BigPanda

The overall use cases involve a few of the applications and servers sending data to BigPanda from an incident perspective, along with a few other tools from an analytic perspective. In terms of incident management, I am focusing on correlation and AIOps capabilities, which reduce noise and provide crisp, clear information regarding incidents.

The features of BigPanda that have most improved my incident response times include AIOps, correlation pieces, and grouping, which is a big win for us. The timelines it groups on specific mentions are considerable advantages.

BigPanda's automation and correlation features impact my operations by reducing incident count due to its capability to project parent-child relationships across incidents. Grouping them together helps narrow down issues faster, and resolution also improves because I can focus on impact-specific issues rather than multiple issues, which made determining impact difficult in the past.

Something that comes to mind regarding improvements is communication, as I rely heavily on another tool. If BigPanda could provide communication capabilities beyond just sending notifications—such as creating a war room style of notification, creating channels, getting people on calls, and opening a bridge—I would prefer functionalities similar to PagerDuty within the tool.

I have been working with BigPanda for close to six years now.

In terms of product stability, I can rate it around nine. It is quite good, though I would not rate it a full ten because every time there is a new feature, I have to go through documents or contact support. As the world moves toward AI, there should be features that auto-enable or help direct users on how to navigate for what they are looking for.

BigPanda's technical support is good, and they are pretty effective, though sometimes I may not be in a position to explain situations well, or they may require more information. Setting that aside, they are responsive.

I am familiar with a couple of those solutions, and I have been using them.

Regarding deployment, I did not face challenges that I need to highlight, but it went smoothly considering any other software deployment. A few scenarios had some challenges, but solutions could be identified for those, and those challenges did not require extensive support.

The deployment required close to a week, considering the complexity and the architecture, as it depended on approvals and process cycles.

My implementation strategy during that week involved procuring a few servers ourselves and dedicating them. I found the resource, started setting up the environment, connected that to BigPanda's SaaS world, defined the configurations, ensured all configuration management data was coming into BigPanda, and created the groups and entities, all taken care of during that one week period. Testing included making sure data was accurate with no drops, and I tested the network bandwidths and any latency, with each having its own set of guidelines, which helped me achieve everything in a week's time.

There were three dedicated people involved in the deployment process, and depending on who wants to send data to BigPanda, there are many core services teams involved as well.

From an operational perspective, while I do not have much detail about costs, I can say that resource count has probably reduced by about ten to twenty percent due to the reduced incident count, which enables me to identify issues faster, meaning business recovery is quicker. Certain internal applications that users faced issues with are restored at faster time intervals, which I can vouch for.

Regarding extra expenses, I pay more for communication and do not incur costs for another dashboard perspective, but there are indirect costs related to managing open-source products, leading to resource investment in maintaining the dashboards for these capabilities.

I am using BigPanda.

The impact of BigPanda's AI on managing and prioritizing IT alerts is something I have not yet explored.

BigPanda supports my team during high-pressure, critical incidents quite well.

I would absolutely recommend BigPanda to other users and companies. My overall rating for this review is eight.

In my role, I receive incidents from ServiceNow , which then alert me through BigPanda . Once I am alerted, I receive notifications in Outlook to check the incident and its description, such as job failures or errors. I then add work notes in BigPanda  and, if necessary, escalate the BigPanda incident to the L2 DB team or the shows team for further action. My main goal is to resolve issues quickly, typically within five to ten minutes, or escalate them to the appropriate level two or three team.

The feature I find most valuable in BigPanda is the capability to independently resolve incidents. After getting errors from Autosys, I document them in work notes. Receiving confirmation from the L2 team enables me to resolve incidents efficiently. This tool is particularly helpful during high volumes of incidents, such as during maintenance or release periods. Even during suppressions when other tools are unavailable, BigPanda provides alerts, enabling me to track incidents effectively, follow up, and resolve them promptly.

It would be beneficial for BigPanda to offer detailed error descriptions similar to what AppDynamics and ThousandEyes  provide. Currently, I can only add notes and notify the L2 team, but having more precise error data in BigPanda would enable me to resolve issues more quickly and efficiently.

I have been using BigPanda for around two years and three months.

I have not encountered issues like lagging or crashing with BigPanda. Despite handling high volumes of incidents, it remains efficient, facilitating easy task management and resolution.

I have not contacted BigPanda's technical support before. I haven't experienced issues recently, as alert handling is primarily due to suppressions.

I have used Splunk for managing alerts related to specific applications, and I am aware that Datadog  offers similar functionalities. I also use other solutions like ThousandEyes  for URL issues, which integrate with BigPanda to provide alerts.

The initial setup was a bit challenging as I had to figure out how to create incidents, check alerts, update notes, and manage incident escalations across groups. However, with regular usage, I became familiar with it, especially during periods of high incident volume when BigPanda's functionality proved invaluable.

Datadog , Zabbix , New Relic , ThousandEyes, AppDynamics, and Splunk.

I rate BigPanda nine out of ten. It is my second-favorite tool after Vista .

My actual work with BigPanda  involved integrating it with Google Cloud  Platform (GCP), PagerDuty, Datadog , and Splunk. My company uses GCP for data storage and system management. We manage alerts from these systems using BigPanda . The tool was configured to execute a sequence of actions when alerts are triggered, involving manual intervention to assign alerts to users and calculate SLA metrics such as Mean Time to Detect, Mean Time to Engage, Mean Time to Action, Mean Time to Escalate, and Mean Time to Resolve. Besides alert management, we used BigPanda for notification management related to maintenance deployments.

The alert enrichment feature is the most valuable feature I recommend for any organization using BigPanda. Additionally, unified analytics is another significant feature, especially for those interested in data analytics and statistics. Unified analytics in BigPanda allows users to create various dashboards using existing data. Despite some deprecated built-in dashboards from BigPanda over a year ago, my team successfully implemented our own dashboards utilizing unified analytics. Although there were integration issues, BigPanda extended support to meet our project deadlines.

The key feature that requires improvement is AI integration. If BigPanda could integrate AI, it would enhance the platform significantly by offering chatbot functionality within the BigPanda UI. This would help end users understand alert enrichment, configurations, alert noise, alert behavior, and alert statistics.

I have been working with BigPanda for six months until it was deprecated from my production system. However, I have worked with BigPanda for a total of two years before it was removed.

The solutions were quite useful but not completely stable. I would rate the stability of the solution as eight out of ten. If they have improved their technical capabilities recently, they might have achieved higher stability.

I rate the scalability of BigPanda as eight point five. There are pluses and minuses at some points, and the configuration and integration efforts contribute to this rating.

The point of contact at BigPanda, a Technical Account Manager, was helpful, but was not deeply involved in technical resolutions. Unfortunately, they are no longer with BigPanda. If BigPanda can consistently provide such competent contacts, I would rate the support ten out of ten, otherwise, it is an eight out of ten.

Before using BigPanda, I did not use any other solutions for these use cases. Currently, I am using Datadog , which also has incident handling features similar to BigPanda.

The initial configuration and setup were already done when I joined the organization, so I don't have firsthand experience with the implementation team.

Overall, I rate BigPanda as a nine out of ten. This rating assumes that everything is up to date and working well; otherwise, it would be between eight to nine point five. I would suggest ensuring that the solution is regularly updated and monitored for effective performance.