PDF Version DemoBuilding & 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.
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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:
- Create ML models using BigQuery ML, APIs, and AutoML.
- 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
- Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
Understanding functional and technical aspects of Google Professional Data Engineer Exam Building and operationalizing data processing systems
The following will be discussed here:
- Lifecycle management of data
- Monitoring pipelines
- Integrating with new data sources
- Storage costs and performance
- Transformation
- Provisioning resources
- Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
- Building and operationalizing data processing systems
- Building and operationalizing pipelines
- Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
- Data cleansing
- Building and operationalizing storage systems
- Data acquisition and import
- Awareness of current state and how to migrate a design to a future state
- Testing and quality control
- Adjusting pipelines
- Building and operationalizing processing infrastructure
- Validating a migration
- Batch and streaming
Reference: https://cloud.google.com/certification/data-engineer
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Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Maintaining and automating data workloads | 18% | - Resource optimization
|
| Ensuring solution quality and reliability | 17% | - Troubleshooting and optimization
|
| Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
| Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
| Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
|
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