Career Path
Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.
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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:
- Performing Spark on Cloud Dataproc
- Introduction to Processing Streaming Data
- Bigtable Streaming Features and High-Throughput BigQuery
- Creating a Data Lake
- Serverless Data Processing with Cloud Dataflow
- Big Data Analytics with Cloud Al Platform Notebook
- Serverless Messaging Using Cloud Sub/Pub
- Introduction to Building Batch Data Pipelines
- Custom Model building Using SQL in BigQuery ML
- Production ML Pipelines and use of Kubeflow
- Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
- Prebuilt ML Models APIs for Unsaturated Data
- Introduction to Data Engineering
- Cloud Dataflow Streaming Features
- Building a Data Warehouse
- Custom Model building Utilizing Cloud AutoML
- 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.
Reference: https://cloud.google.com/certification/data-engineer
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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:
- Storage costs and performance
- Data acquisition and import
- Testing and quality control
- Transformation
- Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
- Building and operationalizing data processing systems
- Lifecycle management of data
- Validating a migration
- Data cleansing
- Monitoring pipelines
- Provisioning resources
- Integrating with new data sources
- Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
- Batch and streaming
- Adjusting pipelines
- Building and operationalizing pipelines
- Building and operationalizing processing infrastructure
- Building and operationalizing storage systems
- Awareness of current state and how to migrate a design to a future state
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