Google Cloud Fundamentals: Big Data and Machine Learning
COURSE OUTLINE:
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, you will get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.
Audience
- Data analysts getting started with Google Cloud Platform
- Data scientists getting started with Google Cloud Platform
- Business analysts getting started with Google Cloud Platform
- Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports
- Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists
Prerequisites
- Basic proficiency with common query language such as SQL
- Experience with data modeling, extract, transform, load activities
- Developing applications using a common programming language such Python
- Familiarity with Machine Learning and/or statistics
Learning Objectives
- 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
1. Introducing Google Cloud Platform
- Google Platform Fundamentals Overview
- Google Cloud Platform Data Products and Technology
- Usage scenarios
2. Compute and Storage Fundamentals
- CPUs on demand (Compute Engine)
- A global filesystem (Cloud Storage)
- CloudShell
3. Data Analytics on the Cloud
- Stepping-stones to the cloud
- CloudSQL: your SQL database on the cloud
- Lab: Importing data into CloudSQL and running queries
- Spark on Dataproc
4. Scaling Data Analysis
- Fast random access
- Datalab
- BigQuery
- Machine Learning with TensorFlow
- Fully built models for common needs
5. Data Processing Architectures
- Message-oriented architectures with Pub/Sub
- Creating pipelines with Dataflow
- Reference architecture for real-time and batch data processing
6. Summary
- Why GCP?
- Where to go from here
- Additional Resources