This 8 hour instructor-led class introduces participants to the Big Data & Machine Learning capabilities of Google Cloud Platform. It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.
This class is intended for Data analysts, Data scientists and Business analysts. It is also suitable for IT decision makers evaluating Google Cloud Platform for use by data scientists.
This class is for people who do the following with big data:
- Extracting, Loading, Transforming, cleaning, and validating data for use in analytics
- Designing pipelines and architectures for data processing
- Creating and maintaining machine learning and statistical models
- Querying datasets, visualizing query results and creating reports
Before attending this course, participants should have roughly one (1) year of experience with one or more of the following:
- A common query language such as SQL
- Extract, transform, load activities
- Data modeling
- Machine learning and/or statistics
- Programming in Python
1 day (8 hours)
Instructor-led, Instructor-led online
At the end of this one-day course, participants will be able to:
- Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform
- Use CloudSQL 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
- Choose between Cloud SQL, BigTable and Datastore
- Train and use a neural network using TensorFlow
- Choose between different data processing products on the Google Cloud Platform
Module 1: Introduction
In this module you will be introduced to Google Cloud Platform and the data handling aspects of the platform.
- What is the Google Cloud Platform?
- GCP Big Data Products
- Usage scenarios
- Lab: Sign up for Google Cloud Platform
Module 2: Foundation of Google Cloud Platform
In this module, we introduce the foundations of the Google Cloud Platform: compute and storage and introduce how they work to provide data ingest, storage, and federated analysis.
- CPUs on demand (Compute Engine)
- Lab: Start Google Compute Engine instance, ssh access
- A global filesystem (Cloud Storage)
- Lab: Set up a Ingest-Transform-Publish data processing pipeline
Module 3: Data Analytics on the Cloud
In this module we introduce the common Big Data use cases that Google will manage for you. These are the things that are widely done in industry today and for which we provide easy migration to the cloud.
- Stepping stones to the cloud
- CloudSQL: your SQL database on the cloud
- Lab: importing data into CloudSQL and running queries on rentals data
- Lab: Machine Learning with SparkML
Module 4: Scaling data analysis
This module is about the more transformational technologies in Google Cloud platform that may not have immediate parallels to technologies that attendees are using (“what’s next”).
- Fast random access
- Demo: Sample notebook in datalab
- Lab: Build machine learning dataset
- Machine Learning with TensorFlow
- Lab: Train and use neural network
- Fully built models for common needs
- Lab: Translate
- Genomics API (optional)
Module 5: Data processing architectures
In this module we will introduce you to data processing architectures in Google Cloud Platform.
- Asynchronous processing with TaskQueues
- Message-oriented architectures with Pub/Sub
- Creating pipelines with Dataflow
Module 6: Summary
- Why GCP?
- Where to go from here