GCP Data Engineer Course
A structured GCP Data Engineer course covering storage, pipelines, ML analytics, security, reliability and architecture, with linked practice questions.
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What you will learn
- Describe data storage and processing.
- Explain data pipelines and orchestration.
- Describe ML and advanced analytics.
- Explain security and compliance.
- Describe reliability, cost and architecture.
Before you start
- Basic Google Cloud knowledge is helpful.
- Some SQL and data concepts are recommended.
- No previous Google certification is required.
Lesson 1 Data Storage and Processing
The Google Cloud Professional Data Engineer exam validates designing, building, and operating data systems on Google Cloud. Data storage and processing starts with choosing the right service. BigQuery is a serverless analytics warehouse, Cloud Storage stores objects, Pub/Sub delivers messages, and Dataflow processes batch and streaming data. Dataproc runs managed Spark and Hadoop, Bigtable provides low-latency NoSQL, Cloud SQL offers managed relational databases, Firestore is a document database, and Spanner provides globally distributed relational data. Engineers choose storage based on latency, consistency, scale, and query patterns.
Example
Example: A team stores raw logs in Cloud Storage, streams events with Pub/Sub, processes them in Dataflow, and loads the results into BigQuery.Lesson 2 Data Pipelines and Orchestration
Data pipelines move and transform data. Dataflow builds ETL pipelines for batch and streaming, Cloud Composer orchestrates workflows with Airflow, and Dataproc can run ephemeral clusters for Spark jobs. Cloud Scheduler runs scheduled jobs, Cloud Functions respond to events, and Pub/Sub subscriptions deliver messages. Cloud Storage triggers start functions when objects change, Dataflow templates make pipelines reusable, and BigQuery load jobs import data. Engineers design idempotent pipelines with retries, monitoring, and clear ownership.
Example
Example: Cloud Scheduler starts a Dataflow template daily, Cloud Composer coordinates dependent steps, and failures trigger retry alerts.Lesson 3 ML and Advanced Analytics
Machine learning and analytics turn data into insight. BigQuery ML creates models with SQL, Vertex AI provides an end-to-end ML platform, Looker Studio builds dashboards, and notebooks support data science. Engineers split data into training and evaluation sets, use batch prediction for large workloads, and store features in feature stores. Model registries manage versions, and hyperparameter tuning finds better settings. The Data Engineer builds reliable pipelines that feed analytics and ML systems.
Example
Example: A team uses BigQuery ML for churn prediction, stores features in a feature store, and schedules batch predictions with Vertex AI.Lesson 4 Security and Compliance
Security and compliance protect data throughout its lifecycle. Cloud IAM controls permissions, Cloud KMS manages encryption keys, and Cloud DLP protects sensitive data. VPC Service Controls creates security perimeters, customer-managed keys give organizations control, and retention policies manage data lifecycle. Cloud Audit Logs record activity, organization policies enforce constraints, and private connectivity keeps traffic internal. Engineers apply least privilege, encryption, data classification, and audit trails.
Example
Example: A healthcare data platform uses IAM, KMS, DLP, VPC Service Controls, and audit logs to protect sensitive datasets.Lesson 5 Reliability, Cost, and Architecture
Reliable data systems balance performance, cost, and governance. Cloud Monitoring collects metrics and alerts, Cloud Logging stores logs, quotas limit usage, and lifecycle rules manage retention. Budget alerts control cost, regions support availability, and retry policies handle failures. Architecture choices include batch and streaming, partitioning and clustering for efficiency, schema evolution, data lakes versus warehouses, star schemas, data governance, and Data Catalog for metadata. Engineers design for exactly-once semantics where possible and document trade-offs.