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What is Medlife?
MEDICINE DELIVERY MEDLIFE ESSENTIALSDOCTOR E-CONSULTMEDLIFE LABS
www.pinhealth.com
When did we come into
existence?
First Order
Delivered – May
2015
First Production
Deployment – May
2015
Stage 1 :
Very early
days of Medlife
Stage 2 :
Automation
Stage 3:
Migrating from
Singapore to
Mumbai
Stage 4:
Optimize & align
ourselves to the
larger goal
Stage 5:
Taking Medlife to
the next level
Stage
1
2 tier monolith architecture
Only t2.medium instances
Default AWS VPC
No application level
optimization
Single point of failure
CPU credits would get exhausted
Lot of unwanted visitors
High data transfer costs
Managing configuration was
SETUP SHORTFALLS
Stage
2
Move EC2 instances to private
subnet
Automated zero down-time
deployment during the day
Auto scaling during the spikes
Decouple the front-end & back-end,
move towards multi-tier architecture
Optimizing the application &
database
ECS for micro-services
Introduce Kafka in our architecture
ElasticSearch for search
S3 for storage
Stage
3
REFERENCE LINKS FOR PREVIOUS TALKS
https://www.slideshare.net/PraneshVittal/success-story-of-migrating-entire-
infrastructure-from-aws-singapore-to-aws-mumbai-for-medlifecom
https://www.youtube.com/watch?v=3QvTzRMIxok
https://www.youtube.com/watch?v=zCqcIr8rbyA
1.
2.
3.
Stage
4
Analytics on EMR Cluster
Introduce Apache Airflow
Redis for faster data retrieval
AWS WAF to secure our API end-
points
Multiple databases
GROWTH SCALE
Infrastructure monitoring
ELK & AWS Cloudwatch for
application
monitoring
Carve some of the monolith into
micro-services
VPC flow logs
Forecast your RIs
Keep a check on some of the new
AWS announcements
Don’t neglect any anomalies in your
invoice
VPC end point for S3 cost optimization
Detect idle instances
COST OPTIMIZATION
Build stateless applications to
leverage spot instances
Optimum values for auto-scaling
rules
Right-sizing EC2 instances
Keep exploring interesting projects
on Github
Stage
5
Data lake architecture and
POC on “AWS Lake
Formation”
AI / ML use cases and POC
on AWS SageMaker, Textract
THANK YOU
1. https://in.linkedin.com/in/praneshvittal
2. https://in.linkedin.com/in/prasanna-desai-6b785562

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Medlife journey with AWS

  • 1.
  • 2.
  • 3. What is Medlife? MEDICINE DELIVERY MEDLIFE ESSENTIALSDOCTOR E-CONSULTMEDLIFE LABS www.pinhealth.com
  • 4. When did we come into existence? First Order Delivered – May 2015 First Production Deployment – May 2015
  • 5. Stage 1 : Very early days of Medlife Stage 2 : Automation Stage 3: Migrating from Singapore to Mumbai Stage 4: Optimize & align ourselves to the larger goal Stage 5: Taking Medlife to the next level
  • 7. 2 tier monolith architecture Only t2.medium instances Default AWS VPC No application level optimization Single point of failure CPU credits would get exhausted Lot of unwanted visitors High data transfer costs Managing configuration was SETUP SHORTFALLS
  • 9. Move EC2 instances to private subnet Automated zero down-time deployment during the day Auto scaling during the spikes Decouple the front-end & back-end, move towards multi-tier architecture Optimizing the application & database ECS for micro-services Introduce Kafka in our architecture ElasticSearch for search S3 for storage
  • 11. REFERENCE LINKS FOR PREVIOUS TALKS https://www.slideshare.net/PraneshVittal/success-story-of-migrating-entire- infrastructure-from-aws-singapore-to-aws-mumbai-for-medlifecom https://www.youtube.com/watch?v=3QvTzRMIxok https://www.youtube.com/watch?v=zCqcIr8rbyA 1. 2. 3.
  • 13. Analytics on EMR Cluster Introduce Apache Airflow Redis for faster data retrieval AWS WAF to secure our API end- points Multiple databases GROWTH SCALE Infrastructure monitoring ELK & AWS Cloudwatch for application monitoring Carve some of the monolith into micro-services VPC flow logs
  • 14. Forecast your RIs Keep a check on some of the new AWS announcements Don’t neglect any anomalies in your invoice VPC end point for S3 cost optimization Detect idle instances COST OPTIMIZATION Build stateless applications to leverage spot instances Optimum values for auto-scaling rules Right-sizing EC2 instances Keep exploring interesting projects on Github
  • 16. Data lake architecture and POC on “AWS Lake Formation” AI / ML use cases and POC on AWS SageMaker, Textract
  • 17. THANK YOU 1. https://in.linkedin.com/in/praneshvittal 2. https://in.linkedin.com/in/prasanna-desai-6b785562