The Problem We Were Actually Solving
We wanted to give these creators a way to reach their customers, but we also had to deal with the harsh realities of operating in restricted countries. Internet censoring, data sovereignty, and limited access to global APIs made it nearly impossible to use the platforms everyone else takes for granted. In our case, it meant navigating through blocks, firewalls, and custom proxy servers just to get data in and out.
What We Tried First (And Why It Failed)
Our initial approach was to use a cloud-based proxy service to mask our IP addresses and access the e-commerce platforms. Sounds simple, right? But what we didn't consider was the proxy service's limitations on concurrent connections, bandwidth, and caching. It led to pipeline latency averaging 30 seconds, and we were lucky to reach a success rate of 70% for each transaction. The costs were staggering: our monthly bill was 3x more than what we thought it would be.
The Architecture Decision
After a year of struggling with the proxy service, we decided to shift our focus towards building a more robust data infrastructure. We chose to use a mix of batch and streaming architecture to handle data ingestion and processing. We built a cloud-native data warehouse using AWS Glue and Athena, which not only reduced our costs but also gave us more control over data freshness and quality control. We introduced a custom-built data validation pipeline to catch errors early in the ingestion process, reducing failed transactions from 30% to 5%. Our pipeline latency shrunk to under 100ms, and we achieved a 99.9% success rate for transactions.
What The Numbers Said After
Our system now processes over 10,000 transactions daily, with an average latency of 50ms. The query cost on our data warehouse has dropped by 75% since moving to batch and streaming architecture. Freshness SLAs have improved significantly, with our data always staying up-to-date within a 5-minute window.
What I Would Do Differently
If I were to do it again, I would focus more on building a hybrid data infrastructure that combines the strengths of batch and real-time processing from the start. I would also invest more time in data quality at the ingestion boundary, using tools like Apache Airflow and Prefect to automate data validation and ensure data consistency across systems. With more emphasis on proactive data engineering and infrastructure design, I believe we could have achieved our goals faster and more efficiently.
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