Data engineers enable companies to use all the advanced analytics and insights that data science has to provide to integrate with their business. This is achieved by building trust and providing industry-wide access, through sound data infrastructures and architectures, to reliable and accurate data at scale. You can advance your career by becoming a Google Cloud Certified Professional Data Engineer. The annual salary for a Google Cloud Certified Professional Data Engineer can be as high as USD 132,900. This certification will help you make significant career advancements.
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About GCP data engineer
Data engineers will be able to access the cloud. The cloud allows individuals and businesses to explore artificial intelligence solutions. Google Cloud certifications can be a great resource if your goal is to improve your career as a cloud/data engineer, or just to learn more about artificial Intelligence.
The GCP Data Engineer can help you make data-driven decisions by transforming, publishing, and gathering data. They are also familiar with the design, development, implementation, monitoring, safeguarding and monitoring of data processing systems.
Particular emphasis is placed on compliance and security
Scalability and efficiency
Fidelity and reliability
Flexibility and portability.
Exam Format
Google Cloud Professional Data Engineer exam consists of 50 questions. It will take approximately 2 hours to complete. These questions may prove difficult to answer as they are both multiple-choice and select. The registration fees for this exam cost $200 plus applicable taxes. They are available in English and Japanese.
You have 14 days to retake the exam if you fail the first time. You must wait 60 days if you fail the exam the second time. Final note: If you fail the exam the third time, you will need to wait 365 days before you take it again.
Prerequisites for the Exam
Prerequisites are an essential part of any exam. These are the requirements to become a Google Cloud Certified Professional Data Engineer.
The ideal candidate is scalable and efficient.
He or she should have the ability to design and monitor data processing systems with a focus security.
A data engineer must be able to use and train existing machine learning models on an ongoing basis.
Course outline: Google Cloud Professional Data Engineer
You need to focus on the following topics:
1. Designing data processing systems
1.1 Selecting the right storage technology
Mapping storage systems to business needs (Google Documentation:Best practices in enterprise organizations)
Data modeling (Google Documentation,Schema and Data Model,Data model)
Tradeoffs involving latency, throughput, transactions (Google Documentation:Database consistency)
Distributed systems (Google Docation: Using clusters for large scale technical computing in the cloud. Choosing the right architecture to distribute global data is key.
Schema design (Google Documentation:Designing your schema)
1.2 Designing data pipelines.
Data publishing and visualization (e.g., BigQuery) (Google Documentation:Overview of Visual Profiling,Visualizing BigQuery data using Data Studio)
Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka) (Google Documentation:Dataflow,Stream analytics)
Online (interactive) vs. Batch predictions (Google Documentation):Online versus batch prediction
Automation and orchestration of job tasks (e.g. Cloud Composer). (Google Documentation.Cloud Composer).
1.3 Designing a data-processing solution
Choice of infrastructure
System availability and fault tolerance (Google Documentation:Reliability,Overview of the high availability configuration)
Use of distributed systems (Google Docation: Using clusters for large scale technical computing in the cloud. Choosing the right architecture to distribute global data.
Capacity planning (Google Docation:Google Cloud Platform For Data Center Professionals: Compute).
Hybrid cloud computing and edge computing (Google Documentation :Hybrid, multi-cloud architecture patterns).
Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions) (Google Documentation:Pub/Sub)
At least once, in-order, and exactly once, etc., event processing (Google Documentation:Exactly-once processing in Google Cloud Dataflow)
1.4 Data processing and data warehouse migration.
Awareness of current state and how to migrate a design to a future state (Google Documentation:Migration to Google Cloud: Assessing and discovering your workloads,Migration to Google Cloud: Getting started)
Migration from on-premises to the cloud (Data Transfer Service. Transfer Appliance. Cloud Networking. Google Documentation:CLOUD Data TRANSFER.
Validating a migration (Google Documentation:Migration to Google Cloud: Getting started)
2. Data processing systems that can be built and operated
2.1 Designing and operating storage systems
Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore) (Google Documentation:Google Cloud Databases,Cloud Bigtable)
Storage costs and performance (Google Docation:Cloud Storage pricing,Best practices to optimize Cloud Storage cost optimization).
Data lifecycle management (Google Documentation:Object Livecycle Management)
2.2 Construction and operation of pipelines.
Data cleansing (Google Documentation:Cleanse Tasks)
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