Cloud Technology

What are the pros and cons of using a Hybrid Cloud for data-heavy apps?

KA Asked by Karen Taylor · 10-02-2023
0 upvotes 7,907 views 0 comments
The question

Our data science team needs to process massive datasets, but our compliance team is hesitant to move everything to the public cloud. We are considering a Hybrid Cloud model. What are the common pitfalls regarding data latency and synchronization between on-premise servers and the public cloud (AWS/GCP)? 

3 answers

0
DO
Answered on 12-02-2023

The biggest pitfall in Hybrid Cloud is "Gravity." Data is heavy, and moving petabytes between your data center and AWS over a standard internet connection will lead to massive latency and egress fees. You must invest in a dedicated connection like AWS Direct Connect or Azure ExpressRoute. For synchronization, look into Edge Computing solutions like AWS Outposts or Azure Stack. These allow you to run cloud-native services physically in your own data center. This solves the compliance issue (data stays on your hardware) while providing the same API and developer experience as the public cloud, allowing your data science team to use tools like SageMaker without the latency of the open web.

0
BR
Answered on 15-02-2023

How are you planning to handle Data Sovereignty laws if your hybrid model spans across different geographic regions or countries?

 

RI 18-02-2023

Brian, that's a crucial point. Karen, you should use Metadata Tagging to ensure that sensitive data never leaves its designated region during the synchronization process. We use an automated "Data Guardrail" that checks the classification of a dataset before allowing it to be replicated to the public cloud. This keeps the compliance team happy. Also, consider using a Distributed File System like MinIO or Ceph that can bridge on-prem and cloud storage seamlessly, providing a unified S3-compatible interface for your data scientists regardless of where the bits actually live.

0
JE
Answered on 20-02-2023

Hybrid cloud is great for "Cloud Bursting." You keep your steady-state workloads on-prem to save money and only "burst" into the public cloud when you need extra compute power for massive training jobs.

 

KA 22-02-2023

Exactly, Jennifer. It’s the best of both worlds—the cost stability of on-prem with the infinite scalability of the public cloud when you really need it.

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