GCP Data Engineer Pro study guide
GCP Data Engineer Pro · Work through your exam, one topic at a time.
The Professional Data Engineer certification is Google Cloud's credential for practitioners who build and operate data platforms: collecting, transforming, storing, and delivering data for analytics, applications, and downstream teams. It is aimed at data engineers and data architects who already work with pipelines, warehouses, and streaming systems and want independent validation of that skill rather than an entry-level badge. The GCP Data Engineer Pro study guide has 5 sections and about 1,558 words.
01Design Data Processing Systems
Every design question reduces to a few axes: batch or streaming, latency SLA, whether the transformation is mostly SQL or mostly code, and whether you are migrating an existing Hadoop/Spark estate. Dataflow (Apache Beam) is the answer for stateful, event-time streaming and unified batch/streaming pipelines.
299 words, about 1 min02Ingest and Process the Data
For online bulk copies use Storage Transfer Service (GCS, S3, HTTP sources; scheduled, bandwidth-controlled). For petabytes over a constrained link use Transfer Appliance - offline shipping beats any tuned rsync.
287 words, about 1 min03Store the Data
Cloud Storage is the landing zone and cheapest durable tier: know the classes (Standard, Nearline 30-day, Coldline 90-day, Archive 365-day minimums), Autoclass, Object Lifecycle Management, bucket retention and lock, and dual- versus multi-region. BigQuery is the warehouse: choose ingestion-time versus column partitioning, cluster on the columns you filter on (order matters, up to four), and set require partition filter to prevent full scans.
314 words, about 2 min04Prepare and Use Data for Analysis
The modern pattern is to land raw data in GCS or BigQuery and transform with SQL: Dataform or dbt for versioned, tested, dependency-aware models, with assertions feeding data-quality metrics. Dataplex and Data Catalog provide discovery, lineage, and quality scans.
315 words, about 2 min05Maintain and Automate Data Workloads
Cloud Composer (managed Airflow) is the default for multi-step DAGs across services: scheduling, dependencies, retries, SLAs, and backfills. Set catchup=False deliberately rather than by accident, use retries with exponential backoff, and prefer deferrable or reschedule-mode sensors so idle waiting does not pin worker slots.
343 words, about 2 min