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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:
- Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
- Advanced BigQuery Performance and Functionality
- Introduction to Building Batch Data Pipelines
- Cloud Dataflow Streaming Features
- Serverless Data Processing with Cloud Dataflow
- Custom Model building Using SQL in BigQuery ML
- Performing Spark on Cloud Dataproc
- Creating a Data Lake
- Serverless Messaging Using Cloud Sub/Pub
- Custom Model building Utilizing Cloud AutoML
- Building a Data Warehouse
- Introduction to Processing Streaming Data
- Production ML Pipelines and use of Kubeflow
- Prebuilt ML Models APIs for Unsaturated Data
- Introduction to Data Engineering
- Bigtable Streaming Features and High-Throughput BigQuery
- Big Data Analytics with Cloud Al Platform Notebook
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.
The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.
Reference: https://cloud.google.com/certification/data-engineer
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