SpringOne 2020 Running Persistent Data in a Multi-Cloud Architecture Aditya Tripathi, Product Manager at VMware Judy Wang, Product Manager at VMware

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Running Persistent Data in
a Multi-Cloud Architecture
Considerations for data modernization
Judy Wang
Product Manager, VMware Tanzu SQL
Aditya Tripathi
Product Manager, VMware Tanzu Data Services

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Agenda
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Defining Multi-Cloud
Multi-Cloud Today
How to approach running data on
multi-cloud
Examples

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A Multi-cloud Architecture distributes applications and workloads
across multiple cloud computing environments.

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Running stateless workloads on multi-cloud is a tractable problem
Multi-Cloud Today
LB
GKE (us-east1) GCP (us-west1) GCP (asia-east1)
On-Premise

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Stateful workloads are left behind
Multi-Cloud Today
LB
GCP (us-west1) GCP (asia-east1)
iSeries DB2
On-Premise
Oracle
GKE (us-east1)

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Moving stateful workloads to multi-cloud is complicated
Multi-Cloud Today
LB
GCP (us-west1) GCP (asia-east1)GKE (us-east1)On-Premise
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Identify
Limitations
Identify Business
Outcomes
Brainstorm
Solutions
Prioritize Tradeoffs
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Theory of constraints approach
How to Approach Running Data on Multi-Cloud

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● Disaster Recovery - My apps can recover quickly when a hurricane hits
● High Availability - My apps are always available and serving customers
● Geo-query response time - My apps must achieve a certain level of performance
● Handling bursty traffic load - My apps must efficiently handle high traffic periods
● Data sovereignty - My apps must process data in accordance with laws
● …
Why does my business need multi-cloud?
Identify Business Outcomes

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● Business or Organizational Factors
○ Budget - What can I spend on infrastructure/platform?
○ Influence factors - Leaders, lines of business, organisations that have influence?
○ Skills and Expertise - Does my team know how to use/manage Kafka?
○ Compliance - Legal obligations that apps must abide by?
● Technical Factors
○ Networking limitations - No connectivity between my data centers
○ Hardware limitations - We don’t have data centers in the right regions
○ Heterogeneous app requirements - App 1 needs a document store, App 2 needs transactional data,
App 3 needs to handle large queries, …
○ Managing legacy data platforms - All data still needs to be eventually persisted in mainframe.
Consider starting with more greenfield things?
What factors am I bound by?
Identify Limitations

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● Data consistency vs. Data availability - quick recovery or continuity?
● Maintenance & management trade-offs
○ Complex data solutions == more management woes
○ Capacity planning requirements go away in public cloud
○ Performing failovers by hand (active-passive) or automatically (active-active)
● Vendor trade-offs
○ Services marketplace selection in public clouds
○ Redundancy
■ do you need this for all your apps?
○ Lock-in
■ switching cost and data ownership
What do I want to optimize for?
Prioritize Trade-offs

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● Data replication options
○ Active-active vs active-passive
○ Near-near-far
○ Sync vs async
● Cloud footprint
○ All on-prem vs. all public cloud vs. public-private hybrid
● Data technologies
○ DB caching and replication (Gemfire)
○ Messaging - inherently async
○ Event logs (Kafka) - good for data transformation, heterogeneous systems
○ Hosted highly distributed data solutions (Spanner, Aurora)
What combination of tools and services help me achieve my goals?
Brainstorm Solutions

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● Primary Business Outcome:
Disaster Recovery
(When there is a datacenter-wide outage, the system must recover within 20 min and lose no more than 4hrs of data.)
● Limitations:
○ Team experience - Fairly inexperienced, want to minimize operational responsibility
○ Hardware limitations - Only have one private data center
● Trade-offs
1. Data consistency above all else
2. Minimize management complexity/overhead
3. Availability -- 20 min of downtime is OK
4. Query performance -- sub-second query response is not necessary
Business outcomes, Limitations, and Tradeoffs
Example #1 - Traditional Disaster Recovery

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Potential Architecture
Example #1 - Traditional DR
On-Prem Public Cloud
mysql
mysql
mysql
mysql
mysql
mysql
data replication

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● Primary Business Outcome: Apps need to be geographically co-located with the point of
sale location.
● Limitations:
○ Hardware limitations - PoS locations need to run on lighter, bare-metal environments
○ Network connectivity - PoS locations may periodically lose network connectivity to central environment
○ Inertia of legacy data platforms - All transactions must be stored in the System of Record mainframe
● Trade-offs
1. Availability -- the system must not be unavailable, as customers are waiting at the cash register
2. Query performance -- sub-second query responses are required, as customers are waiting at the cash register
3. Data consistency -- fairly important but OK as long as transactions eventually make it to the centralized system
Business outcomes, limitations, and tradeoffs
Example #2 - Point of Sale

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Nightly cron
batch jobs
Central
prioritise business outcome + tradeoffs + limitations
Example #2 - Point of Sale
SoR
Edge 1
Edge 2 Edge 3

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Public Cloud (AWS, GCP, Azure,...)
Central
prioritise business outcome + tradeoffs + limitations
Example #2 - Point of Sale
SoR
Edge 1
Edge 2 Edge 3

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Public Cloud (AWS, GCP, Azure,...)
Central
prioritise business outcome + tradeoffs + limitations
Example #2 - Point of Sale
SoR
Edge 2 Edge 3
Edge 1

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Please consult an expert if necessary!☺

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Thank You
Judy Wang
juwang@vmware.com
Aditya Tripathi
tripathiad@vmware.com