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

6. Stage
1

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

8. Stage
2

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

10. Stage
3

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.

12. Stage
4

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

15. Stage
5

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