I would like to share an example of a challenging data transformation and storage problem I recently encountered while setting up Elasticsearch with Kubernetes. One significant challenge was the need for a local SSD to ensure faster data retrieval, as EBS volumes were not suitable for our requirements. We had to create a storage class for local SSDs, which Kubernetes does not provide by default. To address this, we utilized a third-party solution called OpenEBS, which allows us to provision local storage.

However, mounting SSDs across multiple virtual machines proved to be another challenge. We had an auto-scaling cluster with varying numbers of machines, making manual mounting of SSDs impractical. To streamline this process, I wrote a script that automatically identifies and mounts SSD storage on each node whenever a machine is launched.

Another issue we faced was maintaining data integrity in NoSQL databases like Elasticsearch. Since unstructured data can often lead to the storage of unwanted fields, we created a template to define the data that should be stored. We enforced strict mapping to ensure that only data in the correct format was allowed.

By using OpenEBS for local SSD storage and implementing strict templates for data integrity, we successfully resolved these challenging storage and data transformation problems.