Google Kubernetes Engine vs. Tanzu Data Services (Greenplum, GemFire, RabbitMQ, Tanzu SQL)

Google Kubernetes Engine is well suited for dynamic and large workloads since it can scale up with usage. It is easily configurable, which allows for flexibility. User interface is simple to navigate, which reduces roadblocks for a team with people unfamiliar with Kubernetes. Great if you are already using other GCP services as it integrates well with that.

Incentivized

If you need to execute ml algorithms, learning techniques, or mathematical calculations on large amounts of heterogeneous data, VMware Tanzu Data Services will be ideal. It will be really simple to set up, particularly if you choose AWS as your integrated cloud provider. However, if you're working with lower data amounts, such as gigabytes, it can be superfluous.

Incentivized

• Deployment of a new GKE cluster is really fast in comparison to other cloud providers.
• GCP is ahead other vendors and always provide the most up to date Kubernetes version.
• GKE automation for master upgrade and the worker nodes pool works really well.

Incentivized

• Apache MADlib provides popular machine learning functionality.
• Allows you to query terabytes of data databases.
• Interoperability for AWS S3 is effortless.

Incentivized

• Not as intuitive as it could be
• Documentation could be better, especially for people using other Google Cloud tools
• Not the preferred Kubernetes Engine for many apps

Incentivized

• Running on Azure is a little more difficult.
• Synchronization with Kafka may be a little easier.

Incentivized

It's a great product if you learn it. It has flexibility and is very strong. Autoscaling and Resource management make running huge applications a breeze. Using Helm with Kubernetes and Terraform for infrastructure creation can totally automate your CICD pipeline. You also get easy access to CUDA cores for machine learning.

Incentivized

Google support is excellent and helpful, but the first answer is always so bureaucratic no matter how many logs, evidence, and information you sent.

Incentivized

They were very helpful. We needed support for initial implementation.

Incentivized

We had to move several products to Google Cloud, and the Google Kubernetes Engine was the option recommended to us, so we investigated it and ran with it. Back then (2019), we were not aware of Cloud Run-provisioned K8s clusters, so our other option was a completely self-managed K8s cluster on Compute Engine VMs, which we did not have the knowledge of and capacity to handle.

Incentivized

• Compared to other big K8s providers it has the best price/performance factors.
• Upgrade process from stable to regular versions
• Old stable releases: 1.15/1.16 should be in a stable branch.

Incentivized

• There was a noticeable reduction in system reliability.
• Saw a reduction in unsuccessful analytics operations.

Incentivized