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

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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Building & Operationalizing Data Processing Systems

Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.

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

This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:

• Compute and Storage Fundamentals
• Scaling Data Analytics
• Data Analytics on the Cloud
• Data Processing Architectures
• Introducing Google Cloud Platform ‘
• Machine Learning
• Additional Resources

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