Future Databases and Performance Tuning

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Table of Contents

The current state
Alternative databases
Database performance tuning
Real-Time Database Analytics
Enhanced data management
Future database
References

The current state

The first issue that arises with modern databases is the framework they are built on. Many database-backed applications use an Object Relational Mapping (ORM) framework, which affects the database performance (Yan et al., 2017). ORMs are used in application construction that correlates with database management systems (DBMSs) (Yan et al., 2017). It seems to ease the application development through abstracting persistent data instead of embedding SQL queries, but it also lowers the performance (Yan et al., 2017). Hiding specific query processing details leaves the programmer unaware of how the database works, making it more challenging to optimize the system. Thus, the following problem needs to be addressed in future databases.

Alternative databases

One of the possible solutions starts to arise with alternative database technologies such as NoSQL and Hadoop appearing. The cloud database platforms are also projected to provide a more significant workload without performance costs. Even general-purpose databases are now enabled to support several data models with extended capabilities such as in-memory storage and data virtualization. The future databases are expected to go even further and support a broader range of workloads and functions such as stream processing and secondary functionality.

Database performance tuning

The changes necessary for developing future databases require discovering the patterns in modern ones and improving them. The system’s functional components need to be in tune to provide a qualitative change (Zimniak et al., 2015). Since the database workload continually adapts, the information about modifications in the structure and intensity is essential in forecasting the effectiveness of future databases (Zimniak et al., 2015). The workload information can be reached through audit trails, sequences of dynamic performance views, and traces of user applications analysis.

The following data collection allows the analysts to evaluate the intricate patterns to predict the future workload levels and adapt the database. One of the essential features to be taken into account is estimating future query execution time that predominantly affects the monitoring and scheduling of query processes (Zimniak et al., 2015). The information about the possible workload levels can then be used to automate performance improvement or change the future database design (Zimniak et al., 2015). The following information about the past workloads can be collected from a database management system.

Real-Time Database Analytics

However, due to a large amount of information needed to be processed, a database’s analytics and tuning present a complex and irregular problem. Today there appear to be no visible patterns for database assessment. Many database operations processes remain unclear because of this. The following issue of the inability to identify all the intricate periodic patterns will be solved in the future databases.

Future database development predicts the appearance of real-time analytics. The current issue of complex database assessment process creates the demand for modern real-time analytics on transactional data. The hybrid systems require a more convenient technology for analysis. The following technology of the future would allow not only to increase the speed and simplicity of data transfer and sharing but also enable real-time targeting, fraud detection, and recommendations. This can significantly impact the future and make the maintenance of a database system much more manageable.

Enhanced data management

The future databases will soon evolve to manage completely disparate data sources to increase the system’s functionality. The present-day data pools are already quite massive with merging updating data from the internet. However, such a vast amount of information constrains its analysis. Modern data scientists develop new data architectures to make it easier to manage and orchestrate the data flow pipelines (Zheng, 2018). The new solutions are essential to solve the issue and organize evolving disparate data sources. Hybrid transactional databases are one of the possible future databases untangling the problem (Zheng, 2018).

Future database

Overall, future databases would develop systems, which utilize a broader amount of diversified data in real-time (Zheng, 2018). New technology will also emerge with the introduction of databases. Extensive data analysis would be needed as the system more and more intricate. Data analysts and statisticians will have a significant role in developing the system and giving market insights. Even now, many companies sponsor the innovations in the data industry to use them in e-commerce and media portals and make critical business decisions (Zheng, 2018). Future databases would go further than that and make precise predictions about the future based on the ongoing data analysis

References

Yan, C., Cheung, A., Yang, J., & Lu, S. (2017). Understanding database performance inefficiencies in real-world web applications.Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 1299-1308). Web.

Zheng, X. (2018). Database as a service-current issues and its future. arXiv 1. Web.

Zimniak, M., Getta, J.R. & Benn, W.(2015). Predicting database workloads through mining periodic patterns in database audit trails.Vietnam Journal of Computer Science ,2, 201–211. Web.

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