🏸Zingg + TigerGraph combo for deduplication and big data graph analytics
Graph databases with built-in relationship patterns are great for record disambiguation and entity resolution. For example, TigerGraph is a powerful graph analytics system. And if you supplement it with the open ML tool Zingg (https://github.com/zinggAI/zingg), you can find duplicate and ambiguous records even faster.
Imagine, the same person in different systems is written differently. Therefore, it is very difficult to analyze its user behavior, for example, to generate a personal marketing offer or inclusion in loyalty programs. Zingg have built-in locking mechanisms that only calculate pairwise similarity for selected records. This reduces computation time and helps scale to large datasets. You don't have to worry about manually linking/grouping records: the internal entity resolution framework takes care of that. So with Zingg and TigerGraph you can combine the best simple and scalable entity resolution and further graph analysis.
https://towardsdatascience.com/entity-resolution-with-tigergraph-add-zingg-to-the-mix-95009471ca02