Data Warehouse and DBMS solutions

This assignment focuses mainly on the software that are used to handle the huge amount of data in the data warehouse and in the data management systems. A number of solutions are present in the market to deal with big data. For example, Teradata, Oracle, Amazon web services, Cloudera and lot more. The following paragraphs will evaluate and comparatively analyze them.

Running head: DATA WAREHOUSE
Data Warehouse and DBMS solutions
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1DATA WAREHOUSE
This assignment focuses mainly on the software that are used to handle the huge amount
of data in the data warehouse and in the data management systems. A number of solutions are
present in the market to deal with big data. For example, Teradata, Oracle, Amazon web
services, Cloudera and lot more. The following paragraphs will evaluate and comparatively
analyze them.
Evaluation and Comparative Analysis of the solutions available in the market:
This assignment will deal with four solutions to data warehouse and DBMS. They are
Oracle, Teradata, Amazon Web Services and Cloudera. Oracle is the domestic name of
relational database and data warehousing. The standard version of Oracle that is used in the
industry is Oracle Database 12c that eliminates the need of creating extra copy in separate
analytical data store as revealed in Gartner’s report (2014, cited in Herschel, 2015). The software
makes use of the Oracle Exadata Machine platform, which offers simplicity and scalability.
Advanced features like the Flash Storage and Hybrid Columnar Compression (HCC) are also
included. According to Gartner’s report, more than 390,000 customers use Oracle. However,
Teradata tops the chart of Gartner’s 2014 Magic Quadrant for Data Warehouse Data
Management Systems as the leading software that provides solution to the problems of data
warehousing and database management systems (Mousannif et al., 2016). The platform used is
Teradata’s enterprise data warehouse (EDW) that provides new tools and innovation
opportunities. It also provides analytics to mounds of relational and non-relational data to real-
time business trends and opportunities. Cloud- based DBMS solution is also offered with the
help of Aster database platform. According to Gartner’s report, Teradata has more than 1200
customers. On contrary to Teradata, the concept of Data warehousing and databases was
completely shifted to cloud computing is the past few years by the Amazon Web Services. For

2DATA WAREHOUSE
data transportation between the AWS data and services, AWS Data Pipeline technology is used.
Further, to provide manageable Hadoop solution, Elastic MapReduce platform is implemented
on top of the AWS services platform (Iordache et al., 2013). According to Gartner’s report,
AWS is the leader in DBMS solutions for providing customer satisfaction and experiences.
Cloudera emerged as data processing solutions in the recent years. It owns the Enterprise Data
Hub framework for variety of data warehouses. It mainly focuses on batch processing and
advanced analytics along with simplicity, data protection and management that enterprises
require. According to Gartner’s report, Cloudera has been successful in providing customer
satisfaction and confidence.
Best Solution among the four:
In my opinion, Amazon Web Services (AWS) would be the best choice. AWS provides a
variety of services like a number of storage options, more computing power, which is delivered
based on the utility (Gandhi and Kumbharana, 2014). Further, it provides advanced features like
a number of database engines, encryption and powerful big data tools. Hardware security
modules secure the data in the cloud along with data encryption and broad security certification
processes (Aljawarneh, 2013). With the advancement of the cloud computing technologies AWS
will become one of the leading solutions of database management systems.

3DATA WAREHOUSE
References:
Aljawarneh, S., (2013). Cloud security engineering: Avoiding security threats the right way. In
Cloud Computing Advancements in Design, Implementation, and Technologies (pp. 147-153).
IGI Global.
Gandhi, V.A. and Kumbharana, C., (2014). Comparative study of Amazon EC2 and Microsoft
Azure cloud architecture. International Journal of Advanced Networking & Applications,
pp.117-123.
Herschel, G., Linden, A. and Kart, L., (2015). Magic quadrant for advanced analytics platforms.
Gartner Report G, 270612.
Iordache, A., Morin, C., Parlavantzas, N., Feller, E. and Riteau, P., (2013), May. Resilin: Elastic
mapreduce over multiple clouds. In Cluster, Cloud and Grid Computing (CCGrid), 2013 13th
IEEE/ACM International Symposium on (pp. 261-268). IEEE.
Mousannif, H., Sabah, H., Douiji, Y. and Oulad Sayad, Y., (2016). Big data projects: just jump
right in!. International Journal of Pervasive Computing and Communications, 12(2), pp.260-
288.