My main use case for Solve DevOps Elasticsearch and Kibana is using Kibana to pull reports, do data analysis, and track and monitor data about the business we are doing currently, as Kibana provides us with live data, and we can see how the transactions are doing.

I use Kibana for data analysis because it has a database of transactions that happened in the past or even today. For example, if today I want to review how one business that we are supporting is currently doing, I will pull out a report with specific items that I want to see on that report.

Something that I love about Kibana is that every analysis I run can be saved, allowing me to look at the transactions performance or the success rate and also those I can use to look at the fail rate. When I want to analyze something, I just have to change the name of the business.

The best features of Solve DevOps Elasticsearch and Kibana, in my opinion, include the graph, which I find very useful.

What I appreciate most about the graphs in Solve DevOps Elasticsearch and Kibana is the visualization, which is very easy for even someone who is a beginner in data analysis to use, and the customization also helps in making my work seamless.

Another aspect I forgot to mention about Solve DevOps Elasticsearch and Kibana is the AI Assistant, which is very good for assisting with data indexing, analyzing APIs, generating sample datasets, creating visualizations, and troubleshooting errors.

Solve DevOps Elasticsearch and Kibana has positively impacted our organization by improving our full-time resolution time and SLA, because it supports me in troubleshooting and handling reconciliation issues effectively.

Since I started using Kibana, I can say that it has improved our relationship with our partner because we are able to provide a solution in less than an hour with troubleshooting and data visualization, maintaining our SLA at 90% and ensuring customer satisfaction remains at 100%.

Regarding improvements for Solve DevOps Elasticsearch and Kibana, I feel that the full service of the AI Assistant could be available on the free version, and I think it would be beneficial to have tutorials to help users navigate the application.

I recommend that the application be made more intuitive and user-friendly, as I found it confusing the first time I was using it, and improvements in the user experience would help beginners.

I have been working in my current field since 2022, as I joined the company in October 2022, and I am still working there until now.

Solve DevOps Elasticsearch and Kibana is very stable, and I can access it at any time of the day, with the support team always responsive whenever I need assistance.

In terms of scalability, Solve DevOps Elasticsearch and Kibana can handle a large set of data very efficiently, and I would rate it a nine because it supports simultaneous access for multiple users analyzing different datasets.

The customer support for Solve DevOps Elasticsearch and Kibana is perfect; they are always available and respond promptly whenever I reach out.

I would rate the customer support an 8 out of 10.

Previously, I used advanced Excel, but I found it to be very limited, as Kibana has made it easier for me to analyze large sets of data with just a few clicks to get the graphs and visualizations I need.

I can see a huge return on investment with Kibana, as it saves time and money by eliminating the need to hire more people due to its ability to efficiently handle data analysis.

My company decided to provide us with Solve DevOps Elasticsearch and Kibana, so I did not evaluate any other options before choosing it.

I would advise others looking into using Solve DevOps Elasticsearch and Kibana to go for it, as it has significantly improved our working conditions by saving time and effort and is easy to use, making it possible to train new users easily to become data analysts. I would give this product an overall rating of 8 out of 10.