This document outlines the stages of development for Medlife, an Indian healthcare startup. Stage 1 describes the early days when Medlife had a simple two-tier monolith architecture on AWS with few optimizations and manual deployment processes. Stage 2 focused on automating deployments, adding auto-scaling, separating front-end and back-end into a multi-tier architecture, and introducing services like ECS, Kafka, and Elasticsearch to optimize the application and architecture. Stage 3 saw Medlife migrate operations from Singapore to Mumbai while future stages involved analytics, monitoring, cost optimization, and plans to introduce machine learning.

2. What is Medlife?
MEDICINE
DELIVERY
MEDLIFE
ESSENTIALS
DOCTOR E-
CONSULT
MEDLIFE LABS
www.pinhealth.com

3. First Order
Delivered – May 2015
When did we come into existence?
First Production
Deployment – May
2015

4. Stage 1 :
Very early
days of
Medlife
Stage 2 :
Automatio
n
Stage 3:
Migrating from
Singapore to
Mumbai
Stage 4:
Optimize & align
ourselves to the
larger goal
Stage 5:
Taking Medlife
to the next
level

5. Stage 1

6. 2 tier monolith architecture
Only t2.medium instances
Default AWS VPC
No application level optimization
Deployment using shell scripts
Single point of failure
CPU credits would get exhausted
Lot of unwanted visitors
High data transfer costs
Managing configuration was
tedious
SETUP SHORTFALLS

7. Stage 2

8. 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

9. Stage 3

10. REFERENCE LINKS FOR PREVIOUS TALKS
Slidedeck
https://www.youtube.com/watch?v=3QvTzRMIxok
https://www.youtube.com/watch?v=zCqcIr8rbyA
1.
2.
3.

11. Stage 4

12. 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

13. 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

14. Stage 5

15. Data lake architecture and
POC on “AWS Lake
Formation”
AI / ML use cases and POC
on AWS SageMaker, Textract

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17. THANK YOU
1. https://in.linkedin.com/in/praneshvittal
2. https://www.linkedin.com/in/prajithp/