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Research Report Topic - Investigation into Data warehouses vs. transactional databases

We witnessed the explosion of data and stored data, doubles in every three years. We are drowned by data and we need intelligent ways of structuring data. Traditional databases are not good enough to analyze these dynamically growing and large-scale data to make accurate and prompt decisions. Data warehousing is one of new techniques especially designed for online analysis. Data warehousing provides infrastructure and functions for business managers to systematically analyze their customer data to make strategic decisions. It provides OLAP (On-Line Analytical Processing) tools to analyze subject-oriented, integrated, time-variant and non-volatile data.

Answer - Title: Investigation into Data Warehouses Vs Transactional Data bases

ABSTRACT

Data base management systems and the difference between data warehousing and transactional data bases is discussed in the current report. The basic differences between the two in terms of application and inherent technology are discussed in detailed. Further several insights from contemporary literature is presented in this regard. Subsequent part of the report is focussed on to identify the current applications of the technologies and also insights given into the future works of these two technologies. Several examples of future research areas are conveniently presented in the report at appropriate contexts. Examples are provided.

Investigation into Data warehousing and Transactional Data bases

Explosion of data and stored data doubles in every three years of time. With this pace of increase of data volumes, the magnanimous task of data management becoming complex year after year. As a strategic solution to counter this problem, variety of data management techniques have evolved. Structuring data is one such technique. All the databases which are prepared traditionally may be not suitable for meeting the data base management requirements. Two immediate reasons for the same is the dynamic explosion of the data and the large scales making the process highly complicated. Data warehousing is comparatively a new strategy to face the challenge of high volume data analytical challenges. The inherent flexibility and accomodabilities possible for data management in this model will be of good usage and applicable for modern data management applications. Data warehousing technology is highly compatible with variety of tools like On-Line Analytical Processing tools. They are well designed and applicable for subject oriented, integrated and time variant data; however the data is needed to be non-volatile data for these data management requirements. Data base Management system is in totality is employed for managing digital data bases which in turn will work for the storage of the data base content, creation as well as maintenance of the data as well. Also the same technology can be employed for the sake of search and other related functions. However data ware house is conceptually different from data base management systems. The Technology of data ware housing typically will contain a single computer or a network of computers which will get connected together to form a computer system. This is place where the data will be stored for the sake of archival, analysis as well as for security requirements.

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More insights into Differences between DBMS and transactional data bases

DBMS is typically a data base manager and it is nothing but a collection of set of computer programs which are employed for management of the data. Management of data mean several things, it typically will mean organization of data, retrieval of data, storage of data as well. Normally the set of programs will be installed in the hard drive of the system. There are numerous DBMS applications available in the commercial market each with set of unique features. For example Oracle, DB2. Microsoft Access is some of the several data base applications being used at present for variety of Data base management applications. One of the unique aspects of the DBMS is the flexility of the DBMS system to get employed by either single person or by set of people. Important characteristics of the Data base management system will include modelling language, data structures, query language as well as the mechanism employed for transactional requirements. Mechanism of transactions includes concurrency and multiplicity as well. Data warehousing do have three different distinct layers for operation. The first is storage layer where in the raw data is just stored in. The second is an integration layer where in the data will get integrated to enable abstraction by the users. The final layer is the access layer which will work on for Decision support systems (DSS). DSS mainly consists of evaluation of data obtained from analytics predesigned to understand the data trends and relationships. This is the unique layer that provides distinct capability and advantage to the Data warehouse operators to manage large volumes of data. The Capacity to Support DSS is actually working to let the DSS get employed to manage large quantities of data being used at present in organizations. However still DSS is different from the capabilities of DBMS in relation with the database management applications. The key difference between the two is contained in the fact that DBMS is mainly a tool for data organization and retrieval whereas Data ware house is just can be considered as a database or data base with provisions for analysis and evaluation of data base for decision making requirements sake. In any case it is required that Data ware house need to be used along with DBMS for the sake of better retrieval and organization of data in general.

