Deploying Splunk doesn’t come without challenges. It is common knowledge that Splunk is quite a fantastic tool for monitoring and searching through big data. In simplest terms, it indexes and correlates information generated in an IT environment, makes it searchable, and facilitates generating alerts, reports, and visualizations that aid proactive monitoring, threat remediation, and process improvements. However, there is more to it than meets the eye. It is an understatement to say that only highly skilled and professional technical experts with years of hands-on expertise can maneuver the ins and outs of Splunk.

In this article, we have collated the most common issues faced when deploying Splunk in an IT environment. The good news is that we also describe how you can maneuver through and mitigate these common issues.

1. High Licensing Cost Splunk environments are expensive – how much you pay for them is directly proportional to the volume of data ingested. Meaning, the higher the volume of data, the higher your licensing cost is. Furthermore, one of the most common challenges that customers face while deploying Splunk is in creating structured data pipelines, thereby ingesting unnecessary data into the system. Doing so, in turn, results in higher licensing costs.

As a workaround, teams often switch Splunk off for a few hours to reduce licensing costs. However, periods of zero data ingestion compromises the infrastructure’s security. LogFlow

Optimizing Splunk Licensing Cost
At LOGIQ.AI, we recognize the common issues faced with Splunk. We are on a mission to provide XOps teams with complete control over their observability data pipelines without breaking the bank.

Our AI and ML-powered data processing module enables and facilitates only necessary and high-quality data into your Splunk environment, thereby lowering the volume of data ingested. Lower data volumes naturally mean a significantly lower licensing cost. Furthermore, only ingesting the highest quality of data enhances Splunk performance by avoiding clutter and processing only data with real value.

1. Data Retention Data retention does pose a significant challenge in the Splunk environment. Although Splunk is backed up by a data retirement and archiving policy, it still poses many difficulties maneuvering through and archiving the exact data you deem unnecessary. In addition, owing to Splunk’s high storage infrastructure costs, there is a growing need to tier storage with Splunk. Even though Splunk SmartStore may seem like a great option in terms of retention, it isn’t necessarily your best friend when it comes to querying historical data regularly. Although your data is structured in your SmartStore, performance takes a massive hit due to the need for rehydration. Also, it takes immense time and effort to conduct frequent lookback searches with SmartStore deployed.

Overcoming Data Retention Woes with LogFlow
LogFlow’s InstaStore decouples storage from compute, not just on paper. InstaStore uses object storage as the primary and only storage tier. All data stored is indexed and searchable in real-time, without the need for archival or rehydration.

InstaStore comes with a plethora of advantages:

Zero Storage Tax
Zero Rehydration
Zero Reindexing
Zero Reprocessing
Zero Reanalysis
Zero Operation Delays
In short, you can compare months or even years of data with the recent ones in real-time with InstaStore while maintaining 100% compliance and infinite retention.

1. Limited Control Although Splunk is a Data-to-Everything platform, one other major challenge faced by users is that they still have limited access and control over their data pipelines. Not having observability data pipeline control built-in means investing in a whole other separate tool to control the volume of data and when it gets sent to Splunk.

With LogFlow in place, you don’t just have 100% control of upstream data flow into Splunk, but you can also shape, transform, and enhance the data you’re shipping to Splunk.

Conclusion
While Splunk is a great platform for using data to power analytics, security, IT, and DevOps, getting a Splunk deployment to control and derive real value from all the data in your IT environment is no easy task. You’d often find yourselves either depending on third-party tools to exercise greater control over data flow and quality or footing the bill for additional infrastructure and services to control and support data volumes.

At LOGIQ.AI, we understand the pain points of a Splunk user and have engineered LogFlow to mitigate the shortcomings of Splunk and the other observability and monitoring platforms in the market and give your teams total control over the data they need. All of this with extreme cost-effectiveness. In short, LOGIQ.AI makes all observability and monitoring platforms perform better, be more efficient, and be more productive.

If you’d like to try out LogFlow or get a demo on how LogFlow can improve observability, drop us a line.

Originally published on https://logiq.ai/3-common-challenges-faced-when-deploying-splunk/