Tamr uses a hybrid approach to manage data variety at scale, combining machine learning and expert input to track and unify data from multiple sources.

Data lakes are a cost-effective and scalable solution for storing and accessing big data, with Tamr providing late-binding curation.

ETL and ELT are traditional approaches to data integration, but a probabilistic, machine learning-based approach like Tamr offers more flexibility and efficiency.

Tamr's API enables organizations to continuously connect and enrich data, improving data management and decision-making.

The debate between RDBMS and NoSQL systems for data flexibility can be resolved by using late-binding techniques in an RDBMS.

The array discusses the importance of data curation and different approaches to handle Big Data Variety.

Record matching in Big Data requires probabilistic or fuzzy matching using string similarity functions to handle data inconsistencies and errors.

Data integration products evolved from ETL to data curation systems, and now to scalable data curation systems with expert sourcing.