Google Cloud Platform Big Data & Machine Learning Fundamentals
This 1-day 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 overview of the data processing capabilities. This class showcases big data solutions on Google Cloud, demonstrates how easy it is to use, and gets people excited about what they can do with it.
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
Who Should Attend
This class is intended for:
- Data analysts
- Data scientists
- 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
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 GCP (Compute and Storage)
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)
- GCE: the value proposition
- Lab: Start GCE instance, ssh access
- A global filesystem (Cloud Storage)
- Google Storage, data centers, zones, regions
- GS and its role in data processing
- 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
- Where GCP started
- Towards no-ops
- CloudSQL: your SQL database on the cloud
- A no-ops database
- Lab: importing data into CloudSQL and running queries on rentals data
- Managed Hadoop + Pig + Spark on the cloud
- 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
- Datastore: Key-Entity
- BigTable: wide-column
- Why Datalab? (interactive, iterative)
- Demo: Sample notebook in datalab
- Interactive queries on petabytes
- Lab: Build machine learning dataset
- Machine Learning with TensorFlow
- Lab: Train and use neural network
- Fully built models for common needs
- Vision API
- Translate API
- Lab: Translate
- Genomics API (optional)
- What is linkage disequilibrium?
- Finding LD using Dataflow and BigQuery
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