Course Overview
TOPGet 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
TOP| 12/02/24 - TDV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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| 12/09/24 - TDV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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| 03/04/25 - TDV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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| 09/09/25 - TDV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOPDesign 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:
Outline
TOP
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
Prerequisites
TOPBasic 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
TOPExtracting, 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:
