Summary

In this chapter, we explored two powerful tools, Trino and Elasticsearch, which enable effective data consumption and analysis in a Kubernetes-based big data architecture. We learned the importance of having a robust data consumption layer that bridges the gap between data repositories and business analysts, allowing them to extract valuable insights and make informed decisions.

We learned how to deploy Trino, a distributed SQL query engine, on Kubernetes and leverage its ability to directly query data stored in object storage systems such as Amazon S3. This eliminates the need for a traditional data warehouse and provides a cost-effective, scalable, and flexible solution for querying large datasets. We acquired hands-on experience in deploying Trino, configuring it to use the AWS Glue Data Catalog, and executing SQL queries against our data lake.

Additionally, we dove into Elasticsearch, a highly scalable and efficient search engine, along with Kibana, its powerful data...