Download What`s A Data warehouse

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.
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.
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.
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–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 nonkeyattributes
3NF: The Table is already in 2NF, Contains no transitivedependencies
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
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.
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.
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.
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.
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
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.
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
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'