Fivetran vs. Splunk Observability Cloud

Fivetran's business model justifies the use-case where we require data from a single source basically a lot of data but if the requirement is not on the heavier side, Fivetran comes to costly operation when compared to its peers. Otherwise, I'll recommend Fivetran for stability and update and seamless service provider.

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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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• Easily connects to source data using delivered connectors
• Transforms data into standard models and schemas
• Has very good documentation to help quickly setup connectors

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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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• Very difficult to get connectors enhanced if a specific needed object is not supported by them
• Depending on the edition needed and the data volumes, can get quite expensive

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• 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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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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Very easy and intuitive to setup and maintain as there usually are not that many options. Very well documented (e.g. how to setup each connector, how the schema looks like, any specific features of this connector etc.). Also the operation is intuitive, e.g. you have status pages, log pages, configuration pages etc. for each connector.

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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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It runs pretty well and gets our data from point A to point cluster quickly enough. Honestly, it's not something I think about unless it breaks and that's pretty rare.

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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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We never seriously considered using anything else. Our data engineers had used Fivetran extensively in previous roles so when it came time to make a decision, there wasn't much of a process. They gladly signed the contract with Fivetran pretty quickly.

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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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• It has been very positive in serving BI team with new source requests
• It has been OK at scaling to match as data volumes as source data size grows

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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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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