Splunk Observability Cloud vs. Tidal by Redwood

Its great if you need real-time visibility across complex or regulated environments. Also strong for hybrid or multi-cloud setups where uptime, observability and fast IR are required. It’s probably overkill for smaller teams or environments that don’t have constant changes or compliance reporting needs. It's expensive and has a steep learning curve. Also, in my opinion, do not get yourself into a consumption based model. Costs can certainly get out of control quickly.

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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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• The first one is its Kubernetes container monitoring.
• I really like this features because as we know how much K8s is vast and to manually monitor each part of the Kubernetes it takes so much time but Splunk Observability Cloud makes it easier. And even once we integrate K8s with Splunk Observability Cloud it gives us some prebuilt dashboards which gives holistic view of our Cluster and its nodes, pods, etc.
• The dashbaord feature of Splunk Observability Cloud, it gives us full flexibility to customize our dashboard with a wide range of predefined chart types.
• Now it also supports OTEL, which is a plus point for observability. As now everyone is moving towards Otel and in current market there are only few tools who supports OTEL based integrations, Splunk Observability Cloud is one out of them.

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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.

• You can use table-like functionality to generate dashboards, but these queries are heavy on the system.
• It could be easier to give insight into what type of line parsing is used for specific documents in a company-managed environment and/or show ways to gain the insights needed.
• I would like to see ways to anonymize specific data for shared reports without pre-formatting this in a dashboard on which reports could be based.

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• 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.

Good: Stable system with low error rate Easy to use for simple use cases Bad: UI is not very clear for complex usage Mobile view (when logged in from phone) is bad No library for .net

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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.

When there is an issue, it’s a win if one can easily identify the root cause. To do the same, it should allow the user to dig deep with multiple data points and compare the data and identify the anomaly. In this use case, it’s good to drive from Splunk 011y.

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Splunk support is very quick and efficient. Pre-sale specialists are very skilled and available.

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Follow a training before starting.

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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.

Splunk Infrastructure Monitoring provides far superior options for anybody using a complex hybrid multi-cloud environment and allows both your SOC and NOC to work together on the same data while driving their own insights. We found other products are still in the old world view of servers and agents residing together within a single data centre, but modern apps are no longer like this.

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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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• Significantly reduced the MTTR (Mean Time To Recovery), which in turn has improved the end-user experience tremendously.
• Meets compliance requirements of security policies, audit, regulation, and forensics.
• Helps us to track/manage the resource usage on our cloud instances which has a direct implication on the recurring cost.

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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.

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