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Build a Real-Time Cold Chain Tracker on AWS (Free Labs)
5/10TL;DR
A serverless AWS cold-chain monitoring pipeline can catch vaccine shipment temperature breaches in near real time by validating incoming readings, buffering them in a queue, storing current and historical data separately, and sending alerts when temperatures exceed 5°C.
KEY POINTS
A quiet failure the system is designed to catch
Cold-chain failures often happen without any visible incident: a trailer compressor weakens, the temperature drifts upward, and by the time the shipment arrives the readings look normal again. In that scenario, paperwork can appear clean while the cargo is already unusable. The architecture is built to detect those brief excursions while the truck is still on the road.
Two databases answer two different questions
Each truck sends a temperature reading every few seconds as a small JSON record containing a truck ID, timestamp, and temperature. Every reading is written twice: once to DynamoDB for the truck’s latest state, and once to MySQL on Amazon RDS for the full audit history. The split reflects two distinct needs: “What is truck three’s temperature now?” versus “What happened to truck three last week?”
S3 acts as the landing zone
Readings first land in an Amazon S3 bucket under a telemetry path. That design gives the pipeline a durable front door with effectively unlimited ingestion capacity and 11 nines of durability. Even if downstream components fail, the raw readings remain stored and can be replayed later.
DynamoDB stays small and fast
The DynamoDB table uses the truck identifier as its partition key. Each new reading for a truck overwrites the previous item with the same key, meaning five trucks produce five rows, always. That makes current-state lookups extremely fast, typically in single-digit milliseconds, and prevents the table from growing over time.
RDS keeps the historical trail
The historical store is a free-tier MySQL RDS instance sized at 20 GB. Its audit table keeps an auto-incrementing ID plus the truck ID, timestamp, and decimal temperature, with each reading inserted as a new row. That allows SQL queries such as weekly averages, monthly breach counts, and other aggregations that are difficult or inefficient in a key-value database.
IAM roles replace hard-coded credentials
Two AWS Lambda functions perform the processing, and each receives its own IAM role instead of embedded access keys. The ingester can read from S3, send messages to SQS, and write logs. The processor can access SQS, DynamoDB, RDS, SNS, and Secrets Manager, with short-lived credentials issued automatically at runtime.
SQS absorbs bursts and isolates failures
A standard Amazon SQS queue with a 30-second visibility timeout sits between the two Lambda functions, with a dead-letter queue beside it. That queue decouples ingestion from processing so bursts of readings do not overwhelm the consumer. Messages that fail twice are moved aside instead of retrying forever, preventing one bad record from blocking the system.
Validation happens at the edge
The first Lambda is triggered by every object-create event in S3. It downloads the JSON object, checks that all three required fields are present, and forwards only valid payloads to SQS. Invalid records are rejected before they enter the rest of the pipeline.
The processor fans one message into three actions
The second Lambda is triggered from SQS with a batch size of five and a 15-second timeout. For each reading, it retrieves database credentials from Secrets Manager, opens a MySQL connection, writes the latest value to DynamoDB, inserts a new historical row into RDS, and publishes to Amazon SNS if the temperature exceeds 5°C. One reading therefore updates current state, preserves history, and can alert a human immediately.
A lab-scale architecture with production caveats
The design uses eight AWS services and no servers to manage, but it is intentionally simplified. In this version, the database is reachable from the public internet, permissions rely on broad managed policies, and the trucks are simulated by a small Python script sending temperatures between 1°C and 9°C every 3 seconds. A production deployment would typically place the database in a private subnet, narrow IAM permissions, and ingest device traffic through AWS IoT Core or Kinesis.
CONCLUSION
The architecture shows how a serverless, event-driven stack can make cold-chain failures visible before a compromised shipment reaches the dock. Its core principle is simple: keep a fast store for what is happening now, a relational store for everything that happened, and an alert path for the moment either starts to look unsafe.
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