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Introduction Here at Stackable we are building a modern data platform to easily define and operate data processing architectures. One part of the claim of being a “modern” platform is, that all services are deployed on Kubernetes, which ensures the

Using CREATE TABLE AS SELECT (CTAS) SQL statements is a well established method of copying and transforming structured data.Trino’s inherent ability to manipulate data from many different sources, sinks and formats makes this a particularly effective way to move data

When checking out this year’s KubeCon in Amsterdam, the following text snippet caught my attention: “Join containerd, CoreDNS, Envoy, etcd, Fluentd, Harbor, Helm, Jaeger, Kubernetes, Linkerd, Open Policy Agent, Prometheus, Rook, TiKV, TUF, Vitess, Argo, Backstage, Buildpacks, Chaos Mesh, Cilium,

In Part 1 of this blog series I looked at how Stackable and Trino fit in with the wider Apache Hadoop SQL ecosystem and especially with Apache Hive to provide a means of smoothing the migration away from Hive. Now

Apache Hive is a common feature in many Hadoop deployments and it’s not unusual to find Hadoop clusters where the primary use case boils down to SQL queries on structured data. Hive has strong roots in the Hadoop ecosystem and

Motivation With the release of Apples M1 chip, ARM became accessible for a brought audience. AWS published their plans to spin up some clusters with ARM chips at their heart (better known as AWS Graviton3). They promise a 40% better price-performance