Search for a command to run...
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
No comments yet. Be the first to comment.
In this series, I will share my journey of building Traceblade, a product bridging user events and logs to provide insights for product and tech teams. From idea to launch, follow along as I document the process of turning Traceblade into reality.
Your app feels slow. You did all the optimizations in your code imaginable. You curbed the re-renders, lazy loaded the components, memoized where necessary. And yet, people say: “Hey, it is slow.” They aren't necessarily saying your UI is janky. What...
Streaming vs. Polling JSON: Choosing the Right Approach for Rubric Generation When designing the rubric generation system, I faced a key architectural decision: Should I stream JSON responses as they are generated, or should I poll the backend for u...
Decoding the Dynamics: Embracing Flexibility, Navigating Challenges, and Mastering the Art of Configurable Code in Software Development
Introduction Asynchronous JavaScript is a cornerstone of modern web development, enabling efficient and responsive applications. This approach allows web pages to process lengthy operations, such as API calls or data fetching, without stalling the us...
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.
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.