Building a Real-Time Analytics Pipeline with AWS Amplify, Kinesis, Lambda, InfluxDB, and Grafana

 

Introduction

Modern applications often need real-time analytics—tracking user activity, events, or metrics as they happen. In this blog, I’ll walk through a production-style architecture where a frontend app sends data to AWS, processes it in real time, visualizes it on dashboards, and also stores raw data for long-term analysis.

Architecture Overview

Amplify App → Kinesis Data Stream → Lambda → InfluxDB → Grafana
                         ↓
                   Firehose → S3

This setup enables:

• ⚡ Real-time dashboards

• 🧱 Durable raw data storage

• 📈 Scalable, serverless ingestion

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Use Case

• Track real-time events from a web/mobile app

• Visualize metrics instantly (active users, events/sec)

• Store all events in S3 for audits or batch analytics

Typical examples:

• IoT telemetry

• User activity tracking

• Live operational metrics

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Components Explained

1. AWS Amplify (Frontend)

AWS Amplify hosts the frontend (React / Next.js / Mobile app). The app sends event data directly to Amazon Kinesis Data Streams using AWS SDK with IAM-based authentication.

Example event payload:

{
  "event": "message_sent",
  "user_id": 29,
  "timestamp": 1765537730069
}

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2. Amazon Kinesis Data Streams

Kinesis acts as the real-time ingestion layer.

Why Kinesis?

• Handles high-throughput streaming data

• Preserves ordering per shard

• Supports multiple consumers

Key configuration:

• Shard count based on throughput

• Retention period (24h–7 days)

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3. AWS Lambda (Stream Consumer)

A Lambda function is triggered by Kinesis records.

Responsibilities:

• Parse incoming events

• Transform data

• Write metrics to InfluxDB

Simplified Lambda logic:

for record in event['Records']:
    payload = base64.b64decode(record['kinesis']['data'])
    data = json.loads(payload)
    write_to_influx(data)

Best practices:

• Batch processing

• Proper error handling

• Idempotent writes

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4. InfluxDB (Time-Series Database)

InfluxDB stores time-series metrics efficiently.

Why InfluxDB?

• Optimized for time-based queries

• High write throughput

• Works perfectly with Grafana

Example measurement:

measurement: online_users
tags: app=amplify
timestamp: event_time
fields: count=1

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5. Grafana (Visualization)

Grafana connects to InfluxDB to visualize data in real time.

Dashboards can show:

• Active users

• Events per second

• Error rates

Benefits:

• Live auto-refresh

• Alerting support

• Multiple data sources

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6. Kinesis Firehose → Amazon S3

In parallel, Kinesis sends data to Firehose, which delivers raw events to S3.

Why Firehose + S3?

• Long-term storage

• Cheap and durable

• Supports Athena, Glue, Redshift later

S3 structure example:

s3://analytics-bucket/events/year=2025/month=12/day=16/

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Data Flow Summary

1. User performs an action in Amplify app

2. Event sent to Kinesis Data Stream

3. Lambda processes records in near real time

4. Metrics written to InfluxDB

5. Grafana displays live dashboards

6. Firehose stores raw events in S3

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Security Considerations

• IAM roles for Amplify and Lambda

• Least-privilege access to Kinesis

• Private networking for InfluxDB

• Encryption at rest (S3, Kinesis)

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Common Challenges & Fixes

Lambda timeout

• Increase memory

• Optimize batch size

InfluxDB connection issues

• Check VPC routing

• Security group rules

High Kinesis cost

• Tune shard count

• Enable Firehose buffering

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Final Thoughts

This architecture is scalable, serverless, and production-ready. It cleanly separates:

• Real-time analytics (InfluxDB + Grafana)

• Long-term storage (S3)

If you’re building real-time systems on AWS, this pattern works extremely well.

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Key Takeaways

• Kinesis is ideal for real-time ingestion

• Lambda simplifies stream processing

• InfluxDB + Grafana = powerful real-time analytics

• Firehose + S3 ensures data durability

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Written from real-world DevOps experience — not just documentation.