Google Cloud Professional Data Engineer Training Course – Design and Operationalise Data Processing Systems
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Description of the training: Google Cloud Professional Data Engineer Training Course – Design and Operationalise Data Processing Systems
This training prepares you for the Google Cloud Professional Data Engineer certification and is a natural extension of the GCP100B (Big Data & ML) course. In a small group of 1 to 3 participants, you learn to design, build, operationalise and secure data processing systems on Google Cloud, in batch and streaming, up to operationalising Machine Learning models.
To go further, explore our Google Cloud Platform GCP100B – Big Data & Machine Learning Fundamentals, Google Cloud Professional Cloud Architect (PCA) and Google Cloud Generative AI Leader training courses.
Good to know
Sessions are guaranteed from a single registrant (except in cases of force majeure). A preliminary interview tailors the content to the participant’s profile (level, needs, professional context, challenges). Assessment: the trainer evaluates progress through quizzes, practical scenarios and hands-on work. A certificate of completion is issued at the end of the training.
Objectives of the training: Google Cloud Professional Data Engineer Training Course – Design and Operationalise Data Processing Systems
By the end of the training, participants will be able to:
- Design batch and streaming data processing systems.
- Build and operationalise pipelines with Dataflow, Dataproc and Pub/Sub.
- Implement and optimise the BigQuery analytics warehouse.
- Manage data governance and quality with Dataplex.
- Operationalise Machine Learning models with Vertex AI.
- Ensure reliability, security and quality of data solutions.
Prerequisites of the training: Google Cloud Professional Data Engineer Training Course – Design and Operationalise Data Processing Systems
- Command of SQL and programming notions (Python).
- Experience in data manipulation (ETL/ELT, modelling).
- Knowledge of Google Cloud fundamentals (GCP100B or ACE recommended).
- A preliminary interview lets us adapt the content to each participant’s profile and objectives.
Target audience of the training: Google Cloud Professional Data Engineer Training Course – Design and Operationalise Data Processing Systems
This training is intended for:
- Data engineers and data integrators.
- Data analysts and BI profiles upskilling.
- Data scientists needing to operationalise models.
- Profiles preparing the Professional Data Engineer certification.
Detailed programme of the training: Google Cloud Professional Data Engineer Training Course – Design and Operationalise Data Processing Systems
Designing data-processing systems
- Choosing services and modelling data.
- Batch vs streaming, reliability and scalability.
Ingestion and messaging
- Pub/Sub, Datastream and connectors.
- Real-time and batch collection.
Data processing (Dataflow, Dataproc)
- Apache Beam pipelines with Dataflow.
- Managed Spark/Hadoop with Dataproc.
Warehousing and analytics with BigQuery
- Modelling, partitioning and cost optimization.
- Analytical queries and SQL best practices.
Orchestration, quality and governance
- Cloud Composer (Airflow) for orchestration.
- Data quality and governance (Dataplex).
Data for machine learning
- Preparing data for ML.
- BigQuery ML and Vertex AI.
Preparing for the Professional Data Engineer exam
- Review of exam domains and typical scenarios.
- Tips and taking the certification.
Course highlights
- Directly extends the GCP100B course into a data track
- Covers BigQuery, Dataflow, Dataproc, Pub/Sub, Dataplex and Vertex AI
- Hands-on approach with end-to-end pipeline labs
- Groups of 1 to 3 participants for individualised support
FAQ
Who is the Professional Data Engineer training for?
It is intended for data engineers, data integrators, data analysts and BI profiles upskilling, as well as data scientists needing to operationalise their models on Google Cloud. It is an ideal extension of the GCP100B course.
What are the prerequisites?
It is recommended to know SQL, have programming notions (Python) and experience of data manipulation (ETL/ELT, modelling). Knowledge of Google Cloud fundamentals (GCP100B or Associate Cloud Engineer) is advised.
Which Google Cloud services are covered?
The training covers BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Composer, Dataplex and Vertex AI, with a focus on architecture trade-offs (batch vs streaming, choosing the right storage and processing service).
Does the training prepare for the certification exam?
Yes. The programme covers the Professional Data Engineer exam domains and ends with a preparation module: domain review, scenario-based questions and a synthesis lab around a complete data solution.
How long is the training?
The training runs over 5 days, i.e. 35 hours, remotely (recorded sessions). The content is tailored during the preliminary interview to participants’ profiles and objectives.
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