Today’s enterprise networks are heterogeneous, have multiple entry points, integrate with cloud-based applications, offer data center delivered services, include applications that run at the edge of the network, and generate massive amounts of transactional data. In effect, enterprise networks have become larger, more complex, and more difficult to secure and manage.

Last year we published a popular paper on how to calculate the true cost of an Elasticsearch, or ELK (for Elasticsearch, Logstash, Kibana) stack environment. The paper helps readers calculate their overall annual cost of ownership for their ELK environment, and reveals how the cost burden of ELK is much higher than anticipated for most customers. That paper clearly hit a nerve — it’s been, by far, our most downloaded piece of content.

Sometimes, the data you want to analyze lives in AWS S3 buckets by default. If that’s the case for the data you need to work with, good on you: You can easily ingest it into an analytics tool that integrates with S3. But what if you have a data source — such as logs generated by applications running in a Kubernetes cluster — that isn’t stored natively in S3? Can you manage and analyze that data in a cost-efficient, scalable way? The answer is yes, you can.