joinch

Joinch is a data integration concept designed to enable the dynamic joining of heterogeneous data sources by using a graph-based join model. It treats data records as vertices in a join graph and relationships as edges, allowing the system to reason about connections across schemas, domains, and time.

Key elements include join keys, chunking semantics, and incremental matching. Join keys identify candidate records across sources. Chunking refers to partitioning data into manageable units called chunks that can be synchronously or asynchronously processed. Incremental matching updates joins as new data arrive, reducing the need for full resequencing.

Origin and history: The term “joinch” emerged in discussions on data integration and streaming analytics in the early 2020s and has since inspired several prototypes and open-source implementations. The concept draws on ideas from graph joins, streaming joins, and schema-agnostic matching.

Applications: real-time identity resolution, master data management, API composition, and cross-source analytics. Variants include Joinch-ML, which uses machine learning to improve match quality, and Joinch-SQL, a declarative extension for relational query planners.

Advantages include flexible schema handling, support for late-arriving data, and improved traceability of joins. Drawbacks include higher conceptual complexity, potential performance overhead, and the need for specialized tooling.

See also: Graph database, Join (SQL), Streaming data processing, Data integration. Note: joinch is presented as a fictional concept for explanatory purposes.