Data warehousing, Big Data, Green Computing
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Running Head: DATA WAREHOUSE ARCHITECTURE, BIG DATA & GREEN COMPUTING 1
Data Warehouse Architecture, Big Data & Green Computing 9
Data Warehouse Architecture, Big Data & Green Computing Ravi Teja Malepati
Information Technology Importance in Strategic Planning
ITS-831-08
University of the Cumberlands
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Abstract
Data warehouse is a facilitator for disparate integration of the database which is operational within an enterprise to a store which is single. It also offers the knowledge workers an easy access into summarized, historical as well as various forms of data that is aggregated. The available architectures for warehouse have a flaw which is major during data warehouse de-coupling from its operational databases that are underlying. It causes two complications of complex warehouse update as well as inability of providing the consumers with a warehouse drill-down feature into data that is current. Green Computing is an ecofriendly of computers using with their resources that helps reducing the waste of dumping e-waste and helps in reducing the impact on environment. It helps in conserving low energy to manufacture the computers, while using computers and disposing computers need to be recycled. Every organization need to handle their IT infrastructure in an eco- friendly manner.
Introduction
Data architecture basically comprises of the specifications which are utilized to describe the existing state, define the data requirements, and guide integration
of data in addition to controlling the assets of data as per the strategy of data. It is considered to be of great importance since it acts as a bridge between technical execution as well as business strategy. Data architecture is strategically used in helping business to quickly evolve in addition to benefiting from the techniques which are considered to be emerging. Management of the delivery on complex information as well as data all over the organization plays the role of a functional transformation of agility in addition to change. This brings the necessity of having to analyze as well as evaluate the various types of data analytics which are in use within an organization which are: big data analytics, green data in addition to data warehouse. Green computers main concept is to reduce the conserving
energy and use the available resources efficiently. Every organization should replace their old systems that are conserving more energy and replace with the new energy efficient systems. Organization need to take sustainable measures on moving workload to energy efficient server because using power on the computers, data centers, servers and routers is becoming more. (Keri E Pearlson).
Data warehouse
Data warehouse is basically considered to be a collection of data marts which tends to represent the data which is historical from different functions within an institution. This particular data is mainly kept in an optimized structure to analyze as well as query data in form of a data warehouse. The systems for
warehouse of data like the home designs are made up of various architecture options. Several are deployed with operational data stores while others have the amounts of data. In addition, various data warehouses reference finite sources of data which reference external as well as internal sources of data which are
different (Abderrazak Sebaa, 2018). Additionally, data warehouse can be defined as an organization’s design for collecting data as well as storage. These data
should be sorted out, organized in an adequate manner in addition to being cleaned. Architecture of warehouse of data basically concentrates on getting a method which is efficient to acquire information from the set which is raw as well as having it in a structure which gives insights that are crucial in addition to being easily digestible. Its architecture comprises of the collection of data, used in the intelligence for businesses as well as making of decisions. The data is defined as time
variant, data which is oriented on subject in addition to non-updatable integrated data. The construction of warehouse of data is mainly unique to fit the business use, hence important to have the data architectures ‘common layers examined. The data component includes; source data- it has the responsibility of sourcing
data getting to data warehouses which is capable of being classified into various categories such as; archived data, internal data, and external data in addition to
production data. There is also the component for data staging which happens when the data which is extracted from sources that are different needs to be altered, converted as well as presented through a format which is suitable or querying in addition to analysis. Its basic role is data extraction, transformation of data in
addition to loading data. The operational systems data repositories comprises of data which is current needing to be structured in a processing manner which is
fast as well as efficient. Another component is the metadata which is also considered to be same as dictionary of data or a catalog of data within the database
management system.
