UTAR Institutional Repository

A data analytic module to extend the grafana analytic function

Har, Pui See (2020) A data analytic module to extend the grafana analytic function. Final Year Project, UTAR.

[img]
Preview
PDF
Download (6Mb) | Preview

    Abstract

    With the staggering growth of IoT data, the IoT industrial has been transformed into big data industrial.There are wide variety of Time Series Databases(TSDB) has been developed to handle the massive amount of time series data which are being optimized in big data fields in terms of data monitoring and analytics. The Grafana is a powerful open sourced analytic and visualization software that associated with vast number of different databases. The data analytic process is important to enable users to gain useful insight from a huge volume of raw data. While there are vast number of different TSDB and RDMS available in the market that possessed different pros and cons. In this project, we benchmarked the database performance between the InfluxDB and the MySQL with Grafana by comparing the data insertion and the query performance of both databases. The benchmarked results will enable the users gain a deeper insight about the performance of InfluxDB and MySQL and thus easier for them to choose a suitable database for their project development used among wide variety of databases available in the market. Next, we will address the limitation of the Grafana as it cannot perform custom query and cannot visualize the data from the custom query. In order to enhance the analytic performance of the Grafana, we will deliver a programme to sit between the visualization tool Grafana and the data sources that function to perform custom query functions from multiple databases and thus deliver to Grafana for further data analytic function work.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: Q Science > Q Science (General)
    Divisions: Faculty of Information and Communication Technology > Bachelor of Computer Science (Hons)
    Depositing User: ML Main Library
    Date Deposited: 07 Jan 2021 20:18
    Last Modified: 07 Jan 2021 20:18
    URI: http://eprints.utar.edu.my/id/eprint/3949

    Actions (login required)

    View Item