using its services has to provide its user interface. Q. What are offline OLAP cubes?   A. These are OLAP cubes created by clients, end users or third-party applications accessing a data warehouse, relational database or OLAP cube through the Microsoft® PivotTable® Service. E.g. Microsoft® Excel™ is very popular as a client for creating offline local OLAP cubes from relational databases for multidimensional analysis. These cubes have to be maintained and managed by the end users who have to manually refresh their data. Q. What are virtual cubes?   A. These are combinations of one or more real cubes and require no disk space to store them. They store only the definitions and not the data of the referenced source cubes. They are similar to views in relational databases. Q. What are MOLAP cubes?   A. MOLAP Cubes:   stands for Multidimensional OLAP. In MOLAP cubes the data aggregations and a copy of the fact data are stored in a multidimensional structure on the Analysis Server computer. It is best when extra storage space is available on the Analysis Server computer and the best query performance is desired. MOLAP local cubes contain all the necessary data for calculating aggregates and can be used offline. MOLAP cubes provide the fastest query response time and performance but require additional storage space for the extra copy of data from the fact table. Q. What are ROLAP cubes?   A. ROLAP Cubes:   stands for Relational OLAP. In ROLAP cubes a copy of data from the fact table is not made and the data aggregates are stored in tables in the source relational database. A ROLAP cube is best when there is limited space on the Analysis Server and query performance is not very important. ROLAP local cubes contain the dimensions and cube definitions but aggregates are calculated when they are needed. ROLAP cubes requires less storage space than MOLAP and HOLAP cubes. Q. What are HOLAP cubes?   A. HOLAP Cubes:   stands for Hybrid OLAP. A ROLAP cube has a combination of the ROLAP and MOLAP cube characteristics. It does not create a copy of the source data however, data aggregations are stored in a multidimensional structure on the Analysis Server computer. HOLAP cubes are best when storage space is limited but faster query responses are needed. Q. What is the approximate size of a data warehouse? A. You can estimate the approximate size of a data warehouse made up of only fact and dimension tables by estimating the approximate size of the fact tables and ignoring the sizes of the dimension tables. To estimate the size of the fact table in bytes, multiply the size of a row by the number of rows in the fact table. A more exact estimate would include the data types, indexes, page sizes, etc. An estimate of the number of rows in the fact table is obtained by multiplying the number of transactions per hour by the number of hours in a typical work day and then multiplying the result by the number of days in a year and finally multiply this result by the number of years of transactions involved. Divide this result by 1024 to convert to kilobytes and by 1024 again to convert to megabytes. E.g. A data warehouse will store facts about the help provided by a company’s product support representatives. The fact table is made of up of a composite key of 7 indexes (int data type) including the primary key. The fact table also contains 1 measure of time (datetime data type) and another measure of duration (int data type). 2000 product incidents are recorded each hour in a relational database. A typical work day is 8 hours and support is provided for every day in the year. What will be approximate size of this data warehouse in 5 years? First calculate the approximate size of a row in bytes (int data type = 4 bytes, datetime data type = 8 bytes): size of a row = size of all composite indexes (add the size of all indexes) + size of all measures (add the size of all measures). Size of a row (bytes)  = (4 * 7) + (8 + 4). Size of a row (bytes) = 40 bytes. Number of rows in fact table = (number of transactions per hour) * (8 hours) * (365 days in a year). Number of rows in fact table = (2000 product incidents per hour) * (8 hours ) * (365 days in a year). Number of rows in fact table = 2000 * 8 * 365 Number of rows in fact table = 5840000 Size of fact table (1 year) = (Number of rows in fact table) * (Size of a row) Size of fact table (bytes per year) = 5840000 * 40 Size of fact table (bytes per year) = 233600000. Size of fact table (megabytes per year) = 233600000 / (1024*1024) Size of fact table (in megabytes for 5 years) = (23360000 * 5) / (1024 *1024) Size of fact table (megabytes) = 1113.89 MB Size of fact table (gigabytes) = 1113.89 / 1024 Size of fact table (gigabytes) = 1.089 GB