Database development principles and practices

Database Development

The objective of this study is to recommend three specific tasks that could be performed to improve the quality of datasets using the Software Development Life Cycle (SDLC) methodology and to recommend the actions that could be performed to optimize record selections and to improve database performance from a quantitative data quality assessment. Finally, this work will suggest three maintenance plans and three activities that could be performed in order to improve data quality. The second part of this study involves the evaluation of which method would be efficient for planning proactive concurrency control methods and lock granularities and assessment of how the selected method can be used to minimize the database security risks that may occur within a multiuser environment. Secondly, this work will analyze how the verify method can be used to plan system effectively and ensure that the number of transactions do not produce record-level locking while the database is in operation.

I. SDLC and Improving Quality of Datasets

High quality data makes the data resources of the organization more optimal for use in that it promotes both effectiveness and efficiency. Improvement of data quality using SDLC is reported to be in three categories including those of: (1) error detection and correction stated to involve the comparison of data to a correct baseline and is stated to involve checking for errors by examining values; (2) process control and improvement which is stated to have a drawback in the implementation of error detection.; and (3) process design stated to involve the building in or data processes reported to be such that can be built new or through redesigning processes. ( p. 6)

II. Recommendation of Actions to Optimize Record Selection and Improve Database Performance

Important factors that determine performance are reported to include the physical data input and output, CPU and memory storage, and the network load. ( ) Actions that can be taken to bring about the best performance include: (1) optimization of the size of the result; (2) optimization of the amount of data to be transferred to the application server from the database; (3) optimization of the number of data transfers; (4) optimization of the time required to search the database and retrieve the result set; and (5) optimization of the load on the database.

III. Three maintenance plans and three activities that could be performed in order to improve data quality

Three maintenance activities that ensure data quality include data quality assessment, data quality measure and operational data quality improvement. Data quality assessment enables the practitioner in understanding "the scope of how poor data quality affects the ways that the business processes are intended to run and to develop a business case for data quality management." (Loshin, 2013) Data quality measurement assists in the definition of performance metrics that "feed management reporting via data quality scorecard." (Loshin, 2013) Operational data quality improvement is reported to be used for managing identified data quality rules. (Loshin, 2013)

IV. Method Efficient for Planning Proactive Concurrency Control Methods and Lock Granularities