Support for ingesting Change Data Capture (CDC) Debezium data streams
Support for XSLX and ODS spreadsheet data formats
Support for multiple source and mapping file processing
Added support for the AWS Default Credentials Provider Chain. In addition to the environment variables, AWS Credentials can now be set in more ways.
Improved stability and bug fixes
Added support for Property Graphs by generating nodes and edges files
Lens data directories are created on startup minimising required config
Ability to save and restore configuration as well as editing on a running Lens
Added to the suite of built in functions
Added fine grain logging control
Added support for different output file types (NQuads, NTriples, JSON-LD, Turtle, Trig, and Trix)
Removed NamedGraphs being attached to triples when provenance is switched off
SFLENS-104: Enhancements have been made to CSV transformation in order to more elegantly handle CSV files containing errors.
SFLENS-107: It is now possible to choose which provenance activities are generated for your transformed data.
SFLENS-112: Dead Letter Queue messages are now much richer, and include additional information in JSON format, to more easily facilitate consumption by an external process.
SFLENS-113: Update the POM to use our latest custom RMLMapper which prevents erroneous IRI encoding
This is the first official release of the Structured File Lens.
It is a highly configurable pipeline component, which will transform structured content into a Knowledge Graph compatible output.
Highly configurable transformation from structured content, to RDF.
Supplied as a Docker Container, available from DockerHub.
Supports the following input file formats:
Supports the following output file formats:
Capable of iteratively transforming input files of any file size.
May be triggered via Message bus, or directly via RESTful endpoint.
Input / Output files may be accessed from on-premise, or cloud locations.
Provenance as standard.