2.0.1.203

2022-04-27

• 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 the ability to directly query a database

• Added a Kafka Consumer as a new way of triggering the lens

• Added the latest Oracle DB drivers

• Added to the suite of built in functions

• Added fine grain logging control

1.5.4.178

2021-06-10

• 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

1.5.2.172

2021-04-28

• The SQL Lens now fully supports Denodo and the 'com.denodo.vdp.jdbc.Driver' JDBC driver

1.5.1.168

2020-10-02

• Returns an exception message when processing fails via endpoint triggering

1.4.3.157

2020-08-28

• AWS Marketplace compatibility

• The Lens can now accept new Custom Functions via an endpoint

1.3.2.142

2020-05-15

• SQLLENS-55: Update to the latest custom RML Mapper

• SQLLENS-69: Fix Docker image security vulnerabilities as seen in ECR

1.2.4.105

2020/03/17

• SQLLENS-47: It is now possible to choose which provenance activities are generated for your transformed data.

• SQLLENS-51: Dead Letter Queue messages are now much richer, and include additional information in JSON format, to more easily facilitate consumption by an external process.

• SQLLENS-52: Update to our latest custom RMLMapper which prevents erroneous IRI encoding.

1.1.1.87

2020/03/03

• SQLLENS-26: For situations where a message bus is not expected to be used, it is now possible to run the SQL Lens in a kafka-less mode.

• SQLLENS-46: Added support for the following new mapping functions:

  • retrieveValueFromDelimitedListTests

  • extractCharactersFromStartTest

  • extractCharactersFromEndTest

1.0.0.63

2020/02/03

This is the first official release of the SQL Lens.

It is a highly configurable pipeline component, which will transform data stored in an SQL Database into a Knowledge Graph compatible output.

• Highly configurable transformation from data held in an SQL Database, to RDF.

• Supplied as a Docker Container, available from DockerHub.

• Supports the following SQL Databases:

  • MySQL

  • Microsoft SQL Server

  • Oracle

  • Postgre SQL

  • IBM DB2

  • MariaDB

  • SAP Hana

  • IBM Informix

  • Firebird

  • Hyper SQL

  • H2

  • Supports the following output file formats:

    • NQuads

  • Capable of iteratively transforming data from any size of SQL Database result-set..

  • May be triggered via a schedule, or explicitly via a RESTful endpoint.

  • Output files may be written to any on-premise, or cloud location.

  • Provenance as standard.