Literature Review

Basic Differences between Data Warehousing and Data base management System

The key technical difference between Data ware house and Data base management system is contained in the fact that Data ware house operates on OLAP (Online Analytical processing) technology whereas DBMS operates on (Online Transactional processing Technology). Hence in relation with these aspects it can be said that the Data ware house can be employed uniquely for the sake of analytical purposes and DBMS can be employed only for transactional purposes. Infact Data ware house contains a data base management system (OLTP) contained with OLAP mounted on. This unique combination will create a new layer which will enable the extraction of data for the sake of analytical applications. Whereas OLTP technology does not have any such features to enable analytical processing and routinely it can be employed just for the sake of transactional record and retrieval requirements only. Typical limitations of OLTP applications consist in the degree of analytics that they can be subjected to. For example as simple data base transaction systems can be employed for the sake of generating static information silos that can be employed for routine and limited usage applications, however OLAP applications can be employed for the sake of real time applications that require in depth evaluation of the available data. Variety of questions that can be answered with the data ware house enabled analytics include what has happened in the particular data conditions? Why has it happened like that? What will happen in this context? What need to be done in this regard etc. Each of these queries will be answered with the employment of right analytics using data ware housing.

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Data ware housing methodologies and Data base management technologies

There are several factors that do define the efficiency of operation of a typical data ware house technology. Data ware housing technology efficiency will decide the speed and ease with which a particular data base record can be retrieved from the system. Data ware housing technologies will employ technologies like star-schemas or even Snowflake schemas will be normally employed for the sake of quick retrieval of flat and widely recorded data in general. Normally as the data flows moves up and grow, the star schemas and the snowflakes will result in ease in extraction of the data and will enable betterment of data extraction in general. Large Flat tables are employed in the primitive type of data ware housing technologies and are outdated now, due to their inherent difficulties in extraction of data from them. Apart from speed of operation, the most important aspects of the data ware housing are the quality of operations. It is very much required for the data ware housing operations to retain the quality of the data base operations. Typically Grounded theory coupled with the system dynamics can be employed for preparing Casual loops which can be of good use for making data ware housing quality inspection. Typically data ware house quality can be linked with the speed of operations and collectively will be worked on to evaluate the versatility of the technology. Data ware housing data entry errors and data complexity will collectively define the quality of the dataware housing in general (Subrahmanian & Wang, 2017). Smith (2017) indicated insights into the future of data ware housing and data ware housing technologies with proper rationale. Though at the outset the data warehousing has not employed for larger applications at present the applications of data ware housing are enormous; there are range of data ware housing technologies at present being employed for the sake of data management applications. Typically technologies like business intelligence, business analytics, advanced analytics, information stewardship etc are working out to provide excellent insight into the data management requirements. Logical data ware house, Hybrid transaction Analytical processing and In-Memory computing are some of the leading technologies related to the contemporary Data warehousing operations. The limitations of the previous technologies are mitigated and this is paving way for getting advanced insights into the data analytics which can be of good use for organizational decision making requirements. Sorransso & Cavalcanti (2018) specified the possible analytical query applications possible with the DBMS technologies. Traditionally SQL based Transactional data base modelling is employed for data querying and application requirements. However in later times there is extensive development of No SQL based data base management systems as an alternative to increase the performance of the data base management systems. Further the cost of applications employed for traditional data bases has decreased with the development of novel technologies. Data modelling is mainly based on Business Intelligence applications and DBMS has gained increased applicability in these technologies with the advent of novel technologies in this domain.

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It shows how much alternative modelling can significantly impact on the query performance. Experiments were performed on MongoDB, a popular document-oriented NoSQL DBMS, and show some significant results. In addition, a modelling heuristic is presented for this DBMS, and suggests that more than one document collection, based on alternative data modelling, should be maintained in order to improve query performance.

Future work points to query redirection mechanisms for such systems. Recent trends in Data ware housing include cloud based technology usage. Cloud technology is a facilitator that eliminates the limitations in the usage of Data warehousing for large collection of data which is quite common and normal these days. When the data is very large it is very difficult for the conventional OLAP technologies to use the existing resources for the analysis of the same. In all such analytics requirements the cloud based OLAP comes in handy. They simply will work enabling the usage of shared resources available online in the cloud with a simple requisition for sharing. A simple requisition can avail Data warehousing vast quantities of resources to manage vast data available for processing. It can be done in no time and also the extent of human intervention is just limited to the creation of the request. There is nothing much to do in this regard (Foster, Godbole, 2016). Typical technologies employed in DBMS for big data processing includes hashing, indexing, Bloom filtering, parallel computing,

Real world example/Usage

Electronic health record management is a typical example in this regard. OLTP technologies are often employed for the sake of retrieval, recording and storing of the patient data. Which later can be employed for the sake of analytics by clubbing with a data ware house (OLAP)? Database management applications are typically employed for variety of application like document based data base modelling requirements. MongoDB is one such popular Document oriented NoSQL DBMS system mainly employed for the sake of modelling of data and for the sake of improving the overall performance of the same. Document retrieval and employment of the same for data storage etc enabled by technologies like MongoDB very conveniently. Amazon Red shift and Microsoft SQL Data warehouse are some of the technologies being employed for cloud based data warehousing requirements at present. Amazon Web Services (AWS) is providing cloud data warehousing as platform as a service application (Gartner, 2016a). Redshift can even be applied for data of petabyte range very conveniently. Microsoft Azure is more conveniently applicable distributed data base system. The technology does have characteristics of SaaS, PaaS and even IaaS technologies for the sake of massive processing (MPP) distributed database system.

