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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:

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

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

Google Cloud Big Data & Machine Learning Fundamentals course

This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:

• Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud
• Hire BigQuery and Cloud SQL for interactive data analysis
• Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
• Create ML models using BigQuery ML, APIs, and AutoML.

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