Embark on a comprehensive journey through Google Cloud's Data Engineering, Big Data, and Machine Learning program. This course equips you with the skills to succeed in the industry and prepares you for the Google Cloud Professional Data Engineer certification. Through a series of modules, you will delve into Google Cloud's essential products, including Dataflow, Pub/Sub, Vertex AI, AutoML, BigQuery, and more.
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Enhance your skills in Google Cloud's Data Engineering, Big Data, and Machine Learning program. Explore essential topics, including data-to-AI lifecycle, modernizing data lakes and warehouses, batch data pipelines, resilient streaming analytics, and smart analytics with machine learning and AI.
Identify the data-to-AI lifecycle on Google Cloud and learn about major products of big data and machine learning. Design and build streaming pipelines with Dataflow and Pub/Sub. Explore options to build machine learning solutions with Vertex AI and AutoML.
Understand the differences between data lakes and data warehouses and explore use-cases for each type of storage. Dive into the available solutions on Google Cloud, and learn about the role of a data engineer and the benefits of a successful data pipeline.
Review different methods of data loading and processing. Run Hadoop on Dataproc, build data processing pipelines using Dataflow, and manage data pipelines with Data Fusion and Cloud Composer.
Explore use-cases for real-time streaming analytics, manage data events using the Pub/Sub service, and create streaming pipelines with Dataflow and BigQuery for real-time analysis.
Dive into machine learning, AI, and deep learning concepts. Discuss the use of ML APIs on unstructured data, execute BigQuery commands from Notebooks, and create ML models using SQL syntax in BigQuery and AutoML without coding.
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