Why am I building Traceblade: Motivation and MVP Progress
Tackling Scattered Data Challenges, One Step at a Time
Frontend Software Engineer with a passion for unraveling the intricacies of software and systems. Armed with a B.Tech in Mech Engg and an MS in Engg Management from SJSU. My journey spans VFX, tech logistics, and automotive retail, always seeking the harmony between technology and practical application
User event tracking and logging is one of the most crucial instrument for understanding user behaviour and ensuring system stability. In my day-to-day job I found myself juggling multiple tools to connect the dots between a user event and related error. This process was not only time consuming but also frustrating, as the data was scattered across various platforms.
While building AI Assisted Grading Tool , I searched for solutions that could provide both user events and error logs(this is the main focus) in one place- a unified view. Unfortunately, I couldn’t find a tool that met this need without breaking the bank.
Thats when I realised it would be much more efficient(and cost-effective) to build a system tailored to this exact problem- one that bridges the gap between user event tracking and logging. as someone balancing the roles of a product manager and a developer for AI Assisted Grading Tool, I knew this tool could save me time and effort while solving a widespread challenge.
Building the MVP
With Traceblade, I set out to bridge the gap between user events and logs. The first version focuses mainly on the fundamentals: capturing user events and logs independently. While the unified view is still in progress, the foundation is a critical step.
Things I needed for a solid foundation.
An ingestion pipeline
The main focus is not just to ingest data into the system but to do it seamlessly. As my project uses React Native and Go, it was only natural that I created a React Native SDK and Go-Gin middleware, which need to be initialized with an API key configured in the Traceblade project settings.
The Interface
For the MVP, I opted for separate views (events and logs), as this allows me to build a solid foundation before tackling the complexities of a unified experience.
Natural Language Querying
One of my favorite features is natural language querying, which will make it easy to search and filter data using simple, human-readable queries. This feature, demonstrated briefly at the 14-second mark of the video, this can go beyond raw data visualization and provide actionable insights.
The Next Steps
So far, I have captured the basics, but there is still so much more to do. My focus for the next milestones includes:
Unified Timeline View: Combining user events and logs into a single interface to understand the ripple effects of user interactions across services.
Advanced Filtering: Allowing users to fine-tune searches across events and logs.
User Paths/Journeys: Analyzing and presenting user journeys within an app.
Scalability: Ensuring Traceblade can handle larger datasets efficiently.
