Google Professional Data Engineer

Course Overview The Google Professional Data Engineer course by Stalwart Learning empowers professionals to design, build, operationalize, and secure data processing systems on Google Cloud. This comprehensive training covers the…

Created by

Stalwart Learning

Category

Date & Time

Price

Duration

5 Days / 40hrs

Location

https://stalwartlearning.com/

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Course Description

The Google Professional Data Engineer course by Stalwart Learning empowers professionals to design, build, operationalize, and secure data processing systems on Google Cloud. This comprehensive training covers the management of scalable data pipelines, advanced analytics, machine learning integration, and best practices for optimizing data-driven solutions. Participants will be well-prepared to pass the Google Professional Data Engineer certification exam, equipping them with the skills needed to excel in data engineering roles.

40 hours (5 days)

  • Familiarity with data analysis and data warehousing concepts.
  • Basic knowledge of Google Cloud services and SQL.
  • Programming experience in Python or Java is recommended.
  • Overview of data engineering and Google Cloud’s ecosystem
  • Understanding big data, analytics, and machine learning workflows
  • Navigating Google Cloud Console and SDK
  • Introduction to Apache Beam and Cloud Dataflow
  • Designing batch and stream processing pipelines
  • Managing data transformations and event processing
  • Working with Cloud Storage, BigQuery, and Cloud SQL
  • Designing optimal storage solutions for structured and unstructured data
  • Implementing data lifecycle management and archival strategies
  • Utilizing Cloud Dataproc for Hadoop and Spark workloads
  • Managing ETL processes with Data Fusion
  • Implementing real-time data streaming with Pub/Sub
  • Leveraging BigQuery for advanced analytics and SQL queries
  • Integrating ML models with AI Platform and BigQuery ML
  • Exploring Vertex AI for end-to-end machine learning workflows
  • Monitoring and tuning data pipelines for efficiency
  • Managing resource allocation and cost optimization strategies
  • Best practices for scaling data solutions
  • Implementing IAM roles and permissions for data access
  • Managing encryption and securing data in transit and at rest
  • Ensuring compliance with GDPR, HIPAA, and other regulations
  • Understanding the certification exam format and objectives
  • Hands-on labs and mock scenarios for practice
  • Tips and strategies for success on the Professional Data Engineer exam

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