Introduction to Splunk and its Capabilities
Good people over at Splunk explain that the platform “removes the barriers between data and action, empowering observability, IT and security teams to ensure their organizations are secure, resilient and innovative.”
Splunk is a unified security and observability platform that allows companies to go from visibility to action quickly and at scale. It helps organizations bid adieu to data silos by bringing together data from disparate sources, including hybrid and multi-cloud footprints, to get meaningful insights and positive business outcomes from its analyses.
Splunk capabilities include-
- Monitoring, searching, indexing and correlating big data from multiple sources.
- Easily sifting through big data and setting up alerts, reports and visualizations.
- Facilitating cybersecurity, compliance, data pipelines, IT monitoring and overall IT and business management or any other business area with high data volume.
- Accessing data from multiple sources across any device.
Understanding Splunk Costs
Splunk offers various pricing models to suit the needs of your organization. However, no matter how you manage Splunk costs, they can go overboard for large volumes of data, an expected reality for enterprises and large organizations in this digital age.
Besides licensing costs, organizations also pay for the storage of big data. Other indirect costs include productivity, human resources and power and computational resources Splunk needs to run.
The bottom line is that Splunk costs depend on the amount of data sent into the tool for analyses, which can build up substantially.
Where Does Splunk Fall Short?
Let’s start by saying that Splunk is a fantastic tool designed around big data technology that allows organizations to bring all their data to one centralized place to analyze it for insights.
However, modern IT environments witness the following shortcomings from the platform-
- Lack of edge analytics – Splunk requires organizations to bring their data to a single repository, which can be challenging, especially with edge and streaming data where edge processing may be preferable.
- High cost from big data – Consolidating data can get too expensive for organizations, with big data emerging across the organization and the escalating costs of moving and storing it.
- Lack of cost-effective object storage – Splunk warrants high-performance storage and doesn’t effectively use modern object storage that can help optimize costs.
- Multiple integrations – Splunk has a high cost as data needs to be in Splunk for analyses, which requires multiple integrations.
- Splunk professional services – Organizations require professional services from Splunk to integrate it across all IT components, which is an additional cost.
- Expensive trend analytics – Over time, storage and licensing costs climb if organizations want to perform trend analytics and predictive forecasting.
- Lack of distributed architecture – Splunk uses big data architecture and doesn’t support the more modern distributed architecture, which means an organization’s IT environment can’t be modernized and simplified beyond a certain point.
- Bandwidth issues – Sometimes, bandwidth can be a limiting issue when bringing in data across integrations.
- Effort duplication – Organizations often undertake duplicate efforts to bring data into Splunk from an application. Splunk doesn’t support in-place analytics.
Overview of CloudFabrix RDAF and Log Intelligence
CloudFabrix RDAF and Log Intelligence aims to unify observability, AIOps and automation to lower costs and enable efficiency for modern IT environments.
- Composable in-place search – Edge computing and multi-cloud environments amplify challenges around data volume, velocity, veracity and variety, making the traditional approach of collect, store and search too expensive. CloudFabrix low-code data bots offer composable in-place search that enables search, collect and store, creating faster insights, eliminating data silos, and reducing cost and complexity.
- Streaming analytics – Edge and IoT data sources require streaming data analytics. CloudFabrix’s RDAF processes data in motion and offers parallel high-throughput streaming and batch ingestion at the edge or in the cloud.
- Edge capabilities – cfxEdge is an observability pipeline by CloudFabrix that runs at the edge or a third-party data source/sink and is secured with cfxCloud using data fabric.
- Context enrichment – Robotic Data Automation Platform comprises multiple layers, among which the data automation layer shapes, enriches and contextualizes the data stream, running AI/ML and analytics pipelines to derive actionable insights and route to multiple sources and sinks.
- Anomaly detection – Predictive analytics applies regression analysis and forecasting models on time-series data to detect and forecast anomalies. It does so by automatically detecting interdependencies within assets, identifying key assets to continually monitor, finding MELTs with high correlation to IT performance and continuously monitoring observability data.
- Prediction – As CloudFabrix RDAF offers in-place search functionality, it makes predictive analytics efficient and affordable. The solution identifies the key metrics and KPIs before applying prediction models.
The ultimate benefit of using observability pipelines and data fabric is cost reduction. Organizations can do more with less investment with modern data fabric-led ITOps.
How CloudFabrix Log IntelligenceRDAF Helps Optimize Splunk Costs
There are multiple ways CloudFabric solutionRDAF helps optimize Splunk costs.
- It prevents invalid, redundant or unimportant data from going into Splunk. It applies analytics to identify critical data elements for analyses and saves license costs.
- It saves cost with in-place search and pattern analysis as data can stay put and still be analyzed.
- It enriches the context of data and makes it easily searchable, saving time and resources.
- Non-critical data can be routed into cheaper object storage and still be analyzed and searched, making the data useful but not expensive.
- It minimizes duplicate effort and saves productivity as data is kept out and still analyzed.
- CloudFabrix RDAF simplifies an IT environment, providing the flexibility to process data at the edge and not compelling organizations to invest bandwidth and resources in deriving value out of data.
- CloudFabrix data pipelines eliminate difficult decision-making at each step that Splunk users often grapple with. Deciding to eliminate some data means losing out on any critical insights it could’ve provided. Including all data into Splunk requires users to justify costs.
- CloudFabrix allows organizations to move forward and adopt modern IT architectures without risking IT security and visibility.
In summary, organizations can still keep on using Splunk, just more cost-effectively and flexibly.
Case Study – 40-60% Splunk Cost Savings with CloudFabrix
One of the largest telecommunications companies in North America, a billion-dollar entity, uses Splunk for log monitoring. A high data volume meant they were forced to limit the amount of data in Splunk.
Any data older than two weeks had to be wiped out from Splunk, severely limiting the insights they could obtain from their data.
Since extending their budget for Splunk was not an option, they consulted CloudFabrix. Using CloudFabrix Data Fabric and Log Intelligence, they were able to cut down Splunk costs by 40-60% and hold historical data older than two weeks.
CFX helped them summarize older data. Then, this data summary could be stored in Splunk in exponentially less space. Data aggregation helped the organization utilize and derive value from historical data for pattern recognition and predictive analytics while cutting down Splunk costs.