When does class start/end?
Class hours may vary, please reach out to contact@ascendientlearning.com if you have any questions.
Data Engineering on Google Cloud
Get hands-on experience 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...
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$3,600 USD
Duration
4 days
Course Code
GCP-DE
Available Formats
Classroom, Virtual
Overview
Get hands-on experience 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, and analyze data. This course covers structured, unstructured, and streaming data.
Skills Gained
- 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.
Who Can Benefit
- Data engineers
- Database administrators
- System administrators
Prerequisites
- Prior Google Cloud experience using Cloud Shell and accessing products from the Google Cloud console.
- Basic proficiency with a common query language such as SQL.
- Experience with data modeling and ETL (extract, transform, load) activities.
- Experience developing applications using a common programming language such as Python.
Course Details
Products
- BigQuery
- Bigtable
- Cloud Storage
- Cloud SQL
- Spanner
- Dataproc
- Dataflow
- Cloud Data Fusion
- Cloud Composer
- Pub/Sub
Data engineering tasks and component
- Explain the role of a data engineer.
- Understand the differences between a data source and a data sink.
- Explain the different types of data formats.
- Explain the storage solution options on Google Cloud.
- Learn about the metadata management options on Google Cloud.
- Understand how to share datasets with ease using Analytics Hub.
- Understand how to load data into BigQuery using the Google Cloud console and/or the gcloud CLI.
- Lab: Loading Data into BigQuery
Data replication and migration
- Explain the baseline Google Cloud data replication and migration architecture.
- Understand the options and use cases for the gcloud command line tool.
- Explain the functionality and use cases for the Storage Transfer Service.
- Explain the functionality and use cases for the Transfer Appliance.
- Understand the features and deployment of Datastream.
- Lab: Datastream: PostgreSQL Replication to BigQuery
The extract and load data pipeline pattern
- Explain the baseline extract and load architecture diagram.
- Understand the options of the bq command line tool.
- Explain the functionality and use cases for the BigQuery Data Transfer Service.
- Explain the functionality and use cases for BigLake as a non-extract-load pattern.
- Lab: BigLake: Qwik Star
The extract, load, and transform data pipeline pattern
- Explain the baseline extract, load, and transform architecture diagram.
- Understand a common ELT pipeline on Google Cloud.
- Learn about BigQuery’s SQL scripting and scheduling capabilities.
- Explain the functionality and use cases for Dataform.
- Lab: Create and Execute a SQL Workflow in Dataform
The extract, transform, and load data pipeline pattern
- Explain the baseline extract, transform, and load architecture diagram.
- Learn about the GUI tools on Google Cloud used for ETL data pipelines.
- Explain batch data processing using Dataproc.
- Learn to use Dataproc Serverless for Spark for ETL.
- Explain streaming data processing options.
- Explain the role Bigtable plays in data pipelines.
- Lab: Use Dataproc Serverless for Spark to Load BigQuery
- Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow
Automation techniques
- Explain the automation patterns and options available for pipelines.
- Learn about Cloud Scheduler and workflows.
- Learn about Cloud Composer.
- Learn about Cloud Run functions.
- Explain the functionality and automation use cases for Eventarc.
- Lab: Use Cloud Run Functions to Load BigQuery
Introduction to data engineering
- Discuss the challenges of data engineering, and how building data pipelines in the cloud helps to address these.
- Review and understand the purpose of a data lake versus a data warehouse, and when to use which.
- Lab: Using BigQuery to Do Analysis
Build a Data Lake
- Discuss why Cloud Storage is a great option for building a data lake on Google Cloud.
- Explain how to use Cloud SQL for a relational data lake.
- Lab: Loading Taxi Data into Cloud SQL
Build a data warehouse
- Discuss requirements of a modern warehouse.
- Explain why BigQuery is the scalable data warehousing solution on Google Cloud.
- Discuss the core concepts of BigQuery and review options of loading data into BigQuery.
- Lab: Working with JSON and Array Data in BigQuery
- Lab: Partitioned Tables in BigQuery
Introduction to building batch data pipelines
- Review different methods of loading data into your data lakes and warehouses: EL, ELT, and ETL.
Execute Spark on Dataproc
- Review the Hadoop ecosystem.
- Discuss how to lift and shift your existing Hadoop workloads to the cloud using Dataproc.
- Explain when you would use Cloud Storage instead of HDFS storage.
- Explain how to optimize Dataproc jobs.
- Lab: Running Apache Spark Jobs on Dataproc
Serverless data processing with Dataflow
- Identify features customers value in Dataflow.
