Build a Real-Time Dashboard Using AWS — Step-by-Step Guide for Data Engineers

In the world of big data and fast-paced digital systems, real-time dashboards have become essential. Whether you’re tracking user behavior on an e-commerce platform, monitoring error logs from your ETL pipeline, or visualizing sales metrics during a flash sale, the ability to see data as it happens gives you a serious edge. In this blog, we’ll walk through a complete, step-by-step process to build a real-time dashboard using AWS services like Kinesis, Lambda, DynamoDB, API Gateway, and more.

What Is a Real-Time Dashboard?

A real-time dashboard is a dynamic data visualization tool that updates instantly or within seconds. It pulls live data from various sources and displays it using interactive charts, graphs, or tables. Real-time dashboards are widely used in industries such as finance, e-commerce, logistics, health monitoring, and IT operations.

Some common use cases include:

  • Tracking website traffic live

  • Monitoring stock prices or cryptocurrency values

  • Observing CPU or memory usage in production environments

  • Logging and alerting errors in ETL data pipelines

  • Real-time sales and order tracking

Traditional dashboards that rely on batch processing can’t meet the demands of fast-moving businesses. That’s where AWS services come into play.

Benefits of Building a Real-Time Dashboard with AWS

Amazon Web Services (AWS) offers fully managed, scalable services that make real-time processing not just possible but practical and cost-effective. Here are a few advantages:

  • Scalability: Handle millions of records per second.

  • Managed Infrastructure: Focus on business logic, not server maintenance.

  • Integration: Seamless integration between AWS services.

  • Cost-effective: Pay only for what you use, ideal for startups and enterprises alike.

  • Security: Built-in IAM roles and encryption features.

Architecture Overview

Let’s outline the architecture we’ll build. We want to collect streaming data, process it in real-time, store it for querying, and display it on a web dashboard.

Components Involved:
AWS ServiceRole in the Architecture
Amazon KinesisIngests real-time streaming data
AWS LambdaProcesses and transforms incoming data
Amazon DynamoDBStores processed data for querying
API Gateway + LambdaExposes APIs for frontend to fetch data
React.js (or similar)Visualizes metrics in real time using APIs
CloudWatch + SNSMonitoring and alerting (optional but useful)

This stack is entirely serverless and can be scaled according to your needs.

 
Step-by-Step Guide to Building the Dashboard
Realtime Dashboard Architecture Diagram
 
 
1. Setting Up Amazon Kinesis Data Stream

Amazon Kinesis is used to collect and process large streams of real-time data. Start by creating a Kinesis stream:

  1. Open AWS Console and navigate to Kinesis → Data Streams.

  2. Click on Create data stream.

  3. Name your stream: real-time-event-stream.

  4. Set the number of shards (start with 1 or 2; you can scale later).

You now have a data ingestion pipeline ready. This stream will capture JSON events in real time.

2. Creating AWS Lambda to Process Data

AWS Lambda will be triggered every time new data arrives in Kinesis. It will transform and push the data into DynamoDB.

Sample Lambda Code (Python):
import json
import base64
import boto3

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('RealTimeMetrics')

def lambda_handler(event, context):
    for record in event['Records']:
        payload = json.loads(base64.b64decode(record['kinesis']['data']))
        table.put_item(Item={
            'metric_id': payload['id'],
            'timestamp': payload['timestamp'],
            'type': payload['type'],
            'value': payload['value']
        })
    return {"statusCode": 200}

Make sure your Lambda has permissions to access both Kinesis and DynamoDB.

3. Setting Up Amazon DynamoDB

Create a DynamoDB table to store processed metrics:

  • Table Name: RealTimeMetrics

  • Partition Key: metric_id (String)

  • Sort Key: timestamp (String or Number)

You can use TTL (Time to Live) to automatically delete old records and manage costs.

4. Exposing Data via API Gateway and Lambda

To allow your frontend to fetch data, you’ll need a backend API.

  1. Go to Amazon API Gateway.

  2. Create a new REST API.

  3. Create a GET method and connect it to another Lambda function.

  4. This Lambda function will query DynamoDB and return JSON data.

To connect an AWS Lambda function with API Gateway, you’ll configure API Gateway to invoke the Lambda when a specific HTTP request (e.g., GET) is made. Here’s a clear step-by-step process:

Step-by-Step: Connect Lambda with API Gateway
✅ Step 1: Create Your Lambda Function

Make sure you already have your Lambda function that queries DynamoDB.

