week 5 final 6211

MODELING 8

The Data Warehouse Modeling

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The Data Warehouse Modeling

Data warehousing modeling makes a process of coming up with schemas of comprehensive and condensed information of the data warehouse. The objective of the data warehousing framework or modeling is to come up with a schema that describes the reality or the fact that the warehouse is needed to assist. Data warehouse modeling makes an essential stage in designing a data warehouse for different reasons (Raju, 2019). One reason through the schema data warehouse users can visualize the connection among the data to apply them with more ease. Another reason a well-designed framework or schema permits effective data warehouse structure to emerge, help reduce the costs of warehouse implementation and enhance efficiency.

Online Transaction Processing (OLTP) is a kind of data processing that involves executing some transactions that happen concurrently. For example, online banking, entering an order or sending a text message. These operations traditionally were called economic of financial transactions which were recorded and secured for firms to access information all-time for reporting purposes. Before the OLTP was limited to the real-time operations in which items were exchanged for money. Now the context has expanded over the years especially now the internet has grown. Business can be triggered anywhere and anytime and by any kind of web-linked sensor (Raju, 2019). The initial take of economic or financial transaction has remained to be the foundation of OLTP systems hence online transaction involves putting and updating the quantity of data in a data store to gather, manage and secure the transaction.

The OLAP is the technology which is involving the business intelligent (BI) systems and applications. The OLAP make incredible innovation technology which is used for data knowledge and generate reliable report which aid in making predictions through “what if’ questions.

Dimensional Data Architecture

Dimension modeling is the most used approach to design data warehouses because it addresses two aspects simultaneously. First is the delivery of the data that is understandable by the users. Secondly delivery of instant queries performance also makes the work of the model from figure one it indicates the major components of building the Data warehouse when having a source of an operational data source to the analytical tools to help in assisting the business decision making through ETL which is Extract Transformation and Load process. To understand the operation of the model property will be added which is e-Wallet in building the data warehouse through the application of the dimensional modeling approach.

Figure 1: Dimension Data Architecture

Use Case Background

The use of electronic wallets called e-Wallet services which hold the credit that can be applied in the process of transaction like paying for the products. It an online retail that permits purchases of products from the platform. The users get the credit in different ways which include:

I. When a goods or service purchase that has gone through the payment process is disregarded where by the cash is repaid back equally to the cancellation credit

II. Users be able to obtain a different forms of credit cards like gifts

III. In the case where the user has poor services experience, the communication like soo-sorry credit given

The credit within the e-Wallet has been allocated expiry time which is 6 months in scenarios where it is either inform of a gift card as well as so-sorry credit whereby it can extend to a year if subscribed to a cancellation credit. Designing a data warehouse is one of the time-consuming concepts. The pressure from both internal and external are creating a need for warehouse data modeling to scale up the business. The designers must ensure that all information necessary will have all the information important for the business executive (Teng, & Khong, 2021). This pressure amounting from internal prove the worth of warehousing data to me critical requirement. External pressure can also be the reason where the data warehouse likes the changing technologies and enhancing the field of business. These requirements are becoming more significant to the e-wallet environment. The increment of e-wallet to the design becomes both complex and innovative. The e-wallet is making ways to operate as standalone operations and bringing most activities into one transaction. The transaction systems like the online payment systems are integrated with the e-commerce systems like the inventory systems, shipment systems. These systems need to work well when integrated to function well (Teng, & Khong, 2021). Such a transition from the old ways of designing the data warehouse would make the change from OLTP to OLAP for more exceptional experiences with the current data warehousing strategies.

Data warehousing can be used in the e-wallet aspects because it makes how the data is captured and when it is captured. The different information of data can be captured automatically when navigating through the websites (Aftab, & Siddiqui, 2018). Different aspects need to be exploited like the requirements of the design, physical requirements when trying to build a data warehouse for e-commerce and e-wallet.

Requirements

The finance sectors of the forms are likely to construct an analytical and reporting towards the service of e-wallet. Due to apprehend the point at which the wallet liabilities within the firms are likely to experience or it is experiencing. Some of the solutions they may need to answer are; day to day balance related to the credit that is associated to the services involving the e-wallet (Aftab, & Siddiqui, 2018). The credit that is likely to be exhausted by the end of the month and upon where the outcomes in terms of percentage. The used the percentage has to expire and the left percentage in a given period like a month. To achieve the results that are imported in such a case scenario a good schema must come into play so that each aspect can be displayed on the table.

E-wallet Transformation from Website Based to Mobile Apps

At present, the concept which is being embraced is using digital means to make the payments because of what is the environment of e-commerce is calling for. Since the present payments are asking to change the payment means through the gateway model or through the use of mobile apps which electronically enabled to make the transactions. Now the question might arise concerning how to develop wallets for example Venmo, Paypal apple pay among others. These payments have become more popular because they give an ecosystem that is safer to help in making the payment more fast and reliable. These days the people do not like being kept in queues for making the depositing or any kind of transaction or making the transfer of cash (Nadikattu, 2019). To eliminate such scenarios the financial handlers are making it easier through mobile apps and the development of the companies through customizing the solutions. That has prompted the advancement of the technologies like Bluetooth, Blockchain which have enabled the idea of e-wallet at present. the mobile wallet architecture will dominate for quite a long in the future market and will make away with the cash experience. The e-wallet app development companies will leverage the potency of digital wallets for more safe, fast, and easy transactions involving payments.

Transforming OLTP schema to OLAP schema

Such digital migration from a normal business operation is calling for a better experience with data warehousing. To transform the OLTP framework to OLAP different aspects must come to play.

Merge data all information and data must be merged to a specific item for example the products, employees, and customers from different OLTP systems, and condensed to the OLAP system. The merge procedures must give solutions to different OLPT systems. For example, a given system may assign the ID to each employee and other to customers respectively (Nadikattu, 2019). The systems which give the input to the OLAP systems may not be much restricted to the traditional centrally located OLPT. Different valuable information might be stored in the different legacy areas.

Scrub Data, merging the OLPT to make the data warehouse provides an opportunity to make scrub the data. It can be observed in the way systems made of OLPT label the items or spell the items. There can also be other inconsistencies in the ways of naming the items. OLAP addresses these inconsistencies before the data is loaded data warehouses. Then the inconsistencies have been addressed the Aggregation of data is done. OLTP may record all transactions but OLAP in a real sense needs the summary of the data which is aggregated in the same fashion (Nadikattu, 2019). For example, a request to retrieve monthly payments totals for each item for a year will give a total faster in the database. That makes the OLAP more detailed and more present in the process of handling the data warehouse.

References

Aftab, U., & Siddiqui, G. F. (2018, December). Big data augmentation with data warehouse: A survey. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 2785-2794). IEEE.

Nadikattu, R. R. (2019). Data Warehouse Architecture–Leading the Next Generation Data Science. Rahul Reddy Nadikattu" Data Warehouse Architecture–Leading the next generation Data Science" International Journal of Computer Trends and Technology, 67(2019), 78-80.

Raju, M. B. (2019, December 16). Data Warehouse: Dimensional Modelling: Use Case Study: Ewallet. Medium. Retrieved October 22, 2021, from https://towardsdatascience.com/data-warehouse-dimensional-modelling-use-case-study-ewallet-d9d16f559181.

Teng, S., & Khong, K. W. (2021). Examining actual consumer usage of E-wallet: A case study of big data analytics. Computers in Human Behavior, 121, 106778.