Data cleaning is also known as data scrubbing. Data cleaning is a process which ensures the set of data is correct and accurate. Data accuracy and consistency, data integration is checked during data cleaning. Data cleaning can be applied for a set of records or multiple sets of data which need to be merged.

Data cleaning is performed by reading all records in a set and verifying their accuracy. Typos and spelling errors are rectified. Mislabeled data if available is labeled and filed. Incomplete or missing entries are completed. Unrecoverable records are purged, for not to take space and inefficient operations.

At the time of updating the data warehouse, a lookup table is used. When placed on the fact table or warehouse based upon the primary key of the target, the update is takes place only by allowing new records or updated records depending upon the condition of lookup.

The materialized views are aggregate tables. It contains summarized data. For example, to generate sales reports on weekly or monthly or yearly basis instead of daily basis of an application, the date values are aggregated into week values, week values are aggregated into month values and month values into year values. To perform this process aggregate function is used.

The primary keys of entity tables are the foreign keys of dimension tables.

The Primary keys of fact dimensional table are the foreign keys of fact tablels.

Data Mart is a data repository which is served to a community of people who works on knowledge (also known as knowledge workers). The data resource can be from enterprise resources or from a data warehouse.

Dimensional modelling is very flexible for the user perspective. Dimensional data model is mapped for creating schemas. Where as ER Model is not mapped for creating shemas and does not use in conversion of normalization of data into denormalized form.

ER Model is utilized for OLTP databases that uses any of the 1st or 2nd or 3rd normal forms, where as dimensional data model is used for data warehousing and uses 3rd normal form.

ER model contains normalized data where as Dimensional model contains denormalized data.

View:

- Tail raid data representation is provided by a view to access data from its table.

- It has logical structure can not occupy space.

- Changes get affected in corresponding tables.

Materialized view

- Pre calculated data persists in materialized view.

- It has physical data space occupation.

- Changes will not get affected in corresponding tables.

Star schema: A highly de-normalized technique. A star schema has one fact table and is associated with numerous dimensions table and depicts a star.

Snow flake schema: The normalized principles applied star schema is known as Snow flake schema. Every dimension table is associated with sub dimension table.

Differences:

- A dimension table will not have parent table in star schema, whereas snow flake schemas have one or more parent tables.

- The dimensional table itself consists of hierarchies of dimensions in star schema, where as hierarchies are split into different tables in snow flake schema. The drilling down data from top most hierarchies to the lowermost hierarchies can be done.

Dimensional modeling is often used in Data warehousing. In simpler words it is a rational or consistent design technique used to build a data warehouse. DM uses facts and dimensions of a warehouse for its design. A snow and star flake schema represent data modeling.