1. What is Data warehouse?
2. what are the types of data warehouses?
4. What is star schema?
5. What is snow flake schema?
6. What are ETL Tools?
7. What are Dimensional table?
9. What is Surrogate key?
10. What is Data Mining?
11. What is Operational Data Store?
12. What is the Difference between OLTP and OLAP?
13. How many types of dimensions are available in Informatica?
14. What is Difference between ER Modeling and Dimensional Modeling?
15.What is the maplet?
16.What is Session and Batches?
17. What are slowly changing dimensions?
18. What are 2 modes of data movement in Informatica Server?
19. What is the difference between Active and Passive transformation?
20. What is the difference between connected and unconnected transformation?
21. What are different types of transformations available in Informatica?
23. What are Expression transformation?
24. What are Filter transformation?
25. What are Joiner transformation?
According to Bill Inmon, known as father of Data warehousing. “A Data warehouse is a subject oriented, integrated ,time variant, non volatile collection of data in support of management’s decision making process”.
There are three types of data warehouses3. What is Data mart?
Enterprise Data Warehouse
ODS (operational data store)
Data Mart
A data mart is a subset of data warehouse that is designed for a particular line of business, such as sales, marketing, or finance. In a dependent data mart, data can be derived from an enterprise wide data warehouse. In an independent data mart can be collected directly from sources.
A star schema is the simplest form of data warehouse schema that consists of one or more dimensional and fact tables.
A Snowflake schema is nothing but one Fact table which is connected to a number of dimension tables, The snowflake and star schema are methods of storing data which are multidimensional in nature.
ETL Tools are stands for Extraction, Transformation, and Loading the data into the data warehouse for decision making. ETL refers to the methods involved in accessing and manipulating source data and loading it into target database.
Dimension tables contain attributes that describe fact records in the fact table.8. What is data Modelling?
Data Modeling is representing the real world set of data structures or entities and their relationship in their of data models, required for a database.Data Modelling consists of various types like :
Conceptual data modeling
Logical data modeling
Physical data modeling
Enterprise data modeling
Relation data modeling
Dimensional data modeling.
Surrogate key is a substitution for the natural primary key. It is just a unique identifier or number of each row that can be used for the primary key to the table.
A Data Mining is the process of analyzing data from different perpectives and summarizing it into useful information.
A ODS is an operational data store which comes as a second layer in a data warehouse architecture. It has got the characteristics of both OLTP and DSS systems.
OLTP is nothing but On Line Transaction Processing which contains a normalised tables.
But OLAP(Online Analtical Programming) contains the history of OLTP data which is non-volatile acts as a Decisions Support System.
There are three types of dimensions available are :
Junk dimension
Degenerative Dimension
Conformed Dimension
ER Modeling is used for normalizing the OLTP database design.
Dimesional modeling is used for de-normalizing the ROLAP / MOLAP design.
Maplet is a set of transformations that you build in the maplet designer and you can use in multiple mapings.
Session: A session is a set of commands that describes the server to move data to the target.
Batch: A Batch is set of tasks that may include one or more numbar of tasks (sessions, email, command, etc).
Dimensions that change overtime are called Slowly Changing Dimensions (SCD).
Slowly Changing Dimension-Type1 : Which has only current records.
Slowly Changing Dimension-Type2 : Which has current records + historical records.
Slowly Changing Dimension-Type3 : Which has current records + one previous records.
There are two modes of data movement are:
Normal Mode in which for every record a separate DML stmt will be prepared and executed.
Bulk Mode in which for multiple records DML stmt will be preapred and executed thus improves performance.
Active Transformation:An active transformation can change the number of rows that pass through it from source to target i.e it eliminates rows that do not meet the condition in transformation.
Passive Transformation:A passive transformation does not change the number of rows that pass through it i.e it passes all rows through the transformation.
Connected Transformation:Connected transformation is connected to other transformations or directly to target table in the mapping.
Unconnected Transformation:An unconnected transformation is not connected to other transformations in the mapping. It is called within another transformation, and returns a value to that transformation.
There are various types of transformations available in Informatica :22. What are Aggregator Transformation?
Aggregator
Application Source Qualifier
Custom
Expression
External Procedure
Filter
Input
Joiner
Lookup
Normalizer
Output
Rank
Router
Sequence Generator
Sorter
Source Qualifier
Stored Procedure
Transaction Control
Union
Update Strategy
XML Generator
XML Parser
XML Source Qualifier
Aggregator transformation is an Active and Connected transformation. This transformation is useful to perform calculations such as averages and sums (mainly to perform calculations on multiple rows or groups).
Expression transformation is a Passive and Connected transformation. This can be used to calculate values in a single row before writing to the target.
Filter transformation is an Active and Connected transformation. This can be used to filter rows in a mapping that do not meet the condition.
Joiner Transformation is an Active and Connected transformation. This can be used to join two sources coming from two different locations or from same location.
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