Data Engineering on GCP
Google Cloud Platform Training
Overview
This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.
For private classes, please contact us at (852) 2116 3328 for more details.
Course Objectives
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
Prerequisites
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
Intended Audience
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
Delivery Method
- Instructor-led, instructor-led online
Course Outline
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