Embark on a five-week journey to master the intricacies of data engineering on the Google Cloud Platform. Through a blend of presentations, demonstrations, and hands-on labs, this course equips participants with the skills to design and construct data processing systems, create comprehensive data pipelines and analytics, and develop machine learning solutions. The curriculum covers processing both batch and streaming data, utilizing Google BigQuery for deriving business insights, and harnessing tools like TensorFlow and Cloud ML for machine learning. Aimed at experienced developers responsible for managing Big Data transformations, this course provides a robust foundation in the domain of data engineering in a cloud environment.
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This course comprises five modules that cover the fundamentals of data engineering, including Big Data and machine learning, modernizing data lakes and warehouses, building batch and streaming data pipelines, and integrating machine learning in data pipelines on the Google Cloud Platform.
Embark on a journey to understand the lifecycle of data for AI on the Google Cloud Platform and the key Big Data and machine learning products. Analyze Big Data at scale with BigQuery, create machine learning solutions, and explore the workflow of machine learning with Vertex AI.
Gain insights into the differences between data lakes and data warehouses, their use cases, and the role of a data engineer in a cloud environment. Understand why data engineering should be developed in a cloud environment.
Explore various data loading methods and execute Hadoop on Dataproc, utilize Cloud Storage, and optimize Dataproc jobs. Use Dataflow to create data processing pipelines and manage them with Data Fusion and Cloud Composer.
Understand the use cases for real-time streaming analytics, utilize Pub/Sub for managing data events, create streaming pipelines, and interoperate Dataflow, BigQuery, and Pub/Sub for real-time streaming and analytics.
Discover multiple ways to include machine learning in data pipelines on the Google Cloud Platform, from using AutoML for less customized needs to AI Platform Notebooks and BigQuery Machine Learning for more personalized solutions. Learn to create ML models using QwikLabs.
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