Data Engineering on Google Cloud Platform

This course teaches participants the following skills:

• Design and build data processing systems on Google Cloud Platform
• Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
• Derive business insights from extremely large datasets using Google BigQuery
• Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
• Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
• Enable instant insights from streaming data

To get the most of out of this course, participants should have:

• Completed Google Cloud Fundamentals: Big Data & Machine Learning course OR have equivalent experience
• 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 as Python
• Familiarity with Machine Learning and/or statistics

This class is intended for experienced developers who are responsible for managing big data transformations including:

• Extracting, Loading, Transforming, cleaning, and validating data
• Designing pipelines and architectures for data processing
• Creating and maintaining machine learning and statistical models
• Querying datasets, visualizing query results and creating reports

• Instructor-led, instructor-led online

The course includes presentations, demonstrations, and hands-on labs.

Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform
Module 1: Google Cloud Dataproc Overview
Module 2: Running Dataproc Jobs
Module 3: Integrating Dataproc with Google Cloud Platform
Module 4: Making Sense of Unstructured Data with Google’s Machine Learning APIs

Serverless Data Analysis with Google BigQuery and Cloud Dataflow
Module 5: Serverless data analysis with BigQuery
Module 6: Serverless, autoscaling data pipelines with Dataflow

Serverless Machine Learning with TensorFlow on Google Cloud Platform 
Module 7: Getting started with Machine Learning
Module 8: Building ML models with Tensorflow
Module 9: Scaling ML models with CloudML
Module 10: Feature Engineering

Building Resilient Streaming Systems on Google Cloud Platform
Module 11: Architecture of streaming analytics pipelines
Module 12: Ingesting Variable Volumes
Module 13: Implementing streaming pipelines
Module 14: Streaming analytics and dashboards
Module 15: High throughput and low-latency with Bigtable