#kubernetes helps teams of all sizes optimize their #microservices architecture by enabling seamless automated containerized app deployment, easy scalability, and efficient operations. But Kubernetes also has a reputation for being difficult to learn and complex to manage, and when you’re new to something, it’s hard to know what you don’t know.

In this demo first shown at Splunk.conf24, we look at the data-lake creation feature of Observo. Data is stored in the parquet format - a open columnar format. We also support searching the data-lake based on natural language search - under the hood this functionality uses LLM for text to SQL functionality. Use the rehydrate function to send any subset of data to the analytics platform of choice, on-demand. Consider keeping a smaller Splunk index, and use the lake for retention - retain more data, longer, for a lot less cost, all in a flexible format.