Splunk provides valuable insights for IT operations, security, compliance, and business development and can capture the highest returns when it is integrated across all branches of your business. However, Splunk’s storage requirements are directly tied to its utilisation, which if not effectively managed can lead to increased costs to achieve the optimal level of integration to achieve your business goals.
Although deploying Splunk on AWS can significantly reduce the costs associated with hardware provisioning and data storage, it can still present higher operating costs when running at scale.
In this Splunk.conf presentation, Sourced Group engineers Jonathan Hodges and Daniel Barnett present architecture patterns and deployment methods that leverage different AWS storage services like EBS volume types and S3 storage tiers that demonstrate how to optimise the performance of large Splunk deployments on AWS, while lowering total operating costs to facilitate the desired level of business integrations.
In addition to this, the team demonstrates how our in-house automation platform, named “Beast” is used to provide automated request fulfilment for common client requests such as index creation, allowing the team to focus on higher value tasks for our clients.
Jonathan Hodges is an experienced AWS Cloud Consultant and dedicated DevOps practitioner at Sourced. He currently delivers container-based and server-less solutions for enterprise clients in highly regulated industries.
Daniel Barnett is an experienced cloud-agnostic systems specialist with an educational background in Networking and IT Security. At Sourced Group, Daniel is a Consultant with a proven track record of successfully delivering cloud solutions for the highly regulated enterprise.
