Home Our Success Stories Auvik, Connectwise integration in Grafana

Auvik, Connectwise integration in Grafana

Ajay Bidyarthy

July 13, 2022

5083

Client Background

Client: A Leading Tech Firm in the USA

Industry Type: IT & Consulting

Services: Software, Business Solutions, Consulting

Organization Size: 200+

Project Objective

Get statistics such as uptime, availability, cpu throughput etc. from Auvik and Connectwise and make a dashboard from it in Grafana.

Project Description

Unlike many technologies for which plugins are readily available in Grafana, there are none for auvik and Connectwise. So our task was to device a solution through which all the data from Auvik and Connectwise can be fed to Grafana. This data then would be used to plot graphs in Grafana.

Our Solution

Setup Postgres on linux
Create appropriate databases, tables and users in it.
Use python to get data from Auvik and Connectwise and perform necessary preprocesing.
In the same python file, Connect to our postgres database.
Ingest this data into postgres database.
Setup Grafana.
Connect Grafana to postgres using the postgres plugin.
Query our postgres database in Grafana to get desired results.
Plot multiple graphs according to client’s requirement and make a dashboard from it

Project Deliverables

Setup Postgres
Setup Postrges in Grafana
Write Python code to get data from Auvik and Connectwise into Postrges
Plot graphs into Grafana according to client’s requirement
Make dashboards for all the graphs

Tools used

Grafana

Postgres

Vs Code

AWS

Postman

Language/techniques used

Python

bash

Skills used

Python

networking

Data visualisation

Databases used

Postgres

Web Cloud Servers used

Amazon Web Services (AWS)

What are the technical Challenges Faced during Project Execution

Since, the data received from Auvik was in Json fromat, our first approach was to use Grafana’s built-in Json plugin. But this wasn’t working since, the data received from Auvik was multi-dimensional when the Json plugin required One dimensional data.

How the Technical Challenges were Solved

The above challenge was addressed by transforming the multi- dimensional data into one dimensional when it was store in a python variable. This transformed data was then inserted into Postgres.