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Comparison between Azure and Amazon Web services based Data warehousing

Table 1 Data warehousing types

There are numerous challenges there contained in contemporary data warehousing technologies the future developments need to address these concerns. One of the most important concern of this technology development is the fact that when cloud based technology usage needs data download at high volumes and high speeds. The limitations of the internet connections and the limitations of the infrastructure of the cloud can be a problem in this regard. Whenever there is dedicated line, it is possible to have better communication however still it can be expensive. Future technologies can address these concerns and can develop more cost effective solutions that can meet these objectives. Large data availability and the inherent access to the critical information from critical nodes can be a problem both from performance as well as form security aspects. Future technologies need to immune better to safe guard the interests of the end users in these aspects.

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Data base management systems

There are varieties of data base management system at present in use with each type with its unique application characteristics. For example there are diverse tools both for the sake of batch based processing and for the sake of stream base processing requirements. Hadoop, sky tree server, Talented Open studio, Jaspersoft, Dryad, Pentaho, Tableau, Karmasphere are some of such technologies that can be of use for processing large quantities of data and at high speeds. Hadoop is being employed for distributed data processing applications. Skytree server is particularly useful for fast processing of data. Talend open studio is useful for Rich component sets and Code coversion as well. Jaspersoft is employed for making report from database columns is highly useful as is low price and good for easy installation. Storm is stream based processing tool used for real time processing of massive amounts of data. It is easy to use technology. Splunk is employed to capature indexes and correlates real time data with the aim of generating reports, alerts and visualizations. S4 is employed for processing unbounded data streams efficiently. SAP Hana, SQL stream s- server are some of the other stream processing tools employed for similar applications. Typical advantages of these technologies include easy to use, scalability, high performance analytics, low cost etc (Yakoob et al.,2016).

Future Work

As of now, data ware housing is being employed for business analytics in diverse industrial applications. Data from integrated sources is being collected and is being stored in the common location for processing to fulfil the needs of running reports and queries on them. Some of the applications of the data warehousing include generating regular financial reports, working out to develop business metric analytics. The domain of application of the data warehousing will further extend these applications. As such there is growth in the data collection and data preparation, the range of data analytics applications also is growing. Cloud based data ware housing is a typical areas where there is extensive convergence of data due to the huge changes in the data sources, due to the increase in the volume of the data access as well as analytics. Efficiency of data access and analytics technologies is growing more to improve their efficiencies more. Though cloud based data warehousing do address numerous concerns in this regard, the key challenge still remains the same as the need for the existence of the service oriented strategy at the organizational level.

In Data base management system, contemporary technology evaluation indicating that there is need for technology maturity in the areas of graph processing, Heterogeneous computing, Hybrid computing, Memory processing etc. These technologies in future will provide sufficient scope for versatility of the usage of technology. Apart from cloud based technologies some of the other possible potential technologies include Granular computing, Software defined Storage, Stream computing, AI, parallel computing, Edge computing, quantum cryptography etc. Each of them do have their unique advantages. Some of them are good for making the process much faster and some of them are good for contributing to the efficiency, cryptographical security etc.

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Conclusion

Both data ware housing and data base management systems are employed in the domain of data management; both do have different operational aspects. Data warehousing is employed mainly for the sake of integrating data base management systems along with the data analytics and the data base management systems are mainly for transactional data processing requirements. The current report has provided with extensive difference between the data base management system technologies as well as data ware housing technologies with insights into the contemporary and future possible technologies. Though cloud based technology is highly meeting the current demands, it has inherent limitations in security, performance limitations etc. Current research is working to upgrade the skills and levels of these technologies much competent. Apart from cloud based technology there is considerable research in vast array of technologies at present like parallel computing, crypto computing etc, each have their unique explicabilities. There is much scope for maturity of both OLTP and OLAP technologies in use at present will meet the future data requirements.

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