Five real Professional Data Engineer projects mapped directly to GCP-PDE exam objectives. Mark a step complete and HandsOnCert commits the evidence — BigQuery schemas, Dataflow pipelines, Vertex AI model configs — straight to your own GitHub repo. The portfolio employers actually click into.
$79 one-time · Project 1 free · No subscription
Every project maps to an official GCP-PDE exam domain. Each one ends with evidence committed to your GitHub repo automatically.
Design a data architecture for a streaming and batch use case using BigQuery, Pub/Sub, and Cloud Storage — including partitioning and clustering strategy for BigQuery tables. Commit your architecture diagram and BigQuery schema design.
Designing Data Processing Systems — ~22% of examLoad data into BigQuery, write optimized SQL queries using partitioning and clustering, and build a scheduled batch pipeline using Cloud Composer or Dataflow. Commit your SQL queries, pipeline configuration, and Composer DAG.
Building & Operationalizing — ~24% of examBuild a Dataflow streaming pipeline that reads from Pub/Sub, applies a transformation, and writes to BigQuery — using windowing for aggregation. Commit your Dataflow pipeline code and execution evidence.
Building & Operationalizing — ~24% of examTrain a simple model using Vertex AI (AutoML or a pre-built model), deploy it to an endpoint, and run test predictions. Commit your training configuration, model evaluation metrics, and prediction test results.
Operationalizing ML Models — ~15% of examConfigure IAM and VPC Service Controls around your BigQuery datasets, set up data quality checks using Dataplex or scheduled queries, and configure Cloud Monitoring alerts for pipeline failures. Commit your IAM configuration and data quality check definitions.
Ensuring Solution Quality — ~15% of examEvery completed step auto-commits evidence to your own repo. No fake portfolio templates — real configs, real screenshots, real history.
No simulators. You work in the actual Google Cloud console and tools, the same ones you'll use on the job and in the GCP-PDE exam.
Stuck on a step or an exam concept? Cert Buddy is trained on GCP-PDE objectives and helps you debug and understand — not just copy-paste.
Every chargeable resource has a clear alert telling you exactly when to stop, deallocate, or delete — so a lab break doesn't become a surprise bill.
Get the full GCP-PDE lab guide as a PDF — step-by-step instructions, screenshots to capture, and an exam quick-reference section.
$79 once. No subscription, no recurring charges. Or get All-Access to all 21 cert paths for $199.
Are the GCP-PDE hands-on labs free?
Project 1 (Data Architecture Design) is completely free, including unlimited access to Cert Buddy for that project and the manual GitHub commit workflow. The remaining 4 projects unlock for a one-time payment of $79.
Do I need a GCP account for these labs?
Yes, you need a Google Cloud account with the $300 free trial credit (valid 90 days). GCP-PDE uses Dataflow, Cloud Composer, and Vertex AI — three resources with no pause option. Each project includes exact delete/cancel commands; total out-of-pocket cost should be approximately $10-20 across all 5 projects.
How does the GitHub portfolio auto-commit work for GCP-PDE?
When you mark a lab step complete, HandsOnCert commits your SQL queries, Dataflow pipeline code, and Vertex AI training configurations directly to your own GitHub repository via OAuth — giving you real working data engineering code in your portfolio.
How long does the GCP-PDE path take to complete?
The 5 projects take approximately 12 hours total, covering data architecture design, batch processing with BigQuery, streaming with Dataflow, ML operationalization with Vertex AI, and solution quality — the same domains tested on the GCP-PDE exam.
Do I need GCP-ACE before GCP-PDE?
GCP-ACE is not a hard prerequisite for GCP-PDE, but basic GCP familiarity helps since PDE labs use more specialized services. GCP-ACE builds foundational console and IAM skills that support PDE's data-focused work.
Should I take GCP-PDE or GCP-PCA first?
It depends on your career direction. GCP-PDE is specialized for data engineering roles, while GCP-PCA is broader, covering general solution architecture. If you're targeting a data engineering role specifically, go straight to PDE after ACE.
Start Project 1 free — no credit card required. See exactly how the GitHub auto-commit works before you pay anything.
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