data-processor

Filter, map, sort, and aggregate arrays of objects using batch transformations that execute code directly rather than natural language descriptions, reducing tokens and processing time while handling large datasets with consistent business logic.

Yes. Apply filtering, aggregation (sum, average, min, max, count), and de-duplication across multiple data sources in a single batch workload, returning both transformed data and a statistics object detailing operation counts and duplicates removed.

Use array transformations to filter records by field values, sort results in ascending or descending order, and aggregate metrics like revenue totals—all in one operation with detailed statistics on transformations applied.

Input validation ensures data arrives as an array of objects meeting expected structure before filtering, mapping, or aggregation operations execute, preventing errors and maintaining data consistency across transformations.

Yes. Merge arrays from multiple data sources, then apply filtering, grouping, de-duplication, and aggregation in batch workloads to consolidate and summarize data consistently across sources.