This 5-week online specialized course on Google Cloud Platform offers practical training in designing and building data processing systems. Through lectures, demos, and hands-on labs, participants will learn to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning using structured, unstructured, and streaming data.
The course covers skills such as designing and building data processing systems on Google Cloud Platform, leveraging unstructured data using Spark and ML APIs on Cloud Dataproc, implementing automatic scaling data pipelines for batch and streaming data processing using Cloud Dataflow, extracting business analysis insights from large datasets using Google BigQuery, training, evaluating, and predicting with machine learning models using TensorFlow and Cloud ML, and enabling rapid analysis of streaming data.
This specialized course is suitable for developers with experience in managing big data transformations, responsible for tasks such as data extraction, loading, transformation, cleaning, validation, designing data processing pipelines and architectures, creating and maintaining machine learning models and statistical models, executing queries on datasets, visualizing query results, and creating reports.
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This course covers Google Cloud Big Data and Machine Learning Fundamentals, Modernizing Data Lakes and Data Warehouses with GCP, Building Batch Data Pipelines on GCP, Building Resilient Streaming Analytics Systems on GCP, and Smart Analytics, Machine Learning, and AI on GCP.
Google Cloud Big Data and Machine Learning Fundamentals introduces the lifecycle from data to AI, big data analysis with BigQuery, building machine learning solutions on Google Cloud, and understanding machine learning workflows with Vertex AI.
Modernizing Data Lakes and Data Warehouses with GCP covers the differences between data lakes and data warehouses, technical details of available solutions on Google Cloud, the role of data engineers, and the benefits of conducting data engineering in a cloud environment.
Building Batch Data Pipelines on GCP explores various data loading methods, running Hadoop on Dataproc, utilizing Cloud Storage, Dataflow for data processing pipeline construction, and managing data pipelines with Data Fusion and Cloud Composer.
Building Resilient Streaming Analytics Systems on GCP provides an understanding of real-time streaming analytics use cases, managing data events using Pub/Sub, writing streaming pipelines, and interoperating Dataflow, BigQuery, and Pub/Sub for real-time streaming and analysis.
Smart Analytics, Machine Learning, and AI on GCP explores real-time streaming analytics use cases, managing data events using Pub/Sub, writing streaming pipelines, understanding both sides of streaming pipelines, and interoperating Dataflow, BigQuery, and Pub/Sub for real-time streaming and analysis.
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