Data Warehousing Interview

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General Datawarehousing Interview Questions and Answers (1) What's A Data warehouse Answer1: 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. Answer2: 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. 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. What is a dimension table? 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. What is a lookup table? 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. Why should you put your data warehouse on a different system than your OLTP system? Answer1: 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. Answer2: 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.

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Continued from previous part...) General Datawarehousing Interview Questions and Answers (2) What are Aggregate tables? 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. What is Dimensional Modelling? Why is it important ? 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. Why is Data Modeling Important? 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. What is data mining? 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. What is ETL? 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.

General Datawarehousing Interview Questions and Answers (3) What does level of Granularity of a fact table signify? 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 What is the Difference between OLTP and OLAP? 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 of historical 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 What is SCD1 , SCD2 , SCD3? 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.

Why are OLTP database designs not generally a good idea for a Data Warehouse? 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. What is BUS Schema? BUS Schema is composed of a master suite of confirmed dimension and standardized definition if facts. General Datawarehousing Interview Questions and Answers (4) What are the various Reporting tools in the Market? 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 What is Normalization, First Normal Form, Second Normal Form , Third Normal Form? 1.Normalization is process for assigning attributes to entities–Reduces data redundancies–Helps eliminate data anomalies–Produces controlledredundancies to link tables 2.Normalization is the analysis of functional dependency between attributes / data items of user views? It reduces a complex user view to a set of small andstable subgroups of fields / relations 1NF:Repeating groups must be eliminated, Dependencies can be identified, All key attributes defined, No repeating groups in table 2NF: The Table is already in1NF,Includes no partial dependencies–No attribute dependent on a portion of primary key, Still possible to exhibit transitive dependency, Attributes may be functionally dependent on nonkey attributes 3NF: The Table is already in 2NF, Contains no transitive dependencies What is Fact table? 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. What are conformed dimensions? Answer1: 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 Answer2: 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 What are the Different methods of loading Dimension tables?

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. What is conformed fact? Conformed dimensions are the dimensions which can be used across multiple Data Marts in combination with multiple facts tables accordingly General Datawarehousing Interview Questions and Answers (5) What are Data Marts? 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. What is a level of Granularity of a fact table? 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. How are the Dimension tables designed? 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). What are non-additive facts? Non-Additive: Non-additive facts are facts that cannot be summed up for any of the dimensions present in the fact table. What type of Indexing mechanism do we need to use for a typical datawarehouse? 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. What Snow Flake Schema?

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. General Datawarehousing Interview Questions and Answers (6) What is real time data-warehousing? 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. What are slowly changing dimensions? 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 What are Semi-additive and factless facts and in which scenario will you use such kinds of fact tables? 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. Differences between star and snowflake schemas? 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. What is a Star Schema? 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. What is a general purpose scheduling tool? 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.

General Datawarehousing Interview Questions and Answers (7) 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. Which columns go to the fact table and which columns go the dimension table? 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. What are modeling tools available in the Market? 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 Name some of modeling tools available in the Market? 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 How do you load the time dimension? 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. General Datawarehousing Interview Questions and Answers (8) Explain the advanatages of RAID 1, 1/0, and 5. What type of RAID setup would you put your TX logs. 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. What are the vaious ETL tools in the Market? 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 What is VLDB? Answer 1: 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. Answer 2: 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. Data Warehousing Basics Interview Questions and Answers (1) What are Data Marts ? 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 What are the steps to build the datawarehouse ? 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 What is Difference between E-R Modeling and Dimentional Modeling.? 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.

Why fact table is in normal form? Basically the fact table consists of the Index keys of the dimension/ook up tables and the measures. so when ever we have the keys in a table .that itself implies that the table is in the normal form. What are the advantages data mining over traditional approaches? 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. 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 Data Warehousing Basics Interview Questions and Answers (2) What is a CUBE in datawarehousing concept? Cubes are logical representation of multidimensional data.The edge of the cube contains dimension members and the body of the cube contains data values. What is data validation strategies for data mart validation after loading process ? 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 what is the datatype of the surrgate key ? Datatype of the surrgate key is either inteeger or numaric or number What is degenerate dimension table? Degenerate Dimensions : If a table contains the values, which r neither dimesion nor measures is called degenerate dimensions.Ex : invoice id,empno What is Dimensional Modelling? 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. What are the methodologies of Data Warehousing.? 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.

What is a linked cube? 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. Data Warehousing Basics Interview Questions and Answers (3) What is the main difference between Inmon and Kimball philosophies of data warehousing? 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 What is Data warehosuing Hierarchy? 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. What is the main differnce between schema in RDBMS and schemas in DataWarehouse....? 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 Data Warehousing Basics Interview Questions and Answers (4) What is hybrid slowly changing dimension? 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. What are the different architecture of datawarehouse? There are two main things 1. Top down - (bill Inmon) 2.Bottom up - (Ralph kimbol) 1.what is incremintal loading? 2.what is batch processing? 3.what is crass reference table? 4.what is aggregate fact table? 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. what is junk dimension? what is the difference between junk dimension and degenerated dimension? 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..

What are the possible data marts in Retail sales.? Product information,sales information What is the definition of normalized and denormalized view and what are the differences between them? Normalization is the process of removing redundancies. Denormalization is the process of allowing redundancies. What is meant by metadata in context of a Datawarehouse and how it is important? 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. Differences between star and snowflake schemas? Star schema A single fact table with N number of Dimension Snowflake schema Any dimensions with extended dimensions are know as snowflake schema 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? 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 . What is VLDB? The perception of what constitutes a VLDB continues to grow. A one terabyte database would normally be considered to be a VLDB. What's the data types present in bo?n what happens if we implement view in the designer n report 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. can a dimension table contains numeric values? Yes.But those datatype will be char (only the values can numeric/char) What is the difference between view and materialized view? 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. What is surrogate key ? where we use it expalin with examples 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. 2. 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. 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. What is aggregate table and aggregate fact table ... any examples of both? 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. What is active data warehousing? 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 Why do we override the execute method is struts? Plz give me the details? 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. What is the difference between Datawarehousing and BusinessIntelligence? 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. What is the difference between OLAP and datawarehosue? 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. 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 Why Denormalization is promoted in Universe Designing?

