You have recently joined a startup company building sensors to measure street noise and air quality in urban
areas. The company has been running a pilot deployment of around 100 sensors for 3 months each sensor
uploads 1KB of sensor data every minute to a backend hosted on AWS.
During the pilot, you measured a peak or 10 IOPS on the database, and you stored an average of 3GB of
sensor data per month in the database.
The current deployment consists of a load-balanced auto scaled Ingestion layer using EC2 instances and a
PostgreSQL RDS database with 500GB standard storage.
The pilot is considered a success and your CEO has managed to get the attention or some potential investors.
The business plan requires a deployment of at least 100K sensors which needs to be supported by the
backend. You also need to store sensor data for at least two years to be able to compare year over year
To secure funding, you have to make sure that the platform meets these requirements and leaves room for
further scaling. Which setup win meet the requirements?
Add an SQS queue to the ingestion layer to buffer writes to the RDS instance
Ingest data into a DynamoDB table and move old data to a Redshift cluster
Replace the RDS instance with a 6 node Redshift cluster with 96TB of storage
Keep the current architecture but upgrade RDS storage to 3TB and 10K provisioned IOPS