Google Cloud Challenge Lab Walkthroughs

Context

Google provides an online learning platform called Google Cloud Skills Boost, formerly known as QwikLabs. On this platform, you can follow training courses aligned to learning paths, particular products, or particular solutions.

One type of learning experience on this platform is called a quest. This is where you complete a number of guided hands-on labs, and then finally complete a Challenge Lab. The challenge lab differs from the other labs in that goals are specified, but very little guidance on how to achieve the goals is given.

Lab Walkthroughs

• Create and Manage Cloud Resources — here I use gcloud to create a jump host instance, create a GKE cluster and run a service on it, and create an HTTP load balancer.
• Perform Foundational Data, ML, and AI Tasks — here I show you how to create a Dataflow job, a Dataproc job, perform some data wrangling with Dataprep, and finally use the Natural Language API to perform some entity analysis.
• Build and Optimise Data Warehouses with BigQuery — here we process public COVID data. The lab is all about BigQuery SQL. We create a date-partitioned table from a source table. We add columns to our schema, including a RECORD. We perform joins with other tables. We use the AVG aggregation function. And we look for missing data by using left joins and looking for null values.
• Engineer Data in Google Cloud — here we create BigQuery machine learning model to predict taxi fare given some source data. We create a model from some prepared learning data. We evaluate the model. And then we apply the model using some new data.
• Cloud Dataproc Cluster Operations and Maintenance — here we deploy a Dataproc cluster and use it to run a benchmark PySpark job. We then modify the cluster by changing its master node machine type, and by adding extra worker nodes. With each change, we rerun the benchmark and assess the performance.