Data Engineering on Google Cloud Platform

Each participant will leave with a recognised certificate

• 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 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 Python
• Familiarity with Machine Learning and/or statistics

Day 1: Serverless Data Analysis

Module 1: Serverless data analysis with BigQuery

Module 2: Serverless, autoscaling data pipelines with Dataflow

Day 2: Leveraging unstructured data

Module 3: Google Cloud Dataproc Overview

Module 4: Running Dataproc Jobs

Module 5: Integrating Dataproc with Google Cloud Platform

Module 6: Making Sense of Unstructured Data with Google’s Machine Learning APIs

Day 3: Serverless Machine Learning

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

Module 11: ML architectures

Day 4: Resilient streaming systems

Module 12: Need for real-time streaming analytics

Module 13: Architecture of streaming pipelines

Module 14: Stream data and events into PubSub

Module 15: Build a stream processing pipeline

Module 16: High throughput and low-latency with Bigtable

Module 17: Building Dashboards