Résumé du cours
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.
Contenu
Module 1: Introducing Google Cloud Platform
- Google Platform Fundamentals Overview
- Google Cloud Platform Data Products and Technology
- Usage scenarios
- Lab: Sign up for Google Cloud Platform
Module 2: Compute and Storage Fundamentals
- CPUs on demand (Compute Engine)
- A global filesystem (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
- CloudSQL: your SQL database on the cloud
- Lab: Importing data into CloudSQL and running queries
- Spark on Dataproc
- Lab: Machine Learning Recommendations with SparkML
Module 4: Scaling Data Analysis
- Fast random access
- Datalab
- BigQuery
- Lab: Build machine learning dataset
- Machine Learning with TensorFlow
- Lab: Train and use neural network
- Fully built models for common needs
- Lab: Employ ML APIs
Module 5: Data Processing Architectures
- Message-oriented architectures with Pub/Sub
- Creating pipelines with Dataflow
- Reference architecture for real-time and batch data processing
Module 6: Summary
- Why GCP
- Where to go from here
- Additional Resources
A qui s'adresse cette formation
This class is intended for the following participants:
- 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
Certifications
Cette formation prépare à la/aux certifications:
Pré-requis
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
Objectifs
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
Suite de parcours
Outline: Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM)
Module 1: Introducing Google Cloud Platform
- Google Platform Fundamentals Overview
- Google Cloud Platform Data Products and Technology
- Usage scenarios
- Lab: Sign up for Google Cloud Platform
Module 2: Compute and Storage Fundamentals
- CPUs on demand (Compute Engine)
- A global filesystem (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
- CloudSQL: your SQL database on the cloud
- Lab: Importing data into CloudSQL and running queries
- Spark on Dataproc
- Lab: Machine Learning Recommendations with SparkML
Module 4: Scaling Data Analysis
- Fast random access
- Datalab
- BigQuery
- Lab: Build machine learning dataset
- Machine Learning with TensorFlow
- Lab: Train and use neural network
- Fully built models for common needs
- Lab: Employ ML APIs
Module 5: Data Processing Architectures
- Message-oriented architectures with Pub/Sub
- Creating pipelines with Dataflow
- Reference architecture for real-time and batch data processing
Module 6: Summary
- Why GCP
- Where to go from here
- Additional Resources