- Discuss core concepts in Dataflow.
- Review the use of Dataflow templates and SQL.
- Write a simple Dataflow pipeline and run it both locally and on the cloud.
- Identify Map and Reduce operations, execute the pipeline, and use command line parameters.
- Read data from BigQuery into Dataflow and use the output of a pipeline as a sideinput to another pipeline.
- Lab: A Simple Dataflow Pipeline (Python/Java)
- Lab: MapReduce in Beam (Python/Java)
- Lab: Side Inputs (Python/Java
Manage data pipelines with Cloud Data Fusion and Cloud Composer
- Discuss how to manage your data pipelines with Cloud Data Fusion and Cloud Composer.
- Summarize how Cloud Data Fusion allows data analysts and ETL developers to wrangle data and build pipelines in a visual way.
- Describe how Cloud Composer can help to orchestrate the work across multiple Google Cloud services.
- Lab: Building and Executing a Pipeline Graph in Data Fusion
- Lab: An Introduction to Cloud Composer
Introduction to processing streaming data
- Explain streaming data processing.
- Identify the Google Cloud products and tools that can help address streaming data challenges.
Serverless messaging with Pub/Sub
- Describe the Pub/Sub service.
- Explain how Pub/Sub works.
- Simulate real-time streaming sensor data using Pub/Sub.
- Lab: Publish Streaming Data into Pub/Sub
Dataflow streaming features
- Describe the Dataflow service.
- Build a stream processing pipeline for live traffic data.
- Demonstrate how to handle late data using watermarks, triggers, and accumulation.
- Lab: Streaming Data Pipelines
High-throughput BigQuery and Bigtable streaming features
- Describe how to perform ad-hoc analysis on streaming data using BigQuery and dashboards.
- Discuss Bigtable as a low-latency solution.
- Describe how to architect for Bigtable and how to ingest data into Bigtable.
- Highlight performance considerations for the relevant services.
- Lab: Streaming Analytics and Dashboards
- Lab: Generate Personalized Email Content with BigQuery Continuous Queries and Gemini
- Lab: Streaming Data Pipelines into Bigtable
Advanced BigQuery functionality and performance
- Review some of BigQuery’s advanced analysis capabilities.
- Discuss ways to improve query performance.
- Lab: Optimizing Your BigQuery Queries for Performance
Schedule
3 options available
-
Mar 3, 2026 - Mar 6, 2026 (4 days)
Live Virtual | 9:00AM – 5:00PM EST Language English Select from 1 options below Live Virtual |9:00AM – 5:00PM EST- Enroll
- Enroll Add to quote
-
Mar 10, 2026 - Mar 13, 2026 (4 days)
Live Virtual | 9:00AM – 5:00PM EDT Language English Select from 1 options below Live Virtual |9:00AM – 5:00PM EDT- Enroll
- Enroll Add to quote
-
Aug 25, 2026 - Aug 28, 2026 (4 days)
Live Virtual | 9:00AM – 5:00PM EDT Language English Select from 1 options below Live Virtual |9:00AM – 5:00PM EDT- Enroll
- Enroll Add to quote
FAQ
Does the course schedule include a Lunchbreak?
Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.
What languages are used to deliver training?
Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.
What does GTR stand for?
GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.
How do I find an Ascendient Learning training location?
We have training locations across the United States and Canada - View a complete list of classroom training locations.
Which delivery formats are available?
At Ascendient Learning, we offer training that is Instructor-Led, Online, Virtual, and Self-Paced.
Does Ascendient Learning deliver group training?
Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.
What does vendor-authorized training mean?
As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.
Is the training too basic, or will you go deep into technology?
It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.
How up-to-date are your courses and support materials?
We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.
Are your instructors seasoned trainers who have deep knowledge of the training topic?
Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.
Do you provide hands-on training and exercises in an actual lab environment?
Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.
Will you customize the training for our company’s specific needs and goals?
We will work with you to identify training needs and areas of growth. We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.
How do I get started with certification?
Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.
Will I get access to content after I complete a course?
You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.
How do I request a W9 for Ascendient Learning?
View our filing status and how to request a W9.
Reviews
Excellent course - great content and demos! Very good trainer who knew his stuff and followed up on questions
Eric was great! Labs, material were interesting and useful.
Very helpful instructor with solid technical background. The labs were extremely useful, flawless, and to the point.
Eric's ability to break down some complex technologies and frequently use appropriate analogies that helped me grasp things.
