Part 6: Results, Team Presentation, and Key Lessons Learned
Results
Overall we have evolved from a Legacy RDBMS --> DynamoDB --> DynamoDB + Kinesis Solution and here are some metrics captured:
| Metric | Legacy RDBMS Solution | DynamoDB + Kinesis Solution |
|---|---|---|
| Jobs Processed Per Day | ~200 | >500 |
| Complex Job Mix | Baseline | +30% |
| Scalability | Fixed Capacity | Horizontally Scalable |
| Data Availability | Batch-Oriented | < 5 Minutes |
| Concurrent Processing | Limited | Significantly Higher |
AWS Event Presentation
The architecture and scalability journey described in this article was also presented at an AWS community event by members of our engineering team.
As the Principal Architect for the platform, I had the opportunity to contribute to the overall architecture, design decisions, and technical direction of the solution. However, I consciously chose not to participate as a presenter, instead providing an opportunity for members of the team to share their work, present the challenges we faced, and showcase the engineering solutions we implemented together.
I strongly believe that creating visibility and growth opportunities for engineers is an important aspect of technical leadership. Seeing the team confidently present the architecture, lessons learned, and business outcomes to a broader audience was particularly rewarding.
You can watch the session recording here:
Session Recording: Video Presentation on AWS Events
Key Lesson
Kinesis provides an effective buffer between data producers and DynamoDB, but it should not be viewed as an unlimited queue. The overall system remains constrained by the slowest component in the pipeline. Effective capacity planning, monitoring, and automated scaling are essential to ensure that transient traffic spikes do not evolve into sustained bottlenecks.