The Google Cloud Professional Data Engineer is able to harness the power of Google’s big data capabilities and make data-driven decisions by collecting, transforming, and visualizing data. Through designing, building, maintaining, and troubleshooting data processing systems with a particular emphasis on the security, reliability, fault tolerance, scalability, fidelity, and efficiency of such systems, a Google Cloud data engineer is able to put these systems to work.
This course will prepare you for the Google Cloud Professional Data Engineer exam by diving into all of Google Cloud’s data services. With interactive demonstrations and an emphasis on hands-on work, you will learn how to master each of Google’s big data and machine learning services and become a certified data engineer on Google Cloud.
This course has been designed and developed following official Google Cloud Professional Data Engineer exam guide. Our Instructor who is a data architect with over 10 years of experience in databases, data architecture, and machine learning will teach you to understand how to ingest data, create a data processing pipelines in Cloud Dataflow, deploy relational databases, design highly performant Bigtable, BigQuery, and Cloud Spanner databases, query Firestore databases, and create a Spark and Hadoop cluster using Cloud Dataproc.
With that said – Welcome to this course .This is a challenging and rewarding certification and we are eager to guide you through the entire process.
Section 1. Designing data processing systems
1.1 Selecting the appropriate storage technologies. Considerations include:
a. Mapping storage systems to business requirements
b. Data modeling
c. Trade-offs involving latency, throughput, transactions
d. Distributed systems
e. Schema design
1.2 Designing data pipelines. Considerations include:
a. Data publishing and visualization (e.g., BigQuery)
b. Batch and streaming data (e.g., Dataflow, Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Pub/Sub, Apache Kafka)
c. Online (interactive) vs. batch predictions
d. Job automation and orchestration (e.g., Cloud Composer)
1.3 Designing a data processing solution. Considerations include: