| Data Warehousing Interview Questions |
| What is Data Warehousing? |
A data warehouse is the main repository of an organization's historical data,
its corporate memory. It contains the raw material for management's decision
support system. The critical factor leading to the use of a data warehouse is
that a data analyst can perform complex queries and analysis, such as data
mining, on the information without slowing down the operational systems
Data warehousing collection of data designed to support management decision
making. Data warehouses contain a wide variety of data that present a coherent
picture of business conditions at a single point in time. It is a repository of
integrated information, available for queries and analysis. |
| What are
fundamental stages of Data Warehousing? Offline Operational Databases |
| Data warehouses in this initial stage are developed by
simply copying the database of an operational system to an off-line server
where the processing load of reporting does not impact on the operational
system's performance. Offline Data Warehouse - Data warehouses in this stage of
evolution are updated on a regular time cycle (usually daily, weekly or
monthly) from the operational systems and the data is stored in an integrated
reporting-oriented data structure Real Time Data Warehouse - Data warehouses at
this stage are updated on a transaction or event basis, every time an
operational system performs a transaction (e.g. an order or a delivery or a
booking etc.) Integrated Data Warehouse - Data warehouses at this stage are
used to generate activity or transactions that are passed back into the
operational systems for use in the daily activity of the organization |
| What is Dimensional
Modeling? |
| Dimensional data model concept involves two types of
tables and it is different from the 3rd normal form. This concepts uses Facts
table which contains the measurements of the business and Dimension table which
contains the context(dimension of calculation) of the measurements. |
| What is Fact table? |
| Fact table contains measurements of business process. Fact
table contains the foreign keys for the dimension tables. Example, if you are
business process is "paper production", "average production of paper by one
machine" or "weekly production of paper" will be considered as measurement of
business process. |
| What is Dimension table? |
| Dimensional table contains textual attributes of
measurements stored in the facts tables. Dimensional table is a collection of
hierarchies, categories and logic which can be used for user to traverse in
hierarchy nodes. |
| What are the Different methods of loading
Dimension tables? |
|
There are two different ways to load data in dimension tables.
Conventional (Slow) : All the constraints and keys are validated against
the data before, it is loaded, this way data integrity is maintained.
Direct (Fast) : All the constraints and keys are disabled before the data
is loaded. Once data is loaded, it is validated against all the constraints and
keys. If data is found invalid or dirty it is not included in index and all
future processes are skipped on this data.
|
| What is OLTP? |
| OLTP is abbreviation of On-Line
Transaction Processing. This system is an application that modifies data the
instance it receives and has a large number of concurrent users. |
| What is OLAP? |
| OLAP is abbreviation of Online Analytical Processing. This
system is an application that collects, manages, processes and presents
multidimensional data for analysis and management purposes. |
| What is the difference between OLTP and
OLAP? |
|
Data Source
OLTP: Operational data is from original data source of the data
OLAP: Consolidation data is from various source.
Process Goal
OLTP: Snapshot of business processes which does fundamental business tasks
OLAP: Multi-dimensional views of business activities of planning and decision
making
Queries and Process Scripts
OLTP: Simple quick running queries ran by users.
OLAP: Complex long running queries by system to update the aggregated data.
Database Design
OLTP: Normalized small database. Speed will be not an issue due to smaller
database and normalization will not degrade performance. This adopts entity
relationship(ER) model and an application-oriented database design.
OLAP: De-normalized large database. Speed is issue due to larger database and
de-normalizing will improve performance as there will be lesser tables to scan
while performing tasks. This adopts star, snowflake or fact constellation mode
of subject-oriented database design.
Back up and System Administration
OLTP: Regular Database backup and system administration can do the job.
OLAP: Reloading the OLTP data is good considered as good backup option.
|
| Describes the foreign key columns in fact
table and dimension table |
| Foreign keys of dimension tables are primary
keys of entity tables. Foreign keys of facts tables are primary keys of
Dimension tables. |
| What is Data Mining? |
| Data Mining is the process of analyzing data from
different perspectives and summarizing it into useful information |
| What is the difference between view and
materialized view? |
| A view takes the output of a query and makes
it appear like a virtual table and it can be used in place of tables. A
materialized view provides indirect access to table data by storing the results
of a query in a separate schema object. |
| What is ER Diagram? |
| Entity Relationship Diagrams are a major data
modelling tool and will help organize the data in your project into entities
and define the relationships between the entities. This process has proved to
enable the analyst to produce a good database structure so that the data can be
stored and retrieved in a most efficient manner. An entity-relationship (ER)
diagram is a specialized graphic that illustrates the interrelationships
between entities in a database. A type of diagram used in data modeling for
relational data bases. These diagrams show the structure of each table and the
links between tables. |
| What is ODS? |
| ODS is abbreviation of Operational Data
Store. A database structure that is a repository for near real-time operational
data rather than long term trend data. 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 ETL? |
| ETL is abbreviation of extract, transform,
and load. ETL is software that enables businesses to consolidate their
disparate data while moving it from place to place, and it doesn't really
matter that that data is in different forms or formats. The data can come from
any source.ETL is powerful enough to handle such data disparities. First, the
extract function reads data from a specified source database and extracts a
desired subset of data. Next, the transform function works with the acquired
data - using rules orlookup tables, or creating combinations with other data -
to convert it to the desired state. Finally, the load function is used to write
the resulting data to a target database. |
| What is VLDB? |
| VLDB is abbreviation of Very Large DataBase.
A one terabyte database would normally be considered to be a VLDB. Typically,
these are decision support systems or transaction processing applications
serving large numbers of users. |
| Is OLTP database is design
optimal for Data Warehouse? |
| No. OLTP database tables are normalized and
it will add additional time to queries to return results. Additionally OLTP
database is smaller and it does not contain longer period (many years) data,
which needs to be analyzed. A OLTP system is basically ER model and not
Dimensional Model. If a complex query is executed on a OLTP system, it may
cause a heavy overhead on the OLTP server that will affect the normal business
processes. |
| If de-normalized is improves
data warehouse processes, why fact table is in normal form? |
| Foreign keys of facts tables are primary keys
of Dimension tables. It is clear that fact table contains columns which are
primary key to other table that itself make normal form table. |
| What are lookup tables? |
| A lookup table is the table placed on the
target table based upon the primary key of the target, it just updates the
table by allowing only modified (new or updated) records based on thelookup
condition. |
| What are Aggregate tables? |
| Aggregate table contains the summary of
existing warehouse data which is grouped to certain levels of dimensions. It is
always easy to retrieve data from aggregated tables than visiting original
table which has million records. Aggregate tables reduces the load in the
database server and increases the performance of the query and can retrieve the
result quickly |
| 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. |
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