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What Snow Flake Schema
Answered by Girinath.S.V.S on 2005-03-17 06:40:48: Snowflake schemas normalize dimensions to eliminate redundancy. That is, the dimension data has been grouped into multiple tables instead of one large table. For example, a product dimension table in a star schema might be normalized into a products table, a product_category table, and a product_manufacturer table in a snowflake schema. While this...
prodyot.sarkar
- May 13th, 2010
Snow-flake schema is a logical arrangement of tables in dimensional modeling where a centralized fact table references number of other dimension tables; however, those dimension tables are further nor...
harshadapj
- Jul 22nd, 2008
Closely related to the star schema, the snowflake schema is represented by centralized fact tables which are connected to multiple dimensionsIn the snowflake schema, however, dimensions are normalized...
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What is the main differnce between schema in RDBMS and schemas in DataWarehouse....?
Sam Kolta
- Apr 17th, 2010
User It is just an account name for the user Owns a schema Have the same name Create user command, create the user User can be considered schema User can created without schema It is the account for t...
senthil kumar B
- Feb 13th, 2006
Diff b.w OLTP and OLAP :------------------------OLTP Schema :* Normalized * More no.of trans* Less time for queries execution* More no.of users* Have Insert,delete and update trans. OLAP (D...
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How are the Dimension tables designed
divya29july
- Feb 11th, 2010
Dimension table contain the textual descriptors of the business. In a well designed dimemsion model, dimension table has many columns or attributes. Fields that are discretely valued description that ...
Kenny
- Jul 19th, 2005
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). Change fact table and DW population routines.
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What are Semi-additive and factless facts and in which scenario will you use such kinds of fact tables
divya29july
- Feb 11th, 2010
Factless Fact table:Consider an example of Product on promotion, the fact table will contain information of only the products that were on sale and sold but not of those that did not sell as the fact ...
siraj.khan
- Aug 3rd, 2007
Semi-additive facts are those facts which can be aggregate according to the context. so, it can contain a fact but the factless fact table does not contain fact's but it contains the dimmentions. It's a type of a dimmentions.
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opposite is true?
">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?
justins707
- Jan 11th, 2010
When we refer to Star and Snowflake Schemas, we are talking about a dimensional model for a Data Warehouse or a Datamart. The Star schema model gets it name from the design appearance because th...
Chris
- Mar 29th, 2007
Star Schemas generally contain fact tables with fully denormalised dimension tables connecting directly to the fact table. A snowflake schema is a modification of this approach whereby the dimension t...
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What is the life cycle of data warehouse projects
idanh79
- Dec 7th, 2009
1. project planning2. business requirement definition3. dimension modeling+ technical architecture design4. pysical design+ product selection5. data staging design and development+end-user application development6. deployment+QAIdan Halfon
sulee
- Aug 22nd, 2007
STRAGEGY & PROJECT PLANNINGDefinition of scope, goals, objectives & purpose, and expectationsEstablishment of implementation strategyPreliminary identification of project resourcesAssem...
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Can you convert a snowflake schema in to star schema?
idanh79
- Dec 1st, 2009
Yes, you can do it by denormalization of the dimensions into one (and wide) dimension, it is depends of your needs.Idan Halfon
sixto.dsilva
- Sep 25th, 2008
Yes right said both model are designed for different purpose , I think star schema is very informative for business users unlike snowflake which is bit complicated with lot of small , small tables. If...
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Bridge Table
What is the use of bridge table. Can give a simple example?
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Editorial / Best Answer
Sirini- Member Since Jun-2009 | Jun 14th, 2009
An example of a Bridge table would be relationship table between student_master and courses_master. A Student may participate in multiple courses and obviously in a course there will be multiple students. In this scenario to maintain relationships between student_master and courses_master we need another table, which may be called, Student_courses. This table will have all relationships between Students and Courses.A bridge table is a table that only contains the keys between the two tables in a many-to-many relationship. These tables typically consist of mostly key fields, the primary keys of the two tables needing the many to many relationships. Sometimes there are more data elements, but often there are only two columns, both foreign keys.
vijsarav11
- Oct 19th, 2009
Hi all... This is my view on bridge tables:Bridge table in simple can be called as reference tables. As the name indicates, this table can be used to link two tables with 1 reference id...
tisha24
- Oct 7th, 2009
A table which maintained to define the relationship between two different tables is known as bridge tables. For example, say there are two tables one table with employee information (an employee table...
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What is CGID in normalizer transformation
Please provide me deep info on Normalizer !
Sirini
- Jun 14th, 2009
It is GCID which means Generic Column ID. When a Normalizer transformation is used to read Cobol sources, the Informatica Designer identifies the 'OCCURS' and 'REDEFINES' statements and generates 2 co...
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What is ETL
ETL is extraction,transformation and loading,ETL technology is used for extraction the information from the source database and loading it to the target database with the necessary transformations done in between.
rsp2709
- Jun 8th, 2009
ETL is a process that extracts data from one database, transforms it as per the destination database and loads it into another database. In the transformation process, data is actually standardized to...
jaggu_mandya
- Jan 16th, 2008
Data Acquisition technique is now called ETL(Extraction, Transformation and Loading)Extraction-The process of extracting the data from various sources. Sources can be file system, database, XML file, ...
