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. This course showcases the ease, flexibility, and
power of big data solutions on Google Cloud Platform.
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
Employ BigQuery and Cloud Datalab to carry out interactive
Train and use a neural network using TensorFlow.
Employ ML APIs.
Choose between different data processing products on the
Google Cloud Platform.
Instructor-led, Instructor-led online
This class is intended for the following:
Data analysts, Data scientists, 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.
To get the most of out of this course, participants should
Basic proficiency with common query language such as SQL.
Experience with data modeling, extract, transform, load
Developing applications using a common programming language
Familiarity with machine learning and/or statistics.
The course includes presentations, demonstrations, and hands-on
Google Platform Fundamentals Overview.
Google Cloud Platform Big Data Products.
CPUs on demand (Compute Engine).
A global filesystem (Cloud Storage).
Lab: Set up a Ingest-Transform-Publish data processing
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.
Fast random access.
Lab: Build machine learning dataset.
Machine Learning with TensorFlow.
Lab: Carry out ML with TensorFlow
Pre-built models for common needs.
Lab: Employ ML APIs.
Message-oriented architectures with Pub/Sub.
Creating pipelines with Dataflow.
Reference architecture for real-time and batch data
Where to go from here
Monitor your resources on the go
Get the Google Cloud Console app to help you manage your projects.