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Data dictionary includes of logical data structures. It also stores data regarding records as well as addresses in addition to information about indexes. The trends
of data warehouse are several in the digital times. There is the availability of services which are managed; they are basically a form of services which are considered to be of high levels whereby most complications are addressed through the cloud. Most of the encountered challenges are in relation to efficiency, reliability,
performance in addition to scalability which is often managed by providers of cloud services when such services that are managed are in use. In relation to the architecture of warehouse of data, it is possible to utilize the ETK services which are managed fully like the factory for Azure data as well as Amazon, or the data warehouse services that are managed like Amazon AWS, Azure SQL Data warehouse among others. in situations whereby these services are used, it is possible to find interconnectivity services within the cloud, whereby these minimize implementation efforts to a great extent. Provisioning of cloud services in addition to infrastructure templates ease the process of setting up data warehouse solutions. (Rajdeep Chowdhury, 2018). There is the use of data marts for production
lines. In big data repository which is centralized like the warehouse of data it is important to analyze for production lines that are different. Data marts basically offer a solution via containment into the warehouse of data. Another trend is the columnar storage which increases the performance of the disk in comparison to
storage which is row based during the process of retrieving the analytics complex queries. Warehouses of data are capable of offering similar abilities within the cloud like use of amazon redshift for querying as well as storage at a cost which is low. The use of these services minimizes the complexity of have data warehouses set up in addition to providing control of accesses, integrating discourses that are different in addition to others. Big data
It is considered to have brought demands that are varying into organizations. The analytics of big data is a complex process in general which evaluates the varied as well as data sets which are massive or data which is big in having information revealed. Facebook as well as Google are a good example on how management of a strategy on solid data is capable of bringing huge noticeable difference within an organization. It is very essential for institutions to have this measure in consideration since they are required to make use of insights gotten from analyzing big data in relation to decisions which are connected to the operations of the organization. The managers of the IT department are required to be in participation in the efforts of having the extraction of meaning from an organization’s big data. Due to this, the managers for IT have the responsibility of learning about the big data in addition to what is required in assisting the organization make a management strategy which is better. (Zhang, 2018). It comprises of information which is collected from the social media, information which is from devices which are internet enabled including tablets as well as smartphones, machine data, voice in addition to video recordings, as well as continuous logging and preservation of data which is not structured as well as structured. Basically, it is categorized through four Vs. which include; volume; meaning it creates huge data amounts in comparison to traditional sources of data. Variety; its data is from various sources in addition the data is created through machines and people too. Velocity; the speed which data gets generated is very fast whereby it is a continuous process throughout. Veracity; this means that the places where the data gets sourced from are many which makes it essential to have the quality as well as veracity of data tested. Big data is considered to fundamentally alter how enterprises compete as well as operate. Any organization which invests in big data in addition to successfully being able to derive substantial value from their given data have a privilege of having an advantage which is distinct over other competitors who are within the same industry this is due to a gap in performance which will be developing continuously as long as data which is relevant is being generated, techniques which are emerging in addition to channels which are digital provide enhanced acquisition as well as mechanisms for delivery, in addition, the techniques which enable easier as well as faster analysis are still developing Big data analytics concept is something that has been in existence for a long period It
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techniques which enable easier as well as faster analysis are still developing. Big data analytics concept is something that has been in existence for a long period. It helps companies to harness their data in addition to making use of it in having the new opportunities identified. This later on leads to business moves which are more enhanced, profits which have been escalated, operations which are more effective as well as efficient in addition all these causes the customers to be happy.
The techniques of big data such as analytics which are based in cloud in addition to Hadoop are known for bringing cost benefits which are better in relation to big data storage (Thomas Erl, 2016). The impacts of big data are several. Tools that are new as well as processes are necessary to have big data managed which flows all over the enterprise in addition to processing as well as analysis. The development of new tools is important as it enables users to have the information which is essential without assistance. The components for effectively managing data are basically; security, extraction of information, storage, reconciliation of data as well as distribution of insights all over a company. Green data computing
Green computing is defined as the use of computers in addition to the resources which is environmentally eco-friendly as well as responsible. In addition it is
also considered to be the study of engineering, designing, manufacturing, utilizing in addition to disposing the devices of computing in a manner which basically minimizes their impact to the environment. These practices mainly involve implementing central processing units which are considered to be energy efficient, in addition to peripherals as well as servers and consumption of resource which is minimized in addition to the appropriate electronic waste disposal. Most of the IT manufacturers as well as vendors are continuing to invest in the process of designing computing gadgets which are energy efficient, minimizing the utilization of materials which are dangerous in addition to encouraging recycling of the gadgets which are digital. Its centers are considered important in the process of integrating sustainability measures. Though these centers are known for not generating waste commodities, this sector of high energy in addition to high water is meant to give priority to sustainability programs due to an increase in the demand for sustainability measures, facilities that are ranked efficient as well as energy which is clean. Google, Apple, Facebook are among the huge organizations that have adopted the initiative of going green through various means that are innovative which are later futuristic and constructive. Most managers are undergoing pressure to have their use of electricity minimized within their facilities because of considerations of the environment in addition to cutting down on expenses. The operating expense of a server which uses electricity is a lot in comparison to the expense of purchasing a server. Centers of metadata like Yahoo, Google, as well as Microsoft have had their data moved close to the River of Columbia on the border of Washington and
Oregon so that they can benefit from hydraulic power which is less expensive. Organizations are capable of adopting methods which can cool a climate. A number which is significant in relation to the bills of data power most come from the expenses incurred in having the machines cooled. It is a must for organizations to have their centers for big data cooled through the use of climate which is natural. This is leading to organizations making efforts to locate new centers for their data in the nations with cold weather such as Finland, Ireland as well as Norway. For example, Microsoft is putting into consideration the probability of building centers for data which can be submerged into the sea.in order to save energy which is extra. Efficiency of a server is an additional means of using green data by an organization. Apple is an organization which is known to have integrated fully the green data. This is in relation to its efforts in ensuring climate change has been combated and creating an environment which is better. This particular comprises of the data centers, retail stores, offices in addition to the facilities that are collocated within 43
countries among China, United States as well as United Kingdom. It is committed to producing energy which is 100% clean. The Apple organization in addition to its partners has made efforts to implement the process of the energy which is renewable around the globe. It normally creates energy which is renewable. These
projects depend on sources of energy which are different like rays of solar, wind farms, techniques that are emerging like cells of biogas fuels, systems for micro- hydro generation in addition to technologies for storing energy. Due to these given developments, the company of Apple is a leading example or additional organizations. (Keke Gai, 2016). Conclusion
With the need which is increasing to have organizations maximize their profits, the executives as well as managers are trying to find means of enhancing their bottom line. The success of implementing a data warehouse is measurable after the consulting firm as well as the client executes the project as per the requirements which have been agreed in addition to the scope of the project. As the computers application’s breadth as well as performance is increasing, so is the people’s awareness on the energy’s scarcity as well as cost which is needed in having them powered in addition to the materials required in having them manufactured.
