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

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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:

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

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

Ensure Solution Quality

• Design for Compliance & Security: The consideration for this topic includes identity & access management such as Cloud IAM. You should also know about data security (including key management and encryption) and privacy assurance (such as Data Loss Prevention API). This part also covers the skills needed in legal compliance, including Health Insurance Portability & Accountability Act, FedRAMP, Children’s Online Privacy Protection Act, and General Data Protection Regulation;
• Ensure Fidelity & Reliability: The applicants should be able to carry out data preparation & quality control (such as Cloud Dataprep), verify and monitor, as well as plan, execute, and stress test data recovery (including rerunning failed jobs, fault tolerance, and retrospective re-analysis performance). Besides that, they should be able to choose between idempotent ACID and eventual consistent prerequisites;
• Ensure Portability & Flexibility: The considerations for this domain include the design for application and data portability, including data residency prerequisites and Multiple-Cloud. It also coves data staging, discovery, and cataloging, as well as mapping to future and current business prerequisites.
• Ensure Efficiency & Scalability: The potential candidates will be required to demonstrate their ability to build and run test suits as well as monitor pipeline, including Stackdriver. It also focuses on their skills related to assessing, improving, and troubleshooting data process infrastructure and data representations. This area will also require that the test takers demonstrate the capacity to resize and autoscale resources;

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