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Curious to find out more about the hard problems in technology our Cloud Architects are solving? Enjoy the real-life case studies below.

1. Autoscaling Apache Spark / Google Cloud Dataproc

Google Cloud Dataproc is a fast, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters in a simpler, more cost-efficient way. Due to different usage patterns (e.g. high load during work hours, no usage overnight), the cluster may become either under provisioned (user experience lousy performance) or over provision (cluster is idle causing a waste of resources and unnecessary costs). We have built the auto-scaling tool that actively monitors the performance of Dataproc clusters and automatically scales the cluster up and down where appropriate. The tool adds and removes nodes based on the current load of the cluster. We built this tool on top of Google App Engine utilizing a serverless architecture.

2. Route Egress traffic without NAT with Google Kubernetes Engine

Many applications need to be whitelisted by consumers based on source IP address. As of today, Google Kubernetes Engine doesn't support assigning a static pool of addresses to the GKE cluster. kubeIP solves this problem by assigning GKE nodes external IP addresses from a predefined list by continually watching the Kubernetes API for new/removed nodes and applying changes accordingly.

3. Avoid Cloud Bill Shocks using ML

Linear Regression, although very simple, can be used to generate accurate predictions for various real-world problems efficiently. Due to its simplicity, linear regression training is easy to configure and benefits from fast convergence. We have used BigQuery ML and Tensorflow to analyze Google Cloud billing data and build a simple yet reliable prediction model to estimate the expected overall monthly expenditure.

4. Saved over $240K/year by replacing Mixpanel with BigQuery, Dataflow, & K8s

Traditionally, a lot of companies rely on Mixpanel for product analytics to understand each user’s journey. However, if your product becomes a success and your volume of events is getting high, Mixpanel may become somewhat expensive. We have built custom event-analytics solution based on Google Cloud Platform in a very efficient way and which is going to save our client about a quarter-million dollars each year.

5. BigQuery data source for Grafana

BigQuery DataSource plugin provides support for BigQuery as a backend database.

6. Increase AWS EKS 
Availability while using EC2 Spot

Helping to run workloads on AWS EKS using spot instances with on-demand instances fallback.

7. Instance Scheduler for Google Cloud

Google Cloud Instance Scheduler helping to reduce costs by 60% on average for non-production environments.

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