Google Cloud Dataproc

Unified stream and batch data processing that's serverless, fast, and cost-effective. Fully managed data processing service. Automated provisioning and management of processing resources. Horizontal autoscaling of worker resources to maximize resource utilization. OSS community-driven innovation with Apache Beam SDK. Reliable and consistent exactly-once processing. Streaming data analytics with speed. Dataflow enables fast, simplified streaming data pipeline development with lower data latency. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Dataflow automates provisioning and management of processing resources to minimize latency and maximize utilization.

Edka automates the creation of a production‑ready Platform as a Service (PaaS) on top of standard cloud virtual machines and Kubernetes. It reduces the manual effort required to run applications on Kubernetes by providing preconfigured open source add-ons that turn a Kubernetes cluster into a full-fledged PaaS. Edka simplifies Kubernetes operations by organizing them into layers: Layer 1: Cluster provisioning – A simple UI to provision a k3s-based cluster. You can create a cluster in one click using the default values. Layer 2: Add-ons - One-click deploy for metrics-server, cert-manager, and various operators; preconfigured for Hetzner, no extra setup required. Layer 3: Applications - Minimal config UIs for apps built on top of add-ons. Layer 4: Deployments - Edka updates deployments automatically (with semantic versioning rules), supports instant rollbacks, autoscaling, persistent volumes, secrets/env imports, and quick public exposure.