Google Cloud Fundamentals (1 day)
Google Cloud Platform is a versatile and widely-used platform with many applications. This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform.
Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities.
Designed as a stand-alone introduction, this class is also a recommended refresher for ML. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.
Objectives
This course teaches participants the following skills:
- Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.
- Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.
- Employ BigQuery and Cloud Datalab to carry out interactive data analysis.
- Train and use a neural network using TensorFlow.
Employ ML APIs. - Choose between different data processing products on the Google Cloud Platform.
Prerequisites
To get the most of out of this course, participants should have:
- Basic proficiency with common query language such as SQL.
- Experience with data modeling, extract, transform, load activities.
- Developing applications using a common programming language (Python).
- Basic familiarity with machine learning and/or statistics.
Course Outline
Module 1: Introducing Google Cloud Platform
- Google Platform Fundamentals Overview.
- Google Cloud Platform Big Data Products.
Module 2: Compute and Storage Fundamentals
- CPUs on demand (Compute Engine).
- A global file system (Cloud Storage).
- CloudShell.
- Lab: Set up a Ingest-Transform-Publish data processing pipeline.
Module 3: Data Analytics on the Cloud
- Stepping-stones to the cloud.
- Cloud SQL: your SQL database on the cloud.
- Lab: Importing data into CloudSQL and running queries.
- Spark on Dataproc.
- Lab: Machine Learning Recommendations with Spark on Dataproc.
Module 4: Scaling Data Analysis
- Fast random access.
- Datalab.
- BigQuery.
- Lab: Build machine learning dataset.
Module 5: Machine Learning
- Machine Learning with TensorFlow.
- Lab: Carry out ML with TensorFlow
- Pre-built models for common needs.
- Lab: Employ ML APIs.
Module 6: Data Processing Architectures
- Message-oriented architectures with Pub/Sub.
- Creating pipelines with Dataflow.
- Reference architecture for real-time and batch data processing.
Module 7: Summary
- Why GCP?
- Where to go from here
- Additional Resources
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