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This is a quick start guide to get the Structured File Lens up and running in the quickest and simplest possible way so you can start ingesting and transforming data straight away. For a more in-depth set of instructions go to the User Guide.

In this guide we will be setting up and running the Lens as a docker image deployed to your local machine, however we support a number of cloud deployments technologies, including full support of AWS. Once deployed, you can utilise any of our ready-made sample input, mapping, and expected output files to test your Lens.

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The first step in configuring the Structured File Lens is to create a mapping file. The mapping file is what creates the links between your source data and your target model (ontology). This can be created using our online Data Lens Mapping Tool utilising an intuitive web-based UI. Log in hereto get started, and select the option for Structured File Lens. The Structured File Lens is capable of ingesting XML, CSV, and JSON files, creation of mapping files differ slightly between file types so ensure to select the correct options for your use case. Alternatively, if you wish you create your RML mapping files manually, there is a detailed step by step guide on creating one from scratch, along with a number of examples.

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• License - LICENSE

  • This is the license key required to operate the Lens, request your new unique license key here.

• Mapping Directory URL - MAPPINGS_DIR_URL

  • This is the directory where your mapping file(s) is located. As with all directories, this can be either local or on a remote S3 bucket. Mapping files for the Structured File Lens can be created using our Mapping Config Web App and can be pushed directly to a running Lens.

  • For this quick start, we will use a local directory.

• Output Directory URL - OUTPUT_DIR_URL

  • This is the directory where all generated RDF files are saved to. This also supports local and remote URLs.

  • For this quick start, we will use a local directory.

• Run Standalone Mode - LENS_RUN_STANDALONE

  • Each of the Lenses are designed to be run as part of a larger end-to-end system, with the end result of data being uploaded into Semantic Knowledge Graphs or Property Graphs. As part of this process, Apache Kafka message queues are used for communicating between services.

  • For this quick start, we are going to enable standalone mode by setting this value to true, so that the Lens won't attempt to connect to external services.

• Record Provenance - RECORD_PROVO or PROV_OUTPUT_DIR_URL

  • In the Lenses, time-series data is supported as standard, so every time a Lens ingests some data, we add provenance information. This means that you have a full record of data over time. Allowing you to see what the state if the data was at any moment. 

  • For this quick start, we can take two approaches. One would be to turn provenance off by setting RECORD_PROVO to false, or the other is to leave it on (true by default) and set the PROV_OUTPUT_DIR_URL to a directory for your provenance RDF files to be saved to. The latter is the approach we will take in the following examples.

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Once an input file has successfully been processed, the response returned from the Lens is in the form of a JSON, and within the JSON response is the outputFileLocations element. This element contains a list of all the URLs of generated RDF files. (Multiple files are only generated when ingestinglarge CSV files).

Sample output:

{
    "input": "file:///var/local/input/input-data.csv",
    "failedIterations": 0,
    "successfulIterations": 1,
    "outputFileLocations": [
        "/var/local/output/Structured-File-Lens-44682bd6-3fbc-429b-988d-40dda8892328.nq"
    ]
}

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