ScienceLogic AI Platform vs. Tidal by Redwood

For Windows, the issue is in higher resource consumption related to WinRM monitoring, which provides better options then the SNMP monitoring, which on the other hand is less resource intensive. The problem is also with support for OS with other than English language.

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On coming to well suited experience while error handling and effortless recovery processes are crucial, Tidal performs exceptionally well. It can identify and fix automated issues, reducing downtime and interruptions. A smaller automation solution may be more cost-effective if a business primarily utilizes a single platform or uses a small number of applications that do not require complex integration.

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• 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

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• Tidal Automation allows us to automate and schedule/ perform various tasks in a easy and effective manner. It is highly interactive and effective allowing nearly 4k -5k jobs to run a day.
• Tidal is designed to be easily shareable and collaborative allowing multiple users to work at a particular time making work effective.
• Tidal allows us to ensure that every event which is triggered is exactly when it's supposed to be regardless of other activities which are going as well.
• It is user-friendly to generate the reports required of a particular object and allows us to test the codes perfectly well before executing in live environment.

• Dashboards are quite old and are of Iron age. Need to have AP2 dashboards only instead of AP1 and consistent new design across all functionalities.
• Reporting is not improved since Y2020 and need to revamp completely. Need to integrate Dashboards and Reporting. PowerBI Like functionality to be given OOTB. Reports should be extracted in Excel, PDF, HTML and should be heavily automated.
• Create and Open APIs for basic and advanced monitoring data extraction.
• Topology based Event Correlation and Suppression should be improved drastically. Need to identify critical network interfaces based on Topology and monitor them. Basic customization of Dynamic App and/or Powerpack to exclude/include certain metrics/events to be permitted OOTB instead of customizations.
• Integration with ServiceNow to be improved and to be taken to next level. Automation Powerpack should be made available OOTB as part of base product and to be priced attractively.
• Take product to next level where we can monitor actual impacted IT or Business Service instead of metrics and events BSM and Topology map to be auto discovered and identify the network dependencies and alternate paths automatically instead of manual creation of BSM.

• Still a bit slow when navigating. If you close a job you have to wait a few seconds to open another one. Even when you made no changes.
• When viewing a job and make no changes, the "ok" button changes the last modified date as if you made a change. No big deal, but wastes time when troubleshooting a problem and looking into what jobs were changed last.
• You can see the parameters column in the "job activity", but not in "Job definitions".
• Can't search the parameters field in the filter.
• Changing a variable name does not change it on the job. It still works because Tidal Automation uses the ID number. It just causes confusion when you see a variable on a job and can't find the variable under "Variables". On top of that, Tidal Automation does not show the ID column under "Variables" making it even more difficult to find the variable.

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.

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We are on the fence. The increased pricing for renewals is staggering. With new automation options like Microsoft's Power Automate and Event Driven Ansible on the field, there are other options now available.

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)

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SL is always there and online when you need to get info from it. The only times when SL was not available in our own data center, was when network links from out side of the data center was down and those links were not in our controll. Having a central database and people accessing it all over the world, may put a bit of constarin on the performance of the dashboards when reports gets generated, but that is far and few n between.

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SceinceLogic SL1 architecture helps the platform to give a top-notch performance in every respect, Data collection to reporting happens very smoothly. With the new user interface pages load much faster. Individual appliances carrying the individual task ensure things are working without lag. Integration with ticketing tool(SNOW) is well managed by the ScienceLogic, no issue or much delay has been observed while interacting with an external tool.

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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.

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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 / Skylar, worth going through again if it a while back that you first did this.

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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.

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Implementation is smooth if we are to just support the out-of-the-box features available in ScienceLogic. For any custom requirement, having to go to SL1 Professional Services is the worst part of procuring this suite. And more often than not, SL1 Professional Services also ask to raise feature request. So, you subscribe to Professional Services to only hear back from them that "This feature is not supported and needs to have a separate feature request". At times frustrating.

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Having provided consulting services for years on Tidal by Redwood, I recommend going with a solutions partner or consultant to deploy it. I believe there are sizing and tuning guidelines that should be followed for environments of scale. I believe they are not critical when first lighting up the product, but if you are not aware of them you will encounter performance degradation after a few thousand job objects are added.

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.

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1. Tidal is good at processing large volume pf data and is cost effective. 2. Tidal can automate the scheduling of production objects, ensure that materials are delivered on time 3. Tidal Process large volumes of data which cannot be done everyday by running codes/scripts manually which does it with ease when required.

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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.)

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• Once a powerpack is developed and configured for a device for one customer, it is easy to deploy the same powerpack on a second customer estate and configure specifically for that customer without having to reinvent the powerpack. This saves time and therefore money.
• Once the customer estate tuning is complete, the Operations team have come trust the alerts. This is especially true when transient or self-correcting alerts are automatically cleared without ops team involvement, but a record is still available for audit and debugging purposes. This saves time and therefore money.
• When setup correctly, it provides good visibility into applications, devices and whole customer estates. This saves time and therefore money when issues arise.

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• Better decision-making and more process visibility are achieved through reporting and monitoring.
• Lowered time frames and streamlined administration of the workflow.
• Improved cost and resource allocation, especially in cloud contexts.
• A decrease in manual errors and related expenditures.
• Unusual problems that could occur during automation.
• Possible difficulties shifting to a new automation tool.
• The expense of beginning implementation and training.

Incentivized

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info