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ODS and Staging
What is the difference between ODS and Staging?
prat4all
- May 25th, 2009
ODS keeps the transactional data from where direct reporting is possible. The data in ODS can be partly denormalized.Staging area is the exact replica of the transactional database which acts as a source for datawarehouse processing.
shanavaz
- Nov 1st, 2008
ODS, Operational Data Store is majorly different from Staging in the below aspects: - Existing Records in the System can get updated based on necessary instructions & design. - It can be use...
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What is ER Diagram
Answered by Puneet on 2005-05-07 04:21:07: ER - Stands for entitity relationship diagrams. It is the first step in the design of data model which will later lead to a physical database design of possible a OLTP or OLAP database
sukhdev_ce
- Apr 30th, 2009
The ER Diagrams is the initial process to analaysis a database application, and understands the problems related or causes in future with the choosed database application, in this satge different comp...
clickharri
- Apr 22nd, 2008
Entity Relationship Diagrams are a major data modelling tool and will help organize the data in your project into entities and define the relationships There are three basic elements in ER models: Ent...
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What is the differences between the static and dynamic caches?
ramakrishna.inf
- Apr 21st, 2009
Static cache - It doesn't reflect changes once data is cached is known as static cache.
Dynamic cache - It reflect to changes at the time of running the session if any updates happen in source data.jayadhar
- Sep 4th, 2006
static cache stores overloaded values in the memory and it wont change throught the running of the session where as dynamic cache stores the values in the memory and changes dynamically duirng the run...
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What is meant by metadata in context of a Datawarehouse and how it is important?
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kalpit
- Mar 10th, 2009
Metadata is the Information about the Data which is being stored in DWH , Why its imp : 1. To know the flow of source data into Data Warehouse, 2. In case if you have many star schema or many subjec...
thumatinagaraju
- Oct 24th, 2006
Meta data is stored in repository only not in dataware house .. but we r placing our repository in database in that way ur correct ,,but not directly stored in the dataware house plz check it mam
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What is data validation strategies for data mart validation after loading process
kalpit
- Mar 10th, 2009
Data validation is generally done manually in DWH, in this case if source and TGT are relational you need to create SQL scripts to validate source and target data, and if source is Flat file or non re...
uma
- May 3rd, 2006
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
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What type of Indexing mechanism do we need to use for a typical datawarehouse
Answered by on 2005-03-23 01:45:54: bitmap index
ibrarx
- Mar 7th, 2009
Hash Index - If we know that the hashing will result in uniform distribution and doesn't create hotspots (spikes-chaining).Bitmap Index - If the degree of cardinality is high for the attribute, me...
cetzhbo
- Jun 19th, 2007
Space requirements for indexes in a warehouse are often significantly larger than the space needed to store the data, especially for the fact table and particularly if the indexes are B*trees.Hence, y...
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What is a level of Granularity of a fact table
ibrarx
- Mar 7th, 2009
The smallest unit of analysis is called as 'grain'. Also known as the atomic unit of analysis. Deciding the grain and then on basis of it, the aggregation level is called as the granularity of a fact table.
Santanu Patra
- Jun 29th, 2005
It also means that we can have (for example) data agregated for a year for a given product as well as the data can be drilled down to Monthly, weekl and daily basis...teh lowest level is known as the grain. going down to details is Granularity
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What does level of Granularity of a fact table signify
ibrarx
- Mar 7th, 2009
The level of granularity of a fact table signifies the smallest unit of analysis.
ravi kumar guturi
- Mar 9th, 2006
Granularity means nothing but it is a level of representation of measures and metrics.The lowest level is called detailed dataand highest level is called summary dataIt depends of project we extract fact table significanceBye
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Explain Bill Inmon's versus Ralph Kimball's Approach to Data Warehousing.
Bill Inmon vs Ralph Kimball In the data warehousing field, we often hear about discussions on where a person / organization's philosophy falls into Bill Inmon's camp or into Ralph Kimball's camp. We describe below the difference between the two.Bill Inmon's paradigm: Data warehouse is one part of the overall business intelligence system. An enterprise has one data warehouse, and data marts source their...
tisha24
- Mar 3rd, 2009
Bill inmon's approach:--------------------------- Approach is "TOP DOWN approach". ;means Emphasize the data warehouse. Starts with designing an enterprise model f...
sithu
- Nov 19th, 2007
Bill inmon : Data warehouse à Data martRalph Kimbol : Data mart à Data warehouseCheers,Sithu, sithusithu@Hotmail.com
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Transformation keys into generic key by system itself is called as
aquacooldude
- Feb 19th, 2009
The transformation of keys which are supposed to be used as primary key into generic key by the system is called as key restructuring. The problem is something like this, let us a say there was an ex...
Data Warehousing Basics
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