The Google Cloud Platform offers up a very large number of services, for every important aspect of public cloud computing. This array of services and choices can often seem intimidating – even a practitioner who understands several important services might have trouble connecting the dots, as it were, and fitting together those services in meaningful ways. In this course, Designing Scalable Data Architectures on the Google Cloud, you will gain the ability to design lambda and kappa architectures that integrate batch and streaming, plan intelligent migration and disaster-recovery strategies, and pick the right ML workflow for your enterprise. First, you will learn why the right choice of stream processing architecture is becoming key to the entire design of a cloud-based infrastructure. Next, you will discover how the Transfer Service is an invaluable tool in planning both migration and disaster-recovery strategies on the GCP. Finally, you will explore how to pick the right Machine Learning technology for your specific use. When you’re finished with this course, you will have the skills and knowledge of the entire cross-section of Big Data and Machine Learning offerings on the GCP to build cloud architectures that are optimized for scalability, real-time processing, and the appropriate use of Deep Learning and AI technologies.
Author Name: Janani Ravi
Author Description:
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more

Table of Contents

• Course Overview
  3mins
• Implementing Integrated Batch and Streaming Architectures on the GCP
  39mins
• Designing Migration and Disaster Recovery Strategies on the GCP
  37mins
• Designing Robust ML Workflows on the GCP
  38mins