Build & Operationalize Data Processing Systems

• Build & Operationalize Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.
• Build & Operationalize Pipeline: This module requires that the learners demonstrate competence in data cleansing, transformation, batch & streaming, data import & acquisition, as well as integration with the new data sources;
• Build & Operationalize Storage Systems: This part will require the students’ skills and competence in the effective usage of managed services, including Cloud Spanner, CLoug Bigtable, BigQuery, Cloud SQL, Cloud Memorystore, Cloud Datastore, and Cloud Storage. It also covers their skills in managing the data lifecycle and storage performance and costs;

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

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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