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Nevertheless, due to the fact that developments on computing are capable of enabling people as well as enterprises in adopting lifestyles as well as work styles that are greener, in relation to environmental debate computing is automatically part of the needed solution as well as the available problem.
References
Abderrazak .S, Fatima .C, Amina .N & AbdelKamel .T (2018, February 19). Medical Big Data Warehouse: Architecture and System Design, a Case Study: Improving
Healthcare Resources Distribution. Retrieved from https://doi.org/10.1007/s10916-018-0894-9
Keke .G, Meikang. Q (2016, January). Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. Retrieved from
https://www.sciencedirect.com/science/article/abs/pii/S108480451500123X
Pearlson, K, Saunders. C, Galletta. D. (2020). Managin and Using information Systems: A Stategic approach, 7th Edition. Hoboken, NJ: John Wiley &
Sons, Inc. ISBN: 978-1119560562. Chowdhury R., Bhattacharya I., De N., Saha S. (2018, July). Double Ended Bottom-Up Approach of Data Warehouse Architecture Employing Country ISO Code Engendered Cryptographic Algorithm. Retrieved from https://doi.org/10.1007/978-981-10-3953-9_14
Thomas .E, Paul .B, Wajid .K (2016, January). Big Data Fundamentals: Concepts, Drivers & Techniques. Retrieved from
https://dl.acm.org/doi/book/10.5555/2898954
Zhang, D. (2018, October). Proceedings of the 8th International Conference on Management and Computer Science (ICMCS 2018). Retrieved from ATLANTIS
PRESS: https://www.atlantis-press.com/proceedings/icmcs-18/25904185
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DATA WAREHOUSE ARCHITECTURE, BIG DATA & GREEN COMPUTING 1
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Data Warehouse Architecture, Big Data & Green Computing Ravi Teja Malepati
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Data Warehouse Architecture, Big Data & Green Computing
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Information Technology Importance in Strategic Planning
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Information Technology Importance in Strategic Planning
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University of the Cumberlands
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University of the Cumberlands
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Data architecture basically comprises of the specifications which are utilized to describe the existing state, define the data requirements, and guide integration of data in addition to controlling the assets of data as per the strategy of data.
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Data architecture has the specifications used in describing the existing state, it defines the requirements of the data, guiding data integration, and also to control data assets according to the data strategy
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big data analytics, green data in addition to data warehouse.
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data warehouse, big data analytics and green data
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This particular data is mainly kept in an optimized structure to analyze as well as query data in form of a data warehouse.
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Data is typically stored in a structure which is optimized to query and analyze data as a warehouse of data
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The systems for warehouse of data like the home designs are made up of various architecture options. Several are deployed with operational data stores while others have the amounts of data.
Original source
The systems of the data warehouse contain various architecture options While others have operational data stores some have installed data amounts
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In addition, various data warehouses reference finite sources of data which reference external as well as internal sources of data which are different (Abderrazak Sebaa, 2018).
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Some data warehouses are known to reference finite data sources, and they reference different external and internal data sources
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Additionally, data warehouse can be defined as an organization’s design for collecting data as well as storage.
Original source
Additionally, data warehouse architecture can be described as an organizational design for collecting and storing data
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Student paper
The data is defined as time variant, data which is oriented on subject in addition to non-updatable integrated data.
Original source
it's integrated non-updatable, time- variant and subject-oriented data
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The data component includes;
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Data staging component
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archived data, internal data, and external data in addition to production data.
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Production data, internal data, archived data, and external data
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Its basic role is data extraction, transformation of data in addition to loading data.
