FME Splitter

A data splitting helper for FME users, enabling partitioning by attribute to streamline GIS workflows and ETL pipelines.

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About This Tool

The FME Splitter partitions a single geospatial dataset into multiple outputs by applying a defined rule to a chosen field. It targets GIS analysts, data engineers, and ETL specialists who need scalable data partitioning for parallel processing or staged delivery. Conceptually, the tool reads the input dataset, selects a split_field, applies the chosen method (attribute value, hash, or range), and writes one partition per target dataset while preserving schema and CRS. Core features include support for attribute value grouping, hash-based distribution for load balancing, and numeric range banding, plus optional range bounds, metadata preservation, and streaming support for large datasets. Unique differentiators include deterministic hash partitions and flexible range-based splitting for analytical workflows. Typical use cases include per-country data routing, distributed processing in GIS pipelines, and creating manageable subdatasets for QA or dissemination.

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How to Use

Provide inputs: dataset_path, split_field, method, and partitions where required.
Configure range_bounds or hash-related settings as needed.
Run calculation to generate partitioned outputs.
Validate partition datasets and metadata.
Export or integrate partitions into downstream GIS workflows.

Frequently Asked Questions

Find Quick Answers

What inputs are required?

You must provide the input dataset path, the split_field name, the split method (attribute, hash, or range), and the number of partitions for methods that need it. Optional bounds or mappings can refine the split.

What formats are outputs saved in?

Partitioned outputs support common GIS formats such as Shapefile, GeoJSON, and File Geodatabase, preserving the original schema, CRS, and attribute definitions.

Can I split by multiple fields at once?

Not in a single operation. You can run separate partitions by different split_fields or methods, producing distinct partition sets for subsequent joins or analyses.

How scalable is it for large datasets?

The tool supports streaming or chunked processing to handle large datasets efficiently, minimizing memory usage and enabling distributed workflows where applicable.

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