MBA600 Assessment 2: Analyzing Data Warehousing Strategic Problems

This report discusses the process of data warehousing, emphasizing the integration of data from diverse sources into a unified system. It identifies major problems such as lack of data sources, ETL tool inefficiencies, real-time alerting issues, scalability and query contention, OLAP query conflicts, and challenges in enabling real-time ETL and data modeling. The report highlights the difficulties in extracting, transforming, loading, and cleansing data in real-time, as well as the complexities of integrating real-time data into existing data warehouse structures. It underscores the importance of addressing these challenges to ensure consistent, rapid analysis of historical data and effective support for business, finance, risk management, and solvency areas. Desklib offers a platform for students to access similar solved assignments and past papers.

Data Warehousing
Data warehousing depends on the
integration of the data from the
diverse sources into one system.
.
Data
Warehousing
Scalability & Query
Contention
Another most difficult issues
faced in the data warehousing is
the issues with scalability and
query contention. It affects the
deployment of real-time data
warehouse in which it is essential
to make the real-time and well-
planned structure and then testing
it thoroughly. It is essential to
clarifying the data warehousing in
real-time is becoming much
necessary. The use of the
appropriate tools, approaches,
advices, and real-time data can
resolve the issue and make it
easier in the future.
Enabling the Real-Time ETL
development of the data
warehousing face the
difficulties in extracting,
transforming, loading, and
cleansing data from the
appropriate source system. It
is noticeable that the
performance of the ETL data
in the real-time introduces
some of the additional
challenges.
Process of Data
Warehousing
The data warehousing process deals
with specific codes and programs
that are associated with the entire
programming. The file is the
collection of the data in different
formats, which contains the codes of
the data warehousing. The
development of the data
warehousing delivery solutions
creates the basis for the consistent
and sufficiently rapid analysis of the
historical data.
warehouse implementation is based
support users who are associated with
the business areas, finance and
controlling areas, and risk
management and solvency areas.
Mismanagement in these processes
creates the significant problems in the
data warehousing process.
Major Problems in Data
Warehousing
lack of the data
sources
ETL Tools
It is based on the off-the-shelf
products that are operated in
a batch mode. There is the
probability of extracting files
on a certain schedule, which
makes the data available at
any point of time.
Real-Time Alerting
The distribution of reports in the
email versions is associated with
the issues with the alerting
applications. After loading the
data, it is noticeable that the
availability if the real-time data
creates the alerting applications
in a much appealing way. The
mismanagement of the schedule
can affect the data warehousing
storage in a significant manner
OLAP Queries vs.
Changing Data
The Query tools and OLAP system are
designed for operating on the top of static data and
unchanging information. Apparently, the underlying
data cannot be changed due to which they do not
take any precautions for ensuring that the outcome
would not negatively impacts on execution of
emerging queries. This mismanagement can lead to
the confused query results and inconsistencies.
Modeling Real-time Fact
Tables
Introducing a real-time data into an already
existing data warehouse creates the
considerable issues in the data modeling
process. For instance, a data warehouse
generally includes the data aggregated at
different levels, which depend on the time
dimension. This dimension needs to consider
the possible aggregated information, which
might be out of proper synchronization with
the associated real-time data. The issues are
generally occurring when there is the lack of
knowledge regarding the place where the
real-time data is stored and identifying the
best link to the rest of data model.