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Data Engineering on Google Cloud

SS Course: 56025

Course Overview

Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hands-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.                                                                  

Scheduled Classes

04/15/24 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)

What You'll Learn

Design and build data processing systems on Google Cloud.
  • Process batch and streaming data by implementing autoscaling data pipelines on Dataflow.
  • Derive business insights from extremely large datasets using BigQuery.
  • Leverage unstructured data using Spark and ML APIs on Dataproc.
  • Enable instant insights from streaming data.
  • Understand ML APIs and BigQuery ML, and learn to use AutoML tocreate powerful models without coding.
  • This course teaches participants the following skills:


Viewing outline for:
Introduction to Data Engineering
  • Dataflow Streaming Features
  • High-Throughput BigQuery and Bigtable Streaming Features
  • Advanced BigQuery Functionality and Performance
  • Introduction to Analytics and AI
  • Prebuilt ML Model APIs for Unstructured Data
  • Big Data Analytics with Notebooks
  • Production ML Pipelines
  • Custom Model Building with SQL in BigQuery ML
  • Custom Model Building with AutoML
  • Building a Data Lake
  • Building a Data Warehouse
  • Introduction to Building Batch Data Pipelines
  • Executing Spark on Dataproc
  • Serverless Data Processing with Dataflow
  • Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
  • Introduction to Processing Streaming Data
  • Serverless Messaging with Pub/Sub


Basic proficiency with a common query language such as SQL.
  • Experience with data modeling and ETL (extract, transform, load) activities.
  • Experience with developing applications using a common programming language such as Python.
  • Familiarity with machine learning and/or statistics.
  • To get the most of out of this course, participants should have:

    Who Should Attend

    Extracting, Loading, Transforming, cleaning, and validating data
    • Designing pipelines and architectures for data processing
    • Integrating analytics and machine learning capabilities into data pipelines
    • Querying datasets, visualizing query results and creating reports
    • This class is intended for developers who are responsible for:

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