
Nabil Zaman
Nabil developed a novel set data structure that efficiently tracks large quantities of (mostly) sequential values. Instead of storing the set entries individually, they are grouped into closed intervals with new insertions either falling into an existing interval or creating a new one.
By tagging messages passing through our large scale data streams with sequential IDs, we’re able to leverage that data structure to monitor or improve the integrity of our data. We identify data loss in real-time by counting the gaps in the sequence, and eliminate duplication errors altogether.