Note - SPPU's Degreeplus Beta Version is up, stay tuned for more details.
Note - SPPU's Degreeplus Beta Version is up, stay tuned for more details.

Course

Cloud Data using GCP

21 hours 30 minutes

Credits: Optional Learning

Description

This course teaches learners how to design, build, and manage data engineering solutions on the Google Cloud Platform. Topics include platform orientation, designing data processing systems, storing data with BigQuery and OLTP solutions, ingesting and processing structured, unstructured, and semi-structured data, and preparing and automating data workloads through migrations, monitoring, and machine learning integration. The curriculum combines instructional courses with intermediate-level skill benchmarks aligned to each core competency domain.

What Students Will Learn

  • GCP Data Engineer Pro: Becoming a Google Cloud Data Engineer
  • GCP Data Engineer Pro: Navigating the Google Cloud Platform

  • GCP Data Engineer Pro: Creating a Pipeline of Services
  • GCP Data Engineer Pro: Building Robust Data Structures
  • GCP Data Engineer Pro: Messaging with Pub/Sub
  • GCP Data Engineer Pro: Using Google Datastream
  • Google Cloud Professional Data Engineer: Designing Data Processing Systems Competency (Intermediate Level)

  • GCP Data Engineer Pro: BigQuery Data Warehouse
  • GCP Data Engineer Pro: Google Cloud OLTP Structured Data Storage
  • GCP Data Engineer Pro: Optimizing a Google Data Warehouse
  • Google Cloud Professional Data Engineer: Storing Data Competency (Intermediate Level)

  • GCP Data Engineer Pro: Google Cloud Unstructured Data
  • GCP Data Engineer Pro: Google Cloud Semi-structured Data
  • GCP Data Engineer Pro: Dataset Processing
  • Google Cloud Professional Data Engineer: Ingesting and Processing Data Competency (Intermediate Level)

  • GCP Data Engineer Pro: Data Migrations
  • GCP Data Engineer Pro: Monitoring and Troubleshooting Data Warehouses
  • GCP Data Engineer Pro: Google Machine Learning and AI
  • Google Cloud Professional Data Engineer: Preparing and Automating Data Workloads Competency (Intermediate Level)

  • Official Google Cloud Certified Professional Data Engineer Study Guide

Overall Learning Outcomes

  • Navigate the Google Cloud Platform and understand the role of a cloud data engineer
  • Design data processing pipelines, robust data structures, and messaging systems using Pub/Sub and Datastream
  • Build and optimize data storage solutions using BigQuery and OLTP structured data storage
  • Ingest and process unstructured, semi-structured, and dataset-based data workloads
  • Manage data migrations, monitor and troubleshoot data warehouses, and integrate machine learning/AI solutions
  • Demonstrate intermediate-level competency in designing data processing systems, storing data, ingesting/processing data, and preparing/automating data workloads