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This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:

• Advanced BigQuery Performance and Functionality
• Creating a Data Lake
• Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
• Bigtable Streaming Features and High-Throughput BigQuery
• Introduction to Building Batch Data Pipelines
• Building a Data Warehouse
• Cloud Dataflow Streaming Features
• Introduction to Data Engineering
• Big Data Analytics with Cloud Al Platform Notebook
• Prebuilt ML Models APIs for Unsaturated Data
• Custom Model building Utilizing Cloud AutoML
• Performing Spark on Cloud Dataproc
• Production ML Pipelines and use of Kubeflow
• Custom Model building Using SQL in BigQuery ML
• Introduction to Processing Streaming Data
• Serverless Messaging Using Cloud Sub/Pub
• Serverless Data Processing with Cloud Dataflow

These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.

Reference: https://cloud.google.com/certification/data-engineer

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