How To Rebuild a Scalable Data Pipeline with AWS Serverless And Graviton

Learn how a cybersecurity startup cut cloud costs by 95%, automated onboarding, and scaled effortlessly by migrating from EC2 to a fully serverless architecture. 

Takeaways 

• Serverless isn’t just about scaling, when paired with Graviton it can radically cut costs and boost performance. 

• If you’re still running EC2-based data pipelines, modularizing with Lambda and Step Functions can eliminate failure points and speed up delivery. 

• Redshift Serverless can surprise you with huge bills—Athena + S3 is a safer, scalable alternative for most analytics workloads. 

• Automating onboarding with EventBridge and observability with CloudWatch makes your ops leaner and your engineers happier. 

 

When a fast-growing cybersecurity startup hit the limits of its monolithic cloud setup, it didn’t just need an upgrade – it needed a full re-architecture. The goal: eliminate surprise costs, automate customer onboarding, and build for scale. Here’s how a full serverless revamp, powered by Graviton, made that happen.

 

The Problem: Legacy Cloud Stack on the Brink 

This startup was scaling quickly. But their cloud setup? Not so much. 

Everything was running on a private cloud built around EC2 instances and a single PostgreSQL database. Nightly cron jobs and heavy API traffic were hammering the system. Performance slowed. Customers noticed. 

The DevOps team made the smart move to AWS—but early testing with Redshift Serverless triggered a $20,000 bill in just three days. That kind of sticker shock is enough to kill momentum fast. 

On top of runaway costs, the team was struggling with: 

• A monolithic EC2-based architecture that was painful to scale. 

• Manual pipelines for customer onboarding that added operational overhead. 

• A database constantly under pressure from unpredictable API calls. 

They didn’t just need cost optimization. They needed a new operating model. 

 

The Goal: Serverless, Resilient, and Cost-Aware 

The mission was crystal clear: 

• Modernize the architecture with serverless compute and event-driven workflows. 

• Automate the onboarding pipeline to improve speed and consistency. 

• Eliminate cost spikes and improve visibility into cloud spend. 

• Get hands-on cloud expertise to execute with confidence. 

This wasn’t about lifting-and-shifting. It was about rethinking how their infrastructure worked, and who it was built for. 

 

The Fix: Replatforming with Graviton and Serverless AWS Services 

The first move was building a secure, scalable AWS landing zone. Identity and access, tagging strategies, and lifecycle rules were all locked down from day one. Governance and compliance were non-negotiable. 

From there, 2bcloud engineers partnered directly with the startup’s DevOps team to tear down the legacy stack and implement a fully serverless data pipeline. 

The new architecture brought together: 

• Amazon S3 and AWS Glue for a scalable, queryable data lake. 

• Amazon Athena for serverless SQL analytics—pay-per-query, zero infrastructure. 

• AWS Lambda and Step Functions to orchestrate logic in modular, retry-safe chunks. 

• Amazon EventBridge to drive onboarding flows without custom scripts or cron jobs. 

• CloudWatch for unified observability. 

• CloudFront + S3 for lightning-fast global asset delivery. 

• Graviton-powered Lambdas for high-performance, low-cost compute. 

By decoupling everything and leaning into serverless, the team unlocked flexibility without complexity. 

 

Key Architecture Highlights

Component	Technology	Outcome
Data Storage	Amazon S3 + Glue Catalog	Scalable, low-cost data lake
Query Layer	Amazon Athena	Serverless, pay-per-query SQL
App Logic	AWS Lambda + Step Functions	Modular, resilient workflows
Monitoring	CloudWatch	Centralized observability
Scheduling	EventBridge	Event-driven customer onboarding
Delivery	S3 + CloudFront	Fast global access for static assets
Compute	Graviton-powered Lambdas	High performance, low cost

 

 

The Results: 95% Cost Reduction and a Stack That Just Works 

This wasn’t just a tech upgrade—it was a transformation. The startup didn’t just fix the symptoms. They built a foundation for long-term scale. 

Here’s what changed: 

🔻 Infrastructure Costs Dropped by Over 95%: Redshift Serverless was out. Athena and S3 stepped in with cost-effective querying. Graviton-backed Lambdas delivered more performance at a fraction of the price. 

🔁 CI/CD Moved Faster: With modular Lambda functions and automated workflows, the team could ship changes quickly—no waiting on heavy VMs or manual testing. 

🧯 Pipeline Resilience Went Up: Each Lambda function runs in isolation, with built-in retry logic and failure tracing via Step Functions. A single error doesn’t take down the entire flow anymore. 

📊 Observability Got Real: With structured logging in CloudWatch and centralized metrics, the team moved from gut instinct to real insight. 

🧊 Storage Was Optimized: Lifecycle rules automatically push stale data to Glacier. Long-term storage costs dropped by up to 90%. 

 

The Business Impact: Faster Onboarding, Smoother Ops, and Real Savings 

This wasn’t just a win for DevOps, it was a win for the business. 

• New customers are onboarded automatically through EventBridge schedulers. 

• Support teams rely on actual logs and metrics instead of flying blind. 

• Costs are predictable, aligned to usage, and trackable through dashboards. 

• Innovation cycles are faster, because there’s no more tech debt to untangle. 

What used to be a patchwork of scripts and legacy cron jobs is now a smooth, observable, auto-scaling system that’s ready for what’s next. 

 

Conclusion 

Monolithic cloud stacks might work—for a while. But if you’re hitting walls with scale, speed, or spend, it’s time to rethink. 

This startup did. They moved to AWS. Went serverless. Used Graviton. And partnered with engineers who know how to deliver. The payoff? Lower costs, happier users, and room to grow. 

 

Ready to level up your cloud architecture? 

Let’s talk.
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