The tConvertType component is specifically designed for in-line data type conversions within a job flow. It is schema-aware, requires no code, and is more maintainable than embedding logic in a query (which mixes extraction and transformation) or using a heavier component like tJavaRow for a standard task. A tMap could work but is less direct for a simple type cast.

Salesforce enforces strict API request limits. Reducing the ‘Commit size’ (rows per batch) decreases the number of API calls made for a given dataset, making the job less likely to hit the limit. This is a direct configuration within the component. tTryCatch might handle the failure but doesn’t prevent it. Upsert doesn’t necessarily reduce calls, and tBufferOutput manages memory, not API throttling.

Using a single tFilterRow with two expressions (e.g., `row1.Status.equals(“CANCELLED”)` and `row1.Amount <= 0`) on the 'Reject' link is the clearest and most maintainable approach. It keeps the logic together, visible on the design canvas, and is easier to debug than a single complex expression in the source or multiple chained filters. It properly separates the reading (tFileInput) from the business logic (tFilterRow).

For a small, static lookup table, a tMap with a ‘Load once’ cache is the standard and performant choice. It loads the entire lookup set into memory at the start of the job, making subsequent lookups extremely fast by avoiding repeated database queries for each row, which is the problem with tDBRow. tJoin is for combining full flows, not row-by-row lookups, and tUnite simply concatenates rows.

The tBufferOutput/tBufferInput pair acts as an in-memory queue. The producer (tBufferOutput) writes rows to this buffer, and the consumer (tBufferInput) reads from it. This decouples the execution, preventing a fast producer from overwhelming a slow consumer (or vice versa), which is crucial for managing memory and flow control in large data jobs.

A robust incremental load uses a control mechanism. The job reads new rows where a modification timestamp is greater than the last saved value (often stored in a file or context variable). After processing, it uses tSetGlobalVar (or writes to a file) to capture the new maximum timestamp for the next execution. This is efficient and stateful. Option A is flawed as it queries the target within the source query, creating a dependency. Option C is a full diff, which is inefficient.

A very common issue, especially with delimited files, is schema misalignment. If the schema defines Column A, B, C but the actual data file places data for B in the first position, the job will associate that data with Column A. Columns that don’t receive data from their expected position will show as ‘null’. Checking the ‘Print schema’ in tLogRow helps diagnose this by showing the expected column names next to the values.

To treat multiple file processes as one atomic transaction, you must manage the database connection explicitly. Using a dedicated connection component (like tOracleConnection) with ‘Auto commit’ disabled allows you to start a transaction. All subsequent tDBOutput components should use this shared connection. After all files are processed successfully, a tOracleCommit is used. If any error occurs, a tOracleRollback (triggered via an OnSubjobError link) will undo all inserts from all files.

This is a fundamental distinction. tJava is a ‘start’ or ‘end’ point component that runs its code a single time when its subjob is triggered (e.g., for initialization, logging, or cleanup). tJavaRow is a row-processing component that sits in the middle of a data flow; its code executes for every single row that passes through it, allowing manipulation of row data.

The tConvertType component is specifically designed for in-line data type conversions within a job flow. It is schema-aware, requires no code, and is more maintainable than embedding logic in a query (which mixes extraction and transformation) or using a heavier component like tJavaRow for a standard task. A tMap could work but is less direct for a simple type cast.0