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.
Target Audience
- 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.
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 such Python.
- Familiarity with machine learning and/or statistics.
Learning Outcomes
- 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.
Course outline
Session 1
0 Welcome
- Facility Logistics
1 Introduction to Google Cloud Platform
- Google Cloud Platform infrastructure and big data products
- Demo: BigQuery Github query
- The different data roles in an organization
- What you can do with GCP
- Activity: Explore a customer use case
Lab 1: Exploring a Public Dataset with BigQuery
2 Product Recommendations using Cloud SQL and Spark
- Compare Google Cloud Big Data products and services
- Managed Hadoop in the cloud
- Demo: Creating a Cluster
- Your SQL database in the cloud
Lab 2: Product Recommendation using Cloud SQL and Spark
3 Predicting Visitor Purchases using BigQuery Machine Learning
- Introduction to BigQuery
- Fast SQL Query Engine
- Managed Storage for Datasets
- Demo: Google Sheets to BQ
- Insights from Geographic data
- Demo: BigQuery ML
- Creating ML models with SQL w/BigQuery ML
Lab 3: Predicting Visitor Purchases BigQuery ML
Session 2
4 Real-time Dashboards with Pub/Sub, Dataflow, and Data Studio
- Introduction
- Message-oriented architectures
- Serverless data pipelines
- Data Visualization w/Data Studio
Lab 4: Real-time Dashboards with Pub/Sub, Dataflow, and Data Studio
5 Deriving Insights from Unstructured Data using Machine Learning
- Introduction to Machine Learning
- Pre-built ML models
- Demo: Cloud Vision API
- Codeless ML with AutoML
Lab 5: Classify Images using AutoML
6 Summary
- Recap of lesson