Google Professional Data Engineer Question 38
Select 3Google Cloud PlatformYou are designing a data processing solution on Google Cloud for a retail company that requires flexibility and portability. The company wants to avoid vendor lock-in and ensure the solution can be easily migrated to other cloud platforms if needed. Which of the following actions align with designing a solution for flexibility and portability?
- A
Use managed services like BigQuery and Cloud Dataflow exclusively to take advantage of Google Cloud's optimized performance.
- B
Design the solution using open-source tools such as Apache Beam and Kubernetes for cross-platform compatibility.
- C
Store data in open formats like Parquet or Avro instead of proprietary formats.
- D
Incorporate Google-specific APIs and libraries for faster development and integration with Google Cloud.
- E
Use containerized applications and deploy them via Google Kubernetes Engine (GKE) to enable portability.
Show answer and explanation
Correct answers: B, C, E
Explanation
Designing for flexibility and portability involves minimizing dependencies on specific cloud provider services and leveraging solutions that allow easy migration and platform independence. Open-source tools, open data formats, and containerization are key strategies for achieving this, as they are widely supported across various platforms and environments.
- A. Incorrect.
Using managed services like BigQuery and Cloud Dataflow can provide optimized performance on Google Cloud but can lead to vendor lock-in, making migration to other platforms more challenging.
- B. Correct.
Open-source tools like Apache Beam and Kubernetes are designed with platform independence in mind. Apache Beam provides a unified programming model for processing data across environments, while Kubernetes allows portability of containerized workloads.
- C. Correct.
Storing data in open formats like Parquet or Avro ensures that the data can be read and processed by a wide range of tools and platforms, making the solution more flexible and portable.
- D. Incorrect.
Google-specific APIs and libraries may streamline development on Google Cloud but generally increase dependency on the platform, reducing overall portability.
- E. Correct.
Using containerized applications and deploying them on GKE enables portability, as containers can be moved to other Kubernetes-supported environments, such as on-premises or other cloud providers.