Google Cloud Certified Professional Data Engineer (2026)

About this course

Designing data processing systemsSelecting the appropriate storage technologies. Considerations include:●  Mapping storage systems to business requirements●  Data modeling●  Trade-offs involving latency, throughput, transactions●  Distributed systems●  Schema designDesigning data pipelines. Considerations include:●  Data publishing and visualization (e.g., BigQuery)●  Batch and streaming data (e.g., Dataflow, Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Pub/Sub, Apache Kafka)●  Online (interactive) vs. batch predictions●  Job automation and orchestration (e.g., Cloud Composer)Designing a data processing solution. Considerations include:●  Choice of infrastructure●  System availability and fault tolerance●  Use of distributed systems●  Capacity planning●  Hybrid cloud and edge computing●  Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)●  At least once, in-order, and exactly once, etc., event processingMigrating data warehousing and data processing. Considerations include:●  Awareness of current state and how to migrate a design to a future state●  Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)●  Validating a migrationBuilding and operationalizing data processing systemsBuilding and operationalizing storage systems. Considerations include:●  Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Datastore, Memorystore)●  Storage costs and performance●  Life cycle management of dataBuilding and o

Course objectives

• equip students with the skills needed to design and implement data processing solutions
• prepare students for the Google Cloud Certified Professional Data Engineer exam
• teach effective data pipeline construction and migration techniques