Hi. I’ve noticed that when I use enrichment APIs for large datasets, the performance drops, data gets lost, or processes fail. Does anyone know how to manage scaling problems like this?
Hi. I encountered the same problem and recently read a helpful article on Styloceleb called Why Most Enrichment APIs Fail at Scale - and How Generect Solves It. It explains that many tools, including a standard company enrichment api, perform well with small datasets but run into serious issues at scale. Common problems are rate limits, slow response times, data decay, and messy input that breaks the workflow. Generect addresses these challenges by combining multiple data sources, using asynchronous processing for bulk operations, supporting real-time lookups, and automatically cleaning and refreshing records. It also provides confidence scores for each result, which helps maintain accuracy. For me, it offered a clear framework to understand why most APIs fail when scaling.
Hi. I encountered the same problem and recently read a helpful article on Styloceleb called Why Most Enrichment APIs Fail at Scale - and How Generect Solves It. It explains that many tools, including a standard company enrichment api, perform well with small datasets but run into serious issues at scale. Common problems are rate limits, slow response times, data decay, and messy input that breaks the workflow. Generect addresses these challenges by combining multiple data sources, using asynchronous processing for bulk operations, supporting real-time lookups, and automatically cleaning and refreshing records. It also provides confidence scores for each result, which helps maintain accuracy. For me, it offered a clear framework to understand why most APIs fail when scaling.