Data Engineering on Google Cloud Platform
This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. 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
Suitability - Who should attend?
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 modelling, extract, transform, load activities
- Developing applications using a common programming language such Python
- Familiarity with Machine Learning and/or statistics
Training Course Content
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
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