---
title: "Building Real-Time Live Sports Viewer Analytics with Tinybird and AWS"
excerpt: "Showing real-time stats to millions of viewers without crashing? It's possible. Here's the architecture that won't drain your budget."
authors: "Ariel Perez"
categories: "Scalable Analytics Architecture"
createdOn: "2024-12-03 00:00:00"
publishedOn: "2024-12-03 00:00:00"
updatedOn: "2025-04-24 00:00:00"
status: "published"
---
Ever tried to show millions of viewers real-time stats about how many other people like them are watching the same event? It's a bit like trying to count grains of sand while they're being poured into your bucket. Fun times! Let's look at how to build this without breaking the bank (or your sanity).
The Challenge: Fan Engagement at Massive Scale
Imagine you're streaming a major live event and want to show each viewer some engaging stats:
How many people in their state are watching?
How many fans of their team are tuned in?
What's the total viewer count in their country?
What's the global audience size?
Sounds simple? Well, here's the catch - you need to handle:
3.3M concurrent viewers
350,000 events/second
17 GB/minute of incoming data
Globally distributed
And keep it all fresh within an average of 5 seconds (we're optimizing for cost here!)
Drawing inspiration fromhow FOX Sports built their near real-time internal analytics at massive scale, we're going to take it a step further. While their architecture excelled at delivering internal BI analytics, we want to extend it to power real-time viewer segmentation and engagement features for millions of concurrent viewers. We'll show you how to build upon their robust foundation to create engaging, personalized experiences.
The AWS Well-Architected Solution
First, let's look at how you might build this with AWS services. It follows AWS Well-Architected principles to ensure reliability and scale:
An architecture that separates concerns for reliability and scale while keeping costs in check
Component Design & Optimization
The architecture is carefully tuned to balance performance, reliability, and cost. Each choice introduces additional complexity but serves a specific performance or cost goal:
Infrastructure Choices
Kinesis Data Streams with on-demand capacity handles unpredictable traffic patterns with 1ms ingestion latency
Lightweight Lambda functions (128MB) provide 100ms processing time for JSON handling while keeping costs low
CloudFront Functions reduce latency to single-digit milliseconds for JWT validation compared to Lambda@Edge
HTTP API instead of REST API cuts request latency by 60% for simple authentication needs
Request Optimization
JWT validation at CloudFront enables request coalescing, critical for handling traffic spikes
Reduces backend load from millions to thousands of requests per second
Without this optimization, DynamoDB costs would be 3-4x higher with potential throughput issues
Each layer adds complexity but is necessary for cost management at scale
Storage Strategy
Hot path: DynamoDB delivers consistent sub-10ms reads for real-time data
Cloudfront provides low double-digit latencies via globally distributed PoPs
Cold path: S3 with Parquet format (7:1 compression ratio) optimizes for analytical queries
Geographic partitioning reduces Athena query costs by up to 90% for location-based analytics
Each storage tier requires different access patterns and maintenance strategies
Operational Efficiency
4-hour runtime window for streaming components saves 80% on Kinesis costs
30-day data retention balancing analysis needs with storage costs
10-second cache TTL cuts backend requests by 95% while maintaining reasonable freshness
Each optimization requires careful monitoring and adjustment
Data Flow
User actions hit Kinesis Data Streams (4 of them!)
Kinesis Analytics does the real-time number crunching
Then things get interesting: - Need instant stats? That's the DynamoDB path (read more about this approach here) - Building dashboards? Off to RDS Postgres and JSON in S3 + Athena you go - Want historical analysis? Parquet on S3 and Athena have your back
And it'll cost you about $1,588.30 (we'll break down those numbers in detail later).
The Tinybird Approach
Remember that complex AWS architecture we just looked at? Here's the same thing with Tinybird:
Not a typo - it's really that simple! And here's the kicker:
Performance Characteristics
Consistent performance whether you're querying last minute's or last month's data
Near Real-time Stats (User App Path):
Freshness: 2-3 seconds
Query Response: 10-25 milliseconds
Optimized Aggregate Analytics (BI Tool Path):
Freshness: 3 seconds
Query Response: 1 second
Ad-Hoc Analytics (BI Tool Path):
Freshness: 2 seconds
Query Response: 6 seconds
Operational Benefits and Cost Efficiency
Simplified Total Cost of Ownership
No separate paths needed for real-time vs historical data
Fewer moving parts means fewer things to break
Simpler architecture makes troubleshooting a breeze
When something goes wrong, you're not playing detective across multiple services
Rapid Development and Iteration
Need a new feature? Just write SQL
Want to transform data differently? SQL
Need to expose a new API endpoint? You guessed it - SQL
Streamlined Feature Development Want to add new analytics? Let's see how it works in both approaches:
AWS Approach:
Modify Kinesis Analytics application
Update Lambda functions
Add new DynamoDB tables/indexes
Update the streaming aggregates in Postgres
Modify the batch processing pipeline
Test each component separately
Hope everything still works together
Tinybird Approach:
Then publish it as an API endpoint. Done!
How much does it cost?
Total Monthly Cost: $1,270.91
That’s 20% less than the AWS Well-Architected Solution! To be fair, all the costs are estimated based on the expected workloads and the current published prices for both AWS and Tinybird but, even within the margins of error the Tinybird implementation puts less strain on your budget. Run this for an entire month and the differences are even more dramatic.
Implementation Considerations
Go With AWS If:
Pick Tinybird When:
You're already deep in the AWS ecosystem
You want sub-second query latency without the headache
Your team dreams in Lambda functions
Your team prefers writing SQL to managing infrastructure
You need very specific control over data storage locations
You need to iterate quickly on new analytics features
You enjoy building and maintaining complex pipelines (hey, some people do!)
You don't want to manage numerous moving parts
The Bottom Line
Both approaches can handle the scale - that's not the question. The real decision comes down to what you value more: operational and architectural simplicity or operational and architectural uniformity. If you're building something new, Tinybird's approach lets you move faster and sleep easier. But if you're heavily invested in AWS services, their solution, while more complex, might fit better into your existing workflow.
Appendix
Detailed Cost estimates
The costs for both approaches are based on published on-demand pricing - both platforms offer discounts if you're willing to commit long-term.
AWS Approach
Path/Component
Cost
Notes
Total Live Event Cost
$1,592.29
Ingestion
$653.44
Kinesis Data Streams
$653.44
Kinesis Data Streams (Data In)
$326.40
17GB/min * 240 min * $0.08/GB
Kinesis Data Streams (Data Out)
$326.40
17GB/min * 240 min * $0.04/GB * 2 (1/4 * 4 Windowed KDAs + Raw Firehose)
