Imagine building a serverless architecture that seems perfect on paper, only to find out it’s bleeding your company dry with a $1 million monthly AWS Lambda bill. This is the story of a team that faced this exact problem and how they managed to cut their costs to almost zero by fixing the ‘small files’ problem in their Data Lake.
Their original architecture followed a popular playbook: streaming data into S3 and using Lambda to compact small files. But at their scale, this approach led to a storm of Lambda invocations, resulting in massive costs.
The problem wasn’t storage costs; it was the Lambda functions themselves. The architecture created a massive fan-out of Lambda invocations, leading to costly operations and archival overhead.
The team switched to a data platform that changed the core architecture. Instead of ingestion and compaction being two separate, asynchronous jobs, they became a single, transactional operation. This approach consolidated the write path, pruned data at multiple levels, and separated compute and storage.
The results were astounding: the $1 million Lambda bill disappeared, replaced by a predictable $3,000/month EC2 cost. The Total Cost of Ownership (TCO) for the pipeline dropped by over 95%. Engineers went from constant firefighting to focusing on building actual features, and query times for analysts dropped from minutes to seconds.
This story highlights the importance of choosing the right data architecture for high-throughput workloads. Sometimes, a DIY serverless approach might not be the most efficient solution.