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:

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

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.

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Building and operationalizing data processing systems

The following will be discussed here:

• Monitoring pipelines
• Building and operationalizing processing infrastructure
• Transformation
• Batch and streaming
• Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
• Data acquisition and import
• Lifecycle management of data
• Integrating with new data sources
• Building and operationalizing pipelines
• Building and operationalizing storage systems
• Provisioning resources
• Data cleansing
• Validating a migration
• Storage costs and performance
• Adjusting pipelines
• Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
• Testing and quality control
• Building and operationalizing data processing systems
• Awareness of current state and how to migrate a design to a future state

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

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Operationalize ML Models

• Select the Relevant Training & Service Infrastructure: The consideration for this topic includes distributed versus single machine, hardware accelerators (such as TPU and GPU), and edge compute usage;
• Leverage Pre-Built Machine Learning Models as a Service: It covers one’s knowledge and skills in customizing machine learning APIs, including Auto ML text and Auto ML Vision. It also covers the conversational experiences, such as Dialogflow as well as machine learning APIs, including Speech API and Vision API;
• Deploy Machine Learning Pipelines: This objective requires your competence in ingesting relevant data, continuous evaluation, and retraining of ML models (Kuberflow, BigQuery Machine Learning, Cloud Machine Learning Engine, and Spark Machine Learning);
• Measure, Troubleshoot & Monitor Machine Learning Models: The focus of this subtopic includes the effect of dependencies on machine learning models. It will also measure the examinees’ understanding of machine learning terminologies, such as features, regression, labels, classification, models, recommendation, evaluation metrics, and unsupervised & supervised learning. Moreover, it will also assess their knowledge of common sources of error such as assumptions regarding data.

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