Advance your career with the Preparing for Google Cloud Certification: Cloud Data Engineer course. This program offers comprehensive training to prepare for the industry-recognized Google Cloud Professional Data Engineer certification. Through hands-on labs and real-world projects, you will gain practical experience in using Google Cloud Platform products.
With a focus on Big Data and Machine Learning, this course covers essential topics such as data analysis, data migration, and data processing on Google Cloud. By the end of the program, you will be equipped to identify the purpose and value of key Big Data and Machine Learning products, employ tools like BigQuery and Cloud SQL, and build resilient streaming analytics systems.
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Prepare for the Google Cloud Professional Data Engineer certification through six comprehensive modules covering Big Data and Machine Learning, modernizing data lakes and warehouses, building batch data pipelines, resilient streaming analytics systems, smart analytics, machine learning, and AI on Google Cloud, and preparing for the Professional Data Engineer journey.
Understand the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning. Design streaming pipelines with Dataflow and Pub/Sub, and explore machine learning solutions on Google Cloud.
Differentiate between data lakes and data warehouses, and explore use-cases for each type of storage. Discuss the role of a data engineer in a cloud environment and examine available data lake and warehouse solutions on Google Cloud.
Review different methods of data loading and learn when to use EL, ELT, or ETL. Build data processing pipelines using Dataflow, Dataproc, Data Fusion, and Cloud Composer on Google Cloud.
Interpret use-cases for real-time streaming analytics, manage data events using Pub/Sub, and run streaming pipelines with Dataflow, BigQuery, and Pub/Sub for real-time analysis on Google Cloud.
Differentiate between ML, AI, and Deep Learning, and discuss the use of ML APIs on unstructured data. Execute BigQuery commands from Notebooks and create ML models using SQL syntax and AutoML on Google Cloud.
Prepare for the Professional Data Engineer (PDE) certification exam by understanding the domains covered and identifying knowledge gaps for each domain.
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