Original source
The basic functions involved in this process are data extraction, the transformation of data, and loading of data
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The operational systems data repositories comprises of data which is current needing to be structured in a processing manner which is fast as well as efficient.
Original source
the info repositories for operational systems involve this data that need data structured in an efficient and fast processing manner
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Another component is the metadata which is also considered to be same as dictionary of data or a catalog of data within the database management system.
Original source
this is equivalent to the data catalog or data dictionary in the management system database
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Data dictionary includes of logical data structures.
Original source
In data, dictionary data is kept about structures of logical data
8/18/2020 Originality Report
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Most of the encountered challenges are in relation to efficiency, reliability, performance in addition to scalability which is often managed by providers of cloud services when such services that are managed are in use. In relation to the architecture of warehouse of data, it is possible to utilize the ETK services which are managed fully like the factory for Azure data as well as Amazon, or the data warehouse services that are managed like Amazon AWS, Azure SQL Data warehouse among others.
Original source
Many challenges that data warehousing face are associated with reliability, scalability, efficiency, and performance that are managed mostly by cloud provider when using such managed services With regards to the info warehouse architecture, one can use the fully managed ETK services like Azure Data Factory and Amazon), Managed Data Warehouse Services like Azure SQL Data warehouse, Amazon Re-shift et al (Fernando, 2018)
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There is the use of data marts for production lines. In big data repository which is centralized like the warehouse of data it is important to analyze for production lines that are different.
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Thirdly, there are data marts for the lines of production In big centralized data repository like data warehouse, it's critical analyzing for various lines of production
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Another trend is the columnar storage which increases the performance of the disk in comparison to storage which is row based during the process of retrieving the analytics complex queries. Warehouses of data are capable of offering similar abilities within the cloud like use of amazon redshift for querying as well as storage at a cost which is low.
Original source
Lastly, there's the difficulty of the columnar storage that enhances the disk performance compared to row-based storage when retrieving complex queries of analytics Data warehouses can provide such capabilities within the cloud-like using Amazon Redshift both for querying and storage at a lower cost
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Green data computing
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Green Computing."
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Green computing is defined as the use of computers in addition to the resources which is environmentally eco-friendly as well as responsible.
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Green computing is the environmentally responsible and eco-friendly use of computers and their resources
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Centers of metadata like Yahoo, Google, as well as Microsoft have had their data moved close to the River of Columbia on the border of Washington and Oregon so that they can benefit from hydraulic power which is less expensive.
Original source
As such, metadata centers like Google, Yahoo and Microsoft moved their data near Columbia on Washington/Oregon border to learn from a budget hydroelectric power
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This particular comprises of the data centers, retail stores, offices in addition to the facilities that are collocated within 43 countries among China, United States as well as United Kingdom.
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This achievement entails its data centers, its retail stores, and all its offices in the 43 countries, including China, India, the United States, and the United Kingdom
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These projects depend on sources of energy which are different like rays of solar, wind farms, techniques that are emerging like cells of biogas fuels, systems for micro-hydro generation in addition to technologies for storing energy.
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Such projects are supported different energy sources which include wind farms, solar rays and emerging technologies like micro-hydro generation systems, biogas fuel cells, and energy storage technologies (Apple, 2018)
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Medical Big Data Warehouse: Architecture and System Design, a Case Study: Improving Healthcare Resources Distribution.
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Medical big data warehouse Architecture and system design, a case study Improving healthcare resources distribution
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Retrieved from https://doi.org/10.1007/s10916-018- 0894-9
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https://doi.org/10.1007/s00170-018- 2416-9
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Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing.
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Dynamic Energy-aware Cloudlet-based Mobile Cloud Computing Model for Green Computing
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Retrieved from https://www.sciencedirect.com/science/a rticle/abs/pii/S108480451500123X
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Retrieved from https://www.sciencedirect.com/science/a rticle/abs/pii/S0952197616302251
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Pearlson, K, Saunders.
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Managin and Using information Systems:
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Managing and Using Information Systems
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A Stategic approach, 7th Edition.
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A strategic approach, 7th Edition
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John Wiley & Sons, Inc.
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John Wiley & Sons, Inc
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Big Data Fundamentals:
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Big Data Fundamentals
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Concepts, Drivers & Techniques.
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Concepts and Techniques
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Retrieved from https://dl.acm.org/doi/book/10.5555/289 8954
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Retrieved from https://dl.acm.org/doi/book/10.5555/269 2341
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Proceedings of the 8th International Conference on Management and Computer Science (ICMCS 2018).
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Proceedings of the 8th International Conference on Management and Computer Science (ICMCS 2018)
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Retrieved from ATLANTIS PRESS:
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Retrieved from Atlantis Press
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https://www.atlantis- press.com/proceedings/icmcs- 18/25904185
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Retrieved from https://www.atlantis- press.com/proceedings/icmcs- 18/25904185
