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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:
- Introduction to Processing Streaming Data
- Bigtable Streaming Features and High-Throughput BigQuery
- Custom Model building Utilizing Cloud AutoML
- Big Data Analytics with Cloud Al Platform Notebook
- Serverless Messaging Using Cloud Sub/Pub
- Prebuilt ML Models APIs for Unsaturated Data
- Cloud Dataflow Streaming Features
- Advanced BigQuery Performance and Functionality
- Serverless Data Processing with Cloud Dataflow
- Introduction to Data Engineering
- Custom Model building Using SQL in BigQuery ML
- Performing Spark on Cloud Dataproc
- Building a Data Warehouse
- Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
- Production ML Pipelines and use of Kubeflow
- Introduction to Building Batch Data Pipelines
- Creating a Data Lake
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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Career Opportunities
The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.







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