Example name: GetMetricsLambda

✅ Step 2: Create a REST API in API Gateway

  1. Go to API Gateway in AWS Console.

  2. Choose Create API → REST API (not HTTP or WebSocket for this case).

  3. Click Build.

  4. Enter API name (e.g., RealTimeMetricsAPI), and keep other defaults.

  5. Click Create API.

✅ Step 3: Create a Resource and Method

  1. In the Resources panel, click / and choose Create Resource.

    • Resource Name: metrics

    • Resource Path: /metrics

  2. Click on the new /metrics resource.

  3. Click Create Method → choose GET.

  4. In the dropdown, select Lambda Function, then check the box for “Use Lambda Proxy integration”.

  5. Enter your Lambda function name (GetMetricsLambda).

  6. Click Save → OK when prompted to add permissions.

This allows API Gateway to invoke your Lambda securely.

✅ Step 4: Enable CORS (Important for Frontend)

  1. Select the /metrics GET method.

  2. Click Actions → Enable CORS.

  3. Leave default headers (*) and click Enable CORS and replace existing CORS headers.

  4. Click Yes, replace existing values.

✅ Step 5: Deploy the API

  1. Click Actions → Deploy API.

  2. Create a new stage (e.g., prod).

  3. Click Deploy.

You’ll get an API URL like:

https://xyz123.execute-api.us-east-1.amazonaws.com/prod/metrics
✅ Step 6: Test the API

  1. Go to your browser or use curl:

curl https://xyz123.execute-api.us-east-1.amazonaws.com/prod/metrics

  1. You should see JSON data from DynamoDB.

🛡️ IAM Permissions

Ensure your Lambda function has permission to read from DynamoDB, and API Gateway has permission to invoke the Lambda (automatically added if you checked the box earlier).

 
Sample Lambda Code To fetch data from DynamoDb on RestAPI call :
import json
import boto3
from boto3.dynamodb.conditions import Key

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('RealTimeMetrics')

def lambda_handler(event, context):
    response = table.query(
        KeyConditionExpression=Key('type').eq('temperature')
    )
    return {
        'statusCode': 200,
        'body': json.dumps(response['Items']),
        'headers': {'Access-Control-Allow-Origin': '*'}
    }
5. Building the Frontend Dashboard

Using React.js or any modern frontend framework:

  • Call your API every few seconds using setInterval().

  • Display the data using libraries like Recharts or Chart.js.

  • Highlight anomalies or thresholds (e.g., value > 80).

Frontend Code Snippet:
useEffect(() => {
  const fetchData = async () => {
    const response = await fetch("https://your-api-url.amazonaws.com/metrics");
    const data = await response.json();
    setMetrics(data);
  };
  const interval = setInterval(fetchData, 5000);
  return () => clearInterval(interval);
}, []);
Optional Enhancements

To make your dashboard truly enterprise-grade:

  • Add Alerts: Use CloudWatch to monitor anomalies and trigger SNS notifications.

  • Add Authentication: Protect your dashboard with AWS Cognito.

  • Add Filters: Allow users to filter by time, metric type, etc.

  • Optimize Costs: Use on-demand DynamoDB and throttle API Gateway.

 
 
Testing Your Pipeline

Use AWS CLI to simulate data:

aws kinesis put-record \
  --stream-name real-time-event-stream \
  --partition-key "id123" \
  --data '{"id":"m001","timestamp":"2025-06-30T10:00:00Z","type":"cpu","value":88}'

Check CloudWatch logs for Lambda execution and monitor DynamoDB item count.

Real-World Use Cases

Here are some ideas you can implement using this setup:

  • ETL Job Monitoring: Get live status updates from Glue or EMR jobs.

  • IoT Sensor Metrics: Track temperature, humidity, or voltage in real-time.

  • E-Commerce Sales Tracker: Monitor orders during peak sale events.

  • Financial Dashboards: Stream stock tickers or crypto trades.

  • Server Health Monitor: View CPU, memory, and disk usage live.

Example Of a Real-World Case Study: Smart Agriculture Sensor Dashboard

Let’s bring all these concepts to life with a real-world use case that demonstrates the power of real-time dashboards on AWS.

🌾 Scenario: Smart Farming with IoT Sensors

Imagine you’re building a smart agriculture system for a large farm spread across 500 acres. Each plot of land has IoT sensors installed that continuously send the following environmental metrics:

  • Temperature

  • Humidity

  • Soil Moisture

  • Light Intensity

These readings are pushed every 5 seconds from over 1,000 devices across different locations

Realtime Dashboard diagram
 
Final Thoughts

Building a real-time dashboard used to be a complex and expensive task. But with AWS’s managed services, it’s now achievable by anyone — whether you’re an indie developer, startup founder, or enterprise engineer.

This architecture is:

  • Scalable

  • Secure

  • Serverless

  • Fast to deploy

By following this guide, you can create your own real-time analytics platform tailored to your specific needs. Keep experimenting, and consider extending it further with data lakes, ML models, or predictive analytics.