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With data-intensive workloads, you can select from these options to best meet your organization's needs:

  • Fully-managed data solutions: Managed offerings like Cloud SQL, Memorystore, Dataproc, or Spanner are easier to maintain and operate, and can be a good choice if you don't require granular control.
  • Data app on Kubernetes: You can deploy and run your stateful applications on a Google Kubernetes Engine (GKE) cluster, with all the benefits of managed Kubernetes including scalability, security, resilience, and cost effectiveness. You can use Kubernetes operators like those for Kafka and Redis to simplify your workload management.

Learn how running data workloads on Kubernetes helps lower costs and increase your organization's ROI. Read the Data on Kubernetes 2022 Report

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Documentation resources

Find quickstarts and guides, review key references, and get help with common issues.
Explore self-paced training from Google Cloud Skills Boost, use cases, reference architectures, and code samples with examples of how to use and connect Google Cloud services.
Training
Training and tutorials

Deploy a Weaviate vector database to GKE

Build a semantic product catalog powered by Weaviate on Google Kubernetes Engine (GKE), featuring AI-generated descriptions with Gemini Pro Vision.

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Training
Training and tutorials

Deploy a Qdrant vector database to GKE

Deploy a Qdrant vector database cluster to GKE.

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Training
Training and tutorials

Deploy PostgreSQL to GKE using CloudNativPG

Deploy PostgreSQL to GKE clusters using the CloudNativePG operator.

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Training
Training and tutorials

Deploy PostgreSQL to GKE using Zalando

Deploy PostgreSQL to GKE clusters using the Zalando operator.

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Training
Training and tutorials

Deploy Redis to GKE using Redis Enterprise

Deploy Redis to GKE clusters using the Redis Enterprise operator.

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Training
Training and tutorials

Deploy Redis to GKE using Spotahome

Deploy Redis to GKE clusters using the Spotahome operator.

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Training
Training and tutorials

Deploy Apache Kafka to GKE using Confluent

Deploy Apache Kafka to GKE clusters using the Confluent operator.

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Training
Training and tutorials

Deploy Apache Kafka to GKE using Strimzi

Deploy Apache Kafka to GKE clusters using the Strimzi operator.

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Training
Training and tutorials

Deploy an app using GKE Autopilot and Cloud Spanner

Deploy a containerized web app to a GKE Autopilot cluster, and use a Google Cloud Spanner database in the backend to store data.

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Training
Training and tutorials

Deploy a stateful MySQL cluster on GKE

Deploy a MySQL InnoDB cluster and MySQL router middleware on GKE.

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Training
Training and tutorials

Deploy a highly-available Kafka cluster on GKE

Deploy highly-available Kafka clusters on GKE to build streaming data pipelines.

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Training
Training and tutorials

Connect to Cloud SQL from an Application in Kubernetes Engine

Connect an app in GKE to Cloud SQL using the Cloud SQL Proxy container.

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Training
Training and tutorials

Deploy a Redis cluster on GKE

Learn how to create and deploy a highly-available Redis cluster.

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Training
Training and tutorials

Using the Compute Engine persistent disk CSI Driver

Provision and connect to volumes backed by Compute Engine PDs.

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Training
Training and tutorials

Connecting Cloud Spanner with GKE Autopilot

Learn to connect several backend services running on GKE Autopilot to a Cloud Spanner database, using Terraform and Workload Identity.

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Training
Training and tutorials

Running a MongoDB Database in Kubernetes with StatefulSets

Learn to set up a MongoDB database with a StatefulSet.

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Training
Training and tutorials

Run a Spark job on Dataproc on GKE

Create a Dataproc on GKE virtual cluster, then run a Spark job on the cluster.

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Training
Training and tutorials

Deploy Memcached on GKE

Learn to deploy a cluster of distributed Memcached servers on GKE.

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Use case
Use cases

El Carro drives change for Regnology

El Carro is an open-source Kubernetes operator for automating Oracle database management.

Oracle GKE

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Use case
Use cases

Announcing Cloud Storage FUSE and GKE CSI driver for AI/ML workloads

Access AI/ML data stored in Google Cloud Storage in containers running on GKE using file semantics.

AI/ML GKE

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Use case
Use cases

Backup for GKE

Backup for GKE is a service for backing up and restoring workloads in GKE clusters.

Backup for GKE

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Use case
Use cases

Scaling Microservices Applications: From Open Source to Redis Enterprise on Google Cloud

Transition from open source to enterprise-ready using Google Cloud and Redis.

Redis GKE

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Use case
Use cases

Connecting Google Kubernetes Engine to Cloud SQL using the Auth Proxy Operator

Simplify connecting connecting GKE to Cloud SQL.

Cloud SQL GKE

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Code Samples
Code Samples

Stateful PostgreSQL sample

Build and deploy a stateful PostgreSQL cluster using Kubernetes and Day 1 and 2 operations such as Monitoring with Cloud Monitor and disaster recovery with Backup for GKE.

Open GitHub  
Code Samples
Code Samples

Stateful MySQL sample

Deploy a MySQL database instance on GKE with the necessary naming and configuration to create a MySQL InnoDB Cluster.

Open GitHub  
Code Samples
Code Samples

Stateful Kafka sample

Build and deploy a stateful Kafka cluster using Kubernetes and Day 1 and 2 operations such as Monitoring with Cloud Monitor and disaster recovery with Backup for GKE.

Open GitHub  
Code Samples
Code Samples

Stateful workload with Filestore sample

Create Deployments that read and write to the NFS (Network File System).

Open GitHub  

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