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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:

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

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.

This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:

• Data Processing Architectures
• Compute and Storage Fundamentals
• Scaling Data Analytics
• Introducing Google Cloud Platform ‘
• Data Analytics on the Cloud
• Machine Learning
• Additional Resources

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

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Data Engineering on Google Cloud course

It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:

• Designing and building data processing systems on the Google Cloud Platform
• Influencing unstructured data using ML APIs on Cloud Dataproc
• Enable insights from streaming data
• Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
• Predicting machine models using TensorFlow and Cloud ML

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