06 October, 2020
Data warehousing is a process of building a data warehouse. A process includes.
Business analysis and onsite technical coordinates gather the business requirement and technical requirements.
A BRS contains the business requirement which is collected by an analysis.
An SRS contains software and hardware requirement which are collected by senior technical people.
The process of designing the database is called as a data modeling (or) dimensional modeling. A database artechict (or) data modeler designs the warehouse with a set of tables.
An OLAP is a technology that supports the business manager to make a query from the data warehouse. An OLAP provides the gateway between users and the data warehouses.
A data warehouse is known as an OLAP database
Ex:- Cognos, BO’s
A relational database is defined us collection of objects such as tables. Views, procedures, macro’s triggers etc...
Table:- A table is a two-dimensional object where the data can be stored in the form of rows and columns.
view:- A view is like a window into one (or) more table it provides a customized access to the base table and provides
It my a define a subject of rows of table
It my a define a subject of columns of table
The following are the relation databases can be defined to build a data warehouse.
One of the best RDBMS to store massive historical information. Parallel storage, retrieval is Teradata.
It is a process of extracting the relevant business information. Transforming data into a required business format and loading into the target system.
Data acquisition is defined with the following type of process
There are two types of ETL used to build data Acquisition
An ETL application can be developed using some programming language such as SQL, PLSQL
Ex:- SAS base, SAS excess, Teradata, ETL Utilities
ETL applications can be design with simple graphical user interfacing, point, and click techniques.
Ex: Informatics, data stage, abnitio, ODI (oracle data integrated), data services, data manager, SSIS (SQL server integration services)
It is a process of reading the data from various types of source systems. The following are a type of sources which are used to define extraction.
It is the process of transforming data and cleansing the data into the required business format.
The following are data transformation activities take place in the stage
Staging is a temporary memory where the following data transformation activities take place
It is a process of changing inconsistencies and inaccuracy
It’s the process of removing unwanted data from staging
It is a process of deriving new attributes.
Attributes nothing but a table columns.
It’s process of calculating the summary ls from detailed data.
It’s a process of integrating the data from multiple source’s system
There are two type of data merge operation takes places in the staging
It’s process of merging the records horizontally.
It’s process of merging the records vertically when the two sources are having same “meta data” (union)
Meta data means data structures (Two (or) three table column names are same)
It’s the process of inserting the data into a target system. There are two type of data loads
An ETL plan defines extraction, transformation, and loading. An ETL plan is a design with following types of metadata.
It is the structure of source data (or) source table from data extracts.
It is the structure of the garget table to which data load.
It defined the business logic used to for processing the data.
It defined the structure of the data. Which is represented as column name, data type, precision, scale and key’s (primary, foreign key).
An ETL client is a graphical user component where an ETL developer can design ETL plane
An ETL Repository is a brain of an ETL system where you can store metadata such as ETL plants.
It is an ETL Engine that performer extraction transformation and loading.
In informatics, an ETL plan is a call as mapping
In data stage, it is called as job
We are extracting data from source system to load the data into target system first time the records are entered directly into the target system
We are extracting data from the source system and load into targets system first time newly entered the records as well as update the records into the target system
If we want to design ETLPlan we need only metadata.
A data warehouse is a design with the following types of schema.
Who ii. What iii. When iv. Where
Snow flake a large dimension table is spited into one (or) more table to flat and high achy
KPI means key performance indicator
A numeric which is performing key role in the business analysis and estimating the business enterprise in the data warehouse is a fact.
A facts table contain a facts. Facts are numeric
Every numeric is not a fact
Mapping is an ETL plan which extraction – Transaction – Loading.
A Data warehouse is a design with integration of multiple stars schemas or snowflakes schema are both.
A galaxy schema is also known as hybrid schema or constellation schema.
It is a process of joining two fact tables.
A dimension table which can be shared by multiple fact tables is known as conformed dimensions.
A fact table with out any facts is known as fact less fact table.
A dimension which can’t be used to describe key performance indicators is known as junk dimension.
ex:- phone number, fax number, customer address, etc.
A dimension which can be altered over the period is known as the slowly changing dimension. There are three type3s of dimensions.
A type one dimension stores only current data in the target it doesn’t maintain any history.
A type two dimension mentions the full history in the target in each update it inserts a new record in the target.
A type three dimension maintains partial history (current and previous information)
OLAP is nothing but a set of specification (or) technology which client application in retrieving the data from the data warehouse.
OLAP is nothing but an interface (or) get way between the user and database.
DOLAP (Desktop OLAP):-
An OLAP which can query the data from a database which is constructed by using desktop databases like dbase, FoxPro, clipper, etc.
XML file, TXT file, XL there are desktop database.
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