Data Warehouse Manager Interview Questions & Answers

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Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. A data warehouse is often built to enable Data Analytics

Conformed fact is a table which can be used across multiple data marts in combined with the multiple fact tables.

Real-time datawarehousing captures the business data whenever it occurs. When there is business activity gets completed, that data will be available in the flow and become available for use instantly.

Some applications include:
☛ financial services
☛ banking services
☛ customer goods
☛ retail sectors
☛ controlled manufacturing

Foreign keys of dimension tables are primary keys of entity tables. Foreign keys of fact tables are the primary keys of the dimension tables.

Dimensional model consists of dimension and fact tables. Fact tables store different transactional measurements and the foreign keys from dimension tables that qualifies the data. The goal of Dimensional model is not to achieve high degree of normalization but to facilitate easy and faster data retrieval.

Ralph Kimball is one of the strongest proponents of this very popular data modeling technique which is often used in many enterprise level data warehouses.

Aggregate tables are the tables which contain the existing warehouse data which has been grouped to certain level of dimensions. It is easy to retrieve data from the aggregated tables than the original table which has more number of records.

This table reduces the load in the database server and increases the performance of the query.

Following needs to be done when the database is shutdown:

☛ Close the database
☛ Dismount the database
☛ Shutdown the Instance

VLDB is abbreviated as Very Large Database and its size is set to be more than one terabyte database. These are decision support systems which is used to server large number of users.

Dimensional Modeling is a concept which can be used by dataware house designers to build their own datawarehouse. This model can be stored in two types of tables – Facts and Dimension table.

Fact table has facts and measurements of the business and dimension table contains the context of measurements.

A load manager extracts data from the source system. Fast load the extracted data into temporary data store. Perform simple transformations into structure similar to the one in the data warehouse.

Following are the different types of Datawarehousing:

☛ Enterprise Datawarehousing
☛ Operational Data Store
☛ Data Mart

Snowflake schema which has primary dimension table to which one or more dimensions can be joined. The primary dimension table is the only table that can be joined with the fact table.

ODS is abbreviated as Operational Data Store and it is a repository of real time operational data rather than long term trend data.

ER diagram is abbreviated as Entity-Relationship diagram which illustrates the interrelationships between the entities in the database. This diagram shows the structure of each tables and the links between the tables.

A data warehouse helps to integrate data and store them historically so that we can analyze different aspects of business including, performance analysis, trend, prediction etc. over a given time frame and use the result of our analysis to improve the efficiency of business processes.

In datawarehousing, loops are existing between the tables. If there is a loop between the tables, then the query generation will take more time and it creates ambiguity. It is advised to avoid loop between the tables.

Time dimensions are usually loaded through all possible dates in a year and it can be done through a program. Here, 100 years can be represented with one row per day.

Following need to be done to start the database:

☛ Start an Instance
☛ Mount the database
☛ Open the database

A load manager performs the operations required to extract and load the process. The size and complexity of load manager varies between specific solutions from data warehouse to data warehouse.

Following are the ETL tools available:

☛ Informatica
☛ Data Stage
☛ Oracle
☛ Warehouse Builder
☛ Ab Initio
☛ Data Junction

Core dimension is nothing but a Dimension table which is used as dedicated for single fact table or datamart.

Following are the differences between OLTP and OLAP:

OLTP:
Data is from original data source
Simple queries by users
Normalized small database
Fundamental business tasks

OLAP:
Data is from various data sources
Complex queries by system
De-normalized Large Database
Multi-dimensional business tasks

Fact table allows the measurement and the values of the facts of the data to be contained inside the table. This table consists of the foreign keys and primary keys of the dimension tables. It is located in between the star schema or snowflake schema. It provides values that are additive and independent variables through which the dimensional attributes are analyzed. This table consists of the grains, which consist of atomic level of data and through which the facts in the tables are defined. Each record defines the independent facts that provide higher level of data to be given to the user. It is useful in representing the data due to easy storage and less memory to be taken to the facts of the data that are associated with it.

Partitioning is done for various reasons such as easy management, to assist backup recovery, to enhance performance.