Optimising Splunk Storage on Amazon Web Services

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.

James Dymond is the Engineering Practice Lead at Sourced whose team underpins Sourced Managed Services and is currently based in Sydney, Australia. He is focused on the architecture, design, implementation and operation of complex business-critical applications and services at scale in the cloud for large enterprises.

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