Which transformer is most appropriate for conducting multiple joins on many streams of features from non-database formats?

• A. Merger
• B. FeatureJoiner
• C. InlineQuerier
• D. DatabaseJoiner

Answer: (C)

When you need to connect and combine data from many feature streams that aren’t in a database, you want a tool that can perform in-memory joins across several inputs in one step. Inline Querier does exactly that by letting you write an inline SQL-like query that references multiple input feature streams and produces joined features without going to a database. This is ideal for non-database formats because all the joining work happens in memory, directly inside FME, and it supports multi-way joins across many streams in a single operation.

Other options aren’t as well suited for this scenario. A Merger simply stacks or merges features from different inputs without performing attribute-based joins on a key. A FeatureJoiner can join two streams on a common attribute but becomes cumbersome to extend to many streams in one go. A DatabaseJoiner relies on a database backend to perform the join, which isn’t appropriate when working with non-database formats. Inline Querier’s ability to reference multiple inputs in one query makes it the best fit for multi-stream, in-memory joins.