BigPanda vs. ScienceLogic SL1

If the organization has a proper CMDB asset record then BigPanda features can be utilized to their true potential as it has alert correlation capability. The alert can be redirected to the proper support team using the auto-share feature. This wouldn't be the case where asset records are not updated and the Operations have to manually assign the alerts to support groups.

Incentivized

Appropriate if you are setting up a monitoring suite in new Infrastructure Environment. Definitely NOT suited for Migration Projects. ScienceLogic SL1 cannot cater to a lot of monitoring requirements which already would have been configured in old monitoring suite. Plus, limited support for customizations and having to go to "Feature Requests" route makes in extremely complicated.

Incentivized

• Alert correlation feature helps in grouping similar alerts together
• Alert Enrichment to enhance the data after it arrives on the tool
• Integration with multiple tools
• The console is very user friendly and provides ease of administration

Incentivized

• Best overall coverage of montioring different technologies.
• Easy to use in any environment
• Customizable being able to generate your own reports, dashboards, DA's, RBA's, etc.
• Have very good out of the box integrations with other monitoring solutions such as ServiceNow
• Always improving and regularly releasing new versions and upgrades to the system/DA's.
• Interactive community

Incentivized

• Tool should have a feature to query the data
• The dashboard feature needs improvement
• It should have a dark mode

Incentivized

• More freedom to create custom dashboards as on the previous versions we could do much more
• The Performance TAB windows is too small and cannot be resized or maximized when looking at reports for "Overview", "File System" and any of those items.
• There are not enough widgets to create stunning dashboard in AP2
• The reporting feauture is a very untouched area.

Incentivized

It is simply because of all the best possible autonomy solutions it is providing and getting better day by day. Using AI and Devops along with handy automation, The monitoring and Management of devices becomes much easier and the way it is growing in all the aspects is one the best reasons too. Evolution of the SL1 platform in the autonomy monitoring and management is quite appreciable.

Incentivized

The core functions are there.
The complexity is due to the complexity of the space.
The score is based on comfort (I no longer notice the legacy UI) and the promise that I see in the 8.12 Unified UI (a vast improvement).
It is also based on the fact that with 8.12, you can now do everything in the new UI but you still have the legacy UI as a fallback (which should now be unnecessary for new installations)

Incentivized

Science Logic SL1 provides the option of Distributed deployment where multiple instances of each appliance can be deployed to manage the load and availability. SL1 provides a High Availability feature for Database Servers and Data Collection. If one of the Data Collectors in the collector group fails, it will automatically redistribute the devices from the failed Data Collector among the other Data Collectors in the Collector Group. The high availability feature for the Database server ensures that SL1 performs failover automatically to another server without causing the outage to the application.

Incentivized

The performance is entirely dependent on the complexity of the environment/network being used to host the platform. Outside of those factors, the platform runs very efficiently and quickly out of the box. We have integrations with other platforms and neither seem to take a hit from our moderate API usage. Any issues with performance would be experienced by choices made in infrastructure or complexity of things built by the customer to display in the GUI (overly complicated and cluttered dashboards for example)

Incentivized

So far, it's good as part of my overall experience, except for a couple of use cases. The support team is well knowledgeable, has technical sound, and is efficient. When support escalates to engineering, the issue gets stuck and takes months to resolve.

Incentivized

It was good, Do the online training first and understand it and you will get the most out of the in-person training that way.This also takes you to an advanced level which is very good and the training as been overhauled once again along with new product coming in such as Zebruim / Skylair, worth going through again if it a while back that you first did this.

Incentivized

There are a lot of educational materials and courses on the SL1 training site (Litmos university). However the recording quality is sometimes not very good - screen resolution is low. There is a lack of professional rather than user-oriented documents and there are mistakes in documentation and education is not well structured.

Incentivized

As first time developers, getting to grips with powerpack development using SNMP, Powershell and Python etc, was not helped by poor and badly organised online documentation. In many cases, we had to look at existing powerpacks and try to work out what it was doing and why - not always with much success. Even after receiving expert level training, the development of some powerpacks would not have been possible without access to the SL1 support staff.

Incentivized

TrueSight didn't provide many customization options. The features provided were primitive as compared to BigPanda. TrueSight was being used just for alert visibility and assignment to a proper support group from a single console. Although Moogsoft had similar features as compared to BigPanda, the user administration and ease of use were a bit complicated. BigPanda provides a much simpler user interface.

Incentivized

Science logic SL1 is so user friendly and it's really easy to navigate between function. I would recommend Sciene logic SL1 to all of them who are looking for really useful monitoring tool and expecting easy way of managing it.

Incentivized

Our deployment model is vastly different from product expectations. Our global / internal monitoring foot print is 8 production stacks in dual data centers with 50% collection capacity allocated to each data center with minimal numbers of collection groups. General Collection is our default collection group. Special Collection is for monitoring our ASA and other hardware that cannot be polled by a large number of IP addresses, so this collection group is usually 2 collectors). Because most of our stacks are in different physical data centers, we cannot use the provided HA solution. We have to use the DR solution (DRBD + CNAMEs). We routinely test power in our data centers (yearly). Because we have to use DR, we have a hand-touch to flip nodes and change the DNS CNAME half of the times when there is an outage (by design). When the outage is planned, we do this ahead of the outage so that we don't care that the Secondary has dropped away from the Primary. Hopefully, we'll be able to find a way to meet our constraints and improve our resiliency and reduce our hand-touch in future releases. For now, this works for us and our complexity. (I hear that the HA option is sweet. I just can't consume that.)

Incentivized

• BIgPanda is very easy to implement and use
• Better than the previous tool, provides more functionality
• BIgPanda's alert enrichment feature very helpful

Incentivized

• We are still evolving with the tool so can't really comment on the actual RoI but the features provided does adds up the customer expectations.
• Global manager usage gives us the holistic view of separate SL1 infrastructures
• Power flow helps to use some pre built Autometas which helps saves lot of time and effort

Incentivized