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What is real time data-warehousing?
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.
What are conformed dimensions?
Conformed dimensions mean the exact same thing with every possible fact
table to which they are joined. They are common to the cubes.
What is conformed fact?
Conformed dimensions are the dimensions which can be used across multiple
Data Marts in combination with multiple facts tables accordingly.
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. 100 years may be represented in a
time dimension, with one row per day.
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. Level of granularity would mean what detail are you
willing to put for each transactional fact.
What are non-additive facts?
Non-additive facts are facts that cannot be summed up for any of the
dimensions present in the fact table. However they are not considered as
useless. If there is changes in dimensions the same facts can be useful.
What is factless facts table?
A fact table which does not contain numeric fact columns it is called
factless facts table.
What are slowly changing dimensions
(SCD)?
SCD is abbreviation of Slowly changing dimensions. SCD applies to cases
where the attribute for a record varies over time.
There are three different types of SCD.
1) SCD1 : The new record replaces the original record. Only one record
exist in database – current data.
2) SCD2 : A new record is added into the customer dimension table. Two
records exist in database – current data and previous history data.
3) SCD3 : The original data is modified to include new data. One record
exist in database – new information are attached with old information in same
row.
What is hybrid slowly changing
dimension?
Hybrid SCDs are combination of both SCD 1 and SCD 2. It may happen that
in a table, some columns are important and we need to track changes for them
i.e capture the historical data for them whereas in some columns even if the
data changes, we don’t care.
What is BUS Schema?
BUS Schema is composed of a master suite of confirmed dimension and
standardized definition if facts.
What is a Star Schema?
Star schema is a type of organizing the tables such that we can retrieve
the result from the database quickly in the warehouse environment.
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.
Differences between star and
snowflake schema?
Star schema – A single fact table with N number of Dimension, all
dimensions will be linked directly with a fact table. This schema is
de-normalized and results in simple join and less complex query as well as
faster results.
Snow schema – Any dimensions with extended dimensions are know as
snowflake schema, dimensions maybe interlinked or may have one to many
relationship with other tables. This schema is normalized and results in
complex join and very complex query as well as slower results.
What is Difference between ER
Modeling and Dimensional Modeling?
ER modeling is used for normalizing the OLTP database design. Dimensional
modeling is used for de-normalizing the ROLAP/MOLAP design.
What is degenerate dimension table?
If a table contains the values, which is neither dimension nor measures
is called degenerate dimensions.
Why is Data Modeling Important?
Data modeling is probably the most labor intensive and time consuming
part of the development process. 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.
In computer science, data modeling is the process of creating a data
model by applying a data model theory to create a data model instance. A data
model theory is a formal data model description. When data modeling, we are
structuring and organizing data. These data structures are then typically
implemented in a database management system. In addition to defining and
organizing the data, data modeling will impose (implicitly or explicitly)
constraints or limitations on the data placed within the structure.
Managing large quantities of structured and unstructured data is a
primary function of information systems. Data models describe structured data
for storage in data management systems such as relational databases. They
typically do not describe unstructured data, such as word processing documents,
email messages, pictures, digital audio, and video. (Reference : Wikipedia)
What is surrogate key?
Surrogate key is a substitution for the natural primary key. It is just a
unique identifier or number for each row that can be used for the primary key
to the table. The only requirement for a surrogate primary key is that it is
unique for each row in the table. It is useful because the natural primary key
can change and this makes updates more difficult. Surrogated keys are always
integer or numeric.
What is Data Mart?
A data mart (DM) is a specialized version of a data warehouse (DW). Like
data warehouses, data marts contain a snapshot of operational data that helps
business people to strategize based on analyses of past trends and experiences.
The key difference is that the creation of a data mart is predicated on a
specific, predefined need for a certain grouping and configuration of select
data. A data mart configuration emphasizes easy access to relevant information
(Reference : Wiki). Data Marts are designed to help manager make strategic
decisions about their business.
What is the difference between OLAP
and data warehouse?
Data warehouse is the place where the data is stored for analyzing where
as OLAP is the process of analyzing the data, managing aggregations,
partitioning information into cubes for in depth visualization.
What is a Cube and Linked Cube with reference to data warehouse?
Cubes are logical representation of multidimensional data. The edge of
the cube contains dimension members and the body of the cube contains data
values. The linking in cube ensures that the data in the cubes remain
consistent.
What is junk dimension?
A number of very small dimensions might be lumped together to form a single
dimension, a junk dimension – the attributes are not closely related. Grouping
of Random flags and text attributes in a dimension and moving them to a
separate sub dimension is known as junk dimension.
What is snapshot with reference to
data warehouse?
You can disconnect the report from the catalog to which it is attached by
saving the report with a snapshot of the data.
What is active data warehousing?
An active data warehouse provides information that enables
decision-makers within an organization to manage customer relationships nimbly,
efficiently and proactively.
What is the difference between data
warehousing and business intelligence?
Data warehouses deals with all aspects of managing the development,
implementation and operation of a data warehouse or data mart including meta
data management, data acquisition, data cleansing, data transformation, storage
management, data distribution, data archiving, operational reporting,
analytical reporting, security management, backup/recovery planning, etc.
Business intelligence, on the other hand, is a set of software tools that
enable an organization to analyze measurable aspects of their business such as
sales performance, profitability, operational efficiency, effectiveness of
marketing campaigns, market penetration among certain customer groups, cost
trends, anomalies and exceptions, etc. Typically, the term “business
intelligence” is used to encompass OLAP, data visualization, data mining and
query/reporting tools.