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. What is the difference between ODS and OLTP? 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 What is the difference between datawarehouse and BI? 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. Is OLAP databases are called decision support system ??? true/false? True explain in detail about type 1, type 2(SCD), type 3 ? Type-1 Most Recent Value Type-2(full History) i) Version Number ii) Flag iii) Date Type-3 Current and one Perivies value What is snapshot? 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. What is the difference between datawarehouse and BI? 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. What are non-additive facts in detail? 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'

Connected Lookup Receives input values directly from the pipeline.

Unconnected Lookup Receives input values from the result of a :LKP expression in another transformation. Use a dynamic or static cache. Use a static cache. Cache includes the lookup source columns in the Cache includes all lookup/output ports lookup condition and the lookup source columns that in the lookup condition and the are output ports. lookup/return port. Can return multiple columns from the same row or Designate one return port (R). Returns insert into the dynamic lookup cache. one column from each row. If there is no match for the lookup condition, the If there is no match for the lookup Integration Service returns the default value for all condition, the Integration Service output ports. If you configure dynamic caching, the returns NULL. Integration Service inserts rows into the cache or leaves it unchanged. If there is a match for the lookup condition, the If there is a match for the lookup Integration Service returns the result of the lookup condition, the Integration Service condition for all lookup/output ports. If you returns the result of the lookup configure dynamic caching, the Integration Service condition into the return port. either updates the row the in the cache or leaves the row unchanged. Pass multiple output values to another Pass one output value to another transformation. Link lookup/output ports to another transformation. The transformation. lookup/output/return port passes the value to the transformation calling :LKP expression. Supports user-defined default values. Does not support user-defined default values.

Currently I am working as a Informatica developer at CNSI. I am working here from past two years and I am working in the data warehouse team. In CNSI we are using Informatica As the ETL tool .So, I mostly work with Informatica and also the data bases like Oracle, sql server.
Before CNSI, I worked for Capital BlueCross, Pittsburg, PA for more than 1 year.

When comes to overall experience:

I have been working in DW technologies from more than 6years. I worked in different DW projects. I got the opportunity to work with different phases of the projects. For some projects I worked for the complete life cycle and for some projects I worked for enhancements and for some projects I worked for ongoing Production support.

What we did with this project is we have a Mainframe IDMS source. We extract the data from this source using Informatica Power exchange for IDMS. Then Transform the data based on business rules and we load the data to Oracle DW. Business use BO and Cognos as reporting tools to report this data.

Data cleaning: As the data in DW plays an important role in decision making. It is my responsibility as an ETL developer to provide a accurate data to DW. This is what I do. In the ETL mapping first I write the code to identify incomplete, incorrect or inaccurate data then correct the data or modify the data using the business rules and also provide the default when values for missing data. We make sure the data is consistent before we load the data DW. Like….trimming, lowercase, replacing null, date formats, deleting the typing mistakes

SDLC focused on gathering the business requirements from users. I participated in different meetings with business users and business mangers along with my DW manager and data architect. We had discussions on
Requirements:. In the first phase of the project, We

what is the existing process they are using for reporting? From where we need to bring the data? Where we need to load the data? After several meeting and having the enough study and completing the analysis. We come up with a rough estimate ( ROM). I mostly help the team by estimating the number of hours required for ETL work.

My manager creates the estimated budget we help him with statement of work (SOW). Once the business approves. Design : After business approves the sow we go for design, I mostly work with Data architect, he is the one who knows in and out the both source system and also DW. First we do a high level design with business flow and then go for low level with source to target data mapping. Once this is completed we review with business to make sure we are on right path. Then I for Coding: I do the complete coding I may use Informatica or stored procedures or batch scripts. I use the deliverables of the design phase for developing. I also write the test scripts. using these test scripts I do unit testing. Example………….Like comparing the number of rows in the source with target How the data transforms with different cases like case1 , case2…etc I always prefer to do unit testing along with the coding and also I always document the errors Testing : Once the unit testing is done I move the code to test box for integration testing, and also for the user acceptance. In this phase I make sure this coding actually solving the business need what we gathered during the requirement phase The last step is Migration to production: Here we will have more meetings with other teams and DBA’s where we have dependency and then finally I move the code to production. I also worked for the ongoing support

Summary:

I have been working in Data Warehousing technologies from more than 6years. I worked in different DW projects. Currently I am working as an Informatica developer at CNSI. I am working here from past two years and I am working in the data warehouse team. In CNSI we are using Informatica as the ETL tool. So, I mostly work with Informatica and also the data bases like DB2, Oracle, sql server.

I have good experience with Informatica Power center 6.x/7.x/8.x/ Power Exchange for ETL (Extraction, Transformation, and Loading) of data from different database sources to Data Warehouse in UNIX/Windows environment. I have strong working experience of Informatica Repository Administrator console, Repository Manager, Workflow Manager and Designer. In most of my projects I extracted data from several sources like DB2, Oracle, SQL Server. I have good experience with the DB2 databases.

Cigna Health Insurance, Philadelphia, PA 1601 Chestnut St # 1, Philadelphia, PA (215) 761-8400 Near City Hall

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