Google Professional-Data-Engineer日本語 : Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)

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

The following will be discussed here:

• Capacity planning
• Hybrid cloud and edge computing
• System availability and fault tolerance
• Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
• Schema design
• Choice of infrastructure
• Designing data processing systems
• Tradeoffs involving latency, throughput, transactions
• Data modeling
• Selecting the appropriate storage technologies
• Use of distributed systems
• Online (interactive) vs. batch predictions
• Data publishing and visualization (e.g., BigQuery)
• Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
• At least once, in-order, and exactly once, etc., event processing
• Mapping storage systems to business requirements
• Designing data pipelines
• Job automation and orchestration (e.g., Cloud Composer)
• Distributed systems

How to book Google Professional Data Engineer Exams

The registration for the Google Professional Data Engineer Exam follows the steps given below.

• Step1: Visit the Google Cloud Webassessor Website
• Step3: Search for the exam name Google Professional Data Engineer
• Step4: Take the date of the exam, choose exam center and make further payment using payment method like credit/debit etc.
• Step2: Sign in or sign up to your Google Cloud Webassessor account

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

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:

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

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

The following will be discussed here:

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

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