Coursera- GCP Big Data and ML Fundamentals

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Module Lab Exercises

Module 2: Foundations of GCP Compute and Storage

• Create a Compute Engine Instance
• Interact with Cloud Storage

Module 3: Data Analysis on the Cloud

• Setup Rentals Data in Cloud SQL
• Recommendations ML with Dataproc

Module 4: Scaling Data Analysis: Compute with GCP

• Create ML dataset with BigQuery
• Carry out ML with TensorFLow
• Invoke Machine Learning APIs

View my code on GitHub

Note: These exercises were spun up in temporary cloud instances and thus are no longer available for viewing.

GCP Functional View

Big Data and ML Platform

Data Processing in the Cloud

Cloud Storage Data Handling

Loading Data into BigQuery

Fast Random Access

Pub/Sub Real-Time Messaging

Dataflow NoOps Data Pipelines

Dataflow Real-Time and Batch

Module Review Notes

• The way to decouple producers and consumers of data in complex systems in larger organizations is Pub/Sub.
• The way to create scalable, fault-tolerant multi-step processing of data is Cloud Dataflow.

Resources

• Cloud Datastore
• Cloud Bigtable
• Google BigQuery
• Cloud Datalab
• TensorFlow
• Cloud Machine Learning
• Vision API
• Translate API
• Speech API

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• Cloud Pub/Sub
• Cloud Dataflow
• Reliable task scheduling on Google Compute Engine
• Real-time data analysis with Kubernetes, Cloud Pub/Sub, and BigQuery
• Processing logs at scale using Cloud Dataflow