Q)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 controlled redundancies to link tables
2.Normalization is the analysis of functional dependency between attributes / data items of user views? I t reduces a complex user view to a set of small and stable 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 non-key attributes
3NF: The Table is already in 2NF, Contains no transitive dependencies
1NF Eliminate Repeating Groups - Make a separate table for each set of related attributes, and give each table a primary key.
1. Eliminate Repeating Groups. - 1NF
Make a separate table for each set of related attributes, and give each table a primary key.
2. Eliminate Redundant Data - 2NF
If an attribute depends on only part of a multi-valued key, remove it to a separate table.
3. Eliminate Columns Not Dependent On Key -3 NF
If attributes do not contribute to a description of the key, remove them to a separate table.
4 BCNF
If there are non-trivial dependencies between candidate key attributes, separate them out into distinct tables.
BCNF covers very specific situations where 3NF misses inter-dependencies between non-key (but candidate key) attributes. Typically, any relation that is in 3NF is also in BCNF. However, a 3NF relation won't be in BCNF if (a) there are multiple candidate keys, (b) the keys are composed of multiple attributes, and (c) there are common attributes between the keys.
5 Isolate Independent Multiple Relationships - 4 NF
This applies primarily to key-only associative tables, and appears as a ternary relationship, but has incorrectly merged 2 distinct, independent relationships.
6 . Isolate Semantically Related Multiple Relationships – 5 NF
Q)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
Q)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.
Q)What are Conformed Facts?
Two facts are conformed if they have the same name, units, and definition
If two facts are do not represent the same thing to the business, then they must be given different names
Conformed fact is a fact which is used in more than one fact table.
In addition to conformed dimensions, you need conformed facts. Conforming a fact really amounts to standardizing the definitions of terms across individual marts. Often, different divisions or departments use the same term in different ways. Does “revenue” refer to “gross revenue” or “adjusted revenue”? Does “units shipped” refer to cases of items or individual items?
Q)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
Q)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.
Q)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.
Q)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.
Q)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.
Q)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
Q)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 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.
Q)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.
Q)What are modeling tools available in the Market?
here are a number of data modeling tools
There are a number of data modeling tools to transform business requirements into logical data model, and logical data model to physical data model. From physical data model, these tools can be instructed to generate sql code for creating database.
Tool Name Company Name
Erwin Computer Associates
Embarcadero Embarcadero Technologies
Rational Rose IBM Corporation
Power Designer Sybase Corporation
Oracle Designer Oracle Corporation
Xcase RESolution LTD.
Q)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.
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 are the vaious ETL tools in the Market?
ETL Tools are meant to extract, transform and load the data into Data Warehouse for decision making. Before the evolution of ETL Tools, the above mentioned ETL process was done manually by using SQL code created by programmers. This task was tedious and cumbersome in many cases since it involved many resources, complex coding and more work hours. On top of it, maintaining the code placed a great challenge among the programmers.
These difficulties are eliminated by ETL Tools since they are very powerful and they offer many advantages in all stages of ETL process starting from extraction, data cleansing, data profiling, transformation, debuggging and loading into data warehouse when compared to the old method.
There are a number of ETL tools available in the market to do ETL process the data according to business/technical requirements. Following are some those.
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
Popular ETL Tools
Tool Name Company Name
Informatica Informatica Corporation
DT/Studio Embarcadero Technologies
DataStage IBM
Ab Initio Ab Initio Software Corporation
Data Junction Pervasive Software
Oracle Warehouse Builder Oracle Corporation
Microsoft SQL Server Integration Microsoft
TransformOnDemand Solonde
Transformation Manager ETL Solutions
Q)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.
Answer 3
The perception of what constitutes a VLDB continues to grow. A one terabyte database would normally be considered to be a VLDB.
Q)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
Q)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.
Q)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.
Q)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
Q)what is the datatype of the surrgate key ?
Datatype of the surrgate key is either inteeger or numaric or number
Q)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
Q)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.
Q)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
Q)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.
Q)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
Q)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.
Q)What are the different architecture of datawarehouse?
There are two main things
1. Top down - (bill Inmon)
2.Bottom up - (Ralph kimbol)
Q)What is an incremental loading ?
Incremental loading means loading the ongoing changes in the OLTP.
Q)What is a Degenerated 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.?
Product information,sales information
Q)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.
Q)can a dimension table contains numeric values?
Yes.But those datatype will be char (only the values can numeric/char)
Q)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.
Q)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
Q)What is the difference between
Datawarehousing and BusinessIntelligence?
Answer 1
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.
Answer 2
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 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 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
Q)Is OLAP databases are called decision support system ??? true/false?
True
Q)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
Q)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'
Q)Types of facts ?
•Additive: Additive facts are facts that can be summed up through all of the dimensions in the fact table.
•Semi-Additive: Semi-additive facts are facts that can be summed up for some of the dimensions in the fact table, but not the others.
•Non-Additive: Non-additive facts are facts that cannot be summed up for any of the dimensions present in the fact table.
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