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 non-keyattributes
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.
