Professional Data Engineer
Build and operate the data processing systems behind analytics and ML on Google Cloud, including BigQuery, Dataflow, and Pub/Sub, and track your readiness for the Professional Data Engineer exam.
Advanced
Prepare and test your skills
Advanced
Prepare and test your skills
Professional Data Engineer Study Guides
The Google Cloud Professional Data Engineer certification validates that you can design, build, secure, and monitor data processing systems that make data usable for analysis and machine learning. The exam covers five Domains: designing data processing systems; ingesting and processing data; storing data; preparing and using data for analysis; and maintaining and automating data workloads. Expect questions on choosing and combining services such as BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Storage, and Bigtable.
Domains
Designing for security and compliance means ensuring regulated data can be accessed only by approved identities, decrypted only with approved keys, seen only in approved form, and stored only in appro…
Securing pipeline sources and sinks controls who can act on data, which network paths data can use, and how data stays encrypted. Cloud Dataflow pipelines read from sources such as Cloud Storage b…
Analyzing data access patterns means matching how data is written, read, and retained to the storage layout that scans the least data and spreads load evenly. A workload is described by data type, cur…
Preparing data for visualization means connecting dashboards to governed, fast data in BigQuery so business users see consistent answers without slow queries. Traffic flows from the business intellige…
Optimizing resources means sizing compute and storage to actual business demand and letting capacity expand and contract automatically. Elastic compute comes from Compute Engine, Cloud Run, Dataflow, …