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barnard59 is a toolkit to automate extract, transform and load (ETL) like tasks. Its main focus is on creating RDF & Linked Data, a standardized graph data format by the W3C. barnard59 has a clear focus on creating RDF but is not limited to it.

It is built on many years of experience in creating RDF out of non-RDF data sources. Our goals & design principles are inspired by the Unix philosophy:

  • Describe the tasks in an abstracted way. We use RDF to describe the so-called pipeline & steps, barnard59 is an engine to execute them.
  • Enable executing code where necessary. Sometimes plain code is faster & smarter than writing complex abstractions. You can execute in-line code in the pipeline in case you need expressivity.
  • Automate everything. barnard59 can be run within CI/CD toolkits like GitLab, run in a Docker container or simply within a node.js environment.

barnard59 can be used for many different tasks, including, but not limited to

  • Convert existing data to RDF. There is built-in support for CSV and JSON. Transformation is done in a streaming way when possible and thus can handle very large input data.
  • Shell & pipe support: Plug to existing command-line utilities you are comfortable with and work with their output.
  • Read from and write to SPARQL endpoints

barnard59 can be easily extended by providing additional functionality as plugin. We also work on user interfaces, so you do not have to write your pipelines in Turtle/RDF.

We provide a list of all operations supported by barnard59 in our primer in the wiki.


barnard59 runs pipelines which are described in RDF in the pipeline and code ontology.

A barnard59 pipeline consists of one or more steps where each step returns a Stream. The streams of all steps are combined via .pipe. The pipeline itself is also a stream. Depending on the stream types of the first and last stream, the pipeline can be writable or readable. If the pipeline is self-contained, a dummy readable interface is provided to handle events for end of processing and errors.


The code to process pipelines makes heavy usage of RDF Loaders to create JavaScript objects and functions based on a RDF description. For the steps, the Code Ontology is used to point or define the code for each step. The RDF Code Loader takes care of importing code referenced as IRIs or directly process literals as JavaScript code.

Pipeline Definition

The following prefixes are used in the code segments of this section:

@prefix code: <> .
@prefix p: <> .
@prefix rdf: <> .


The pipeline itself must have a rdf:type of p:Pipeline. Based on the interfaces of the first and the last step, the pipeline itself is a Writable and/or Readable stream. Additional to the p:Pipeline rdf:type, p:Writable and/or p:Readable must be defined accordingly. That can look like this:

<#pipeline> a p:Pipeline, p:Writable, p:Readable .

Note: The type of the streams could be also detected during runtime or based on metadata for the streams, but that would required deeper understanding of the pipeline and steps structures. To simplify handling of pipeline definitions without runtime data, the stream types are attached to the pipeline definition and errors are thrown in the runtime if they don't match.


A pipeline consists of one or more steps, which are actually factories which return streams. Each step is linked with the previous one via .pipe. As this requires to process the steps in order, the steps must be provided as an RDF List. This example shows a very simple pipeline which consists of two steps:

<#pipeline> a p:Pipeline ;
  p:steps [
    p:stepList ( <#readFile> <#writeFile> )
  ] .

Note: With the two levels (p:steps and p:stepList) it's possible to use a known identifier (Blank Node with name or Named Node) for a step list and reference to it in multiple pipelines. This is useful if pipelines share the same steps, but have different variables.

The step itself must have a rdf:type of p:Step. The Code Ontology is used to point to the actual code. This is done with the code:implementedBy property and code description with the rdf:type of code:EcmaScript. The code:link points to the JavaScript code file. node: IRIs can be used to use the Node.js resolve logic of require to find files and packages. file: IRIs will be expanded to full paths. This example uses the Node.js file system function to create a readable stream. input.txt is used as the only parameter.

<#readFile> a p:Step ;
  code:implementedBy [ a code:EcmaScript ;
    code:link <node:fs#createReadStream>
  ] ;
  code:arguments ( "input.txt" ) .

It's also possible to give the arguments as key value pairs like this:

<#parseCsvw> a p:Step ;
  code:implementedBy [ a code:EcmaScript ;
    code:link <file:customStreamCode.js>
  ] ;
  code:arguments [
    p:name "url";
    p:value ""
  ], [
    p:name "method";
    p:value "POST"
  ] .

customStreamCode.js should export a function which returns a stream. During the initialization of the pipeline the function is called with the arguments built based on code:arguments. The built argument for this example would look like this:

  url: '',
  method: 'POST'


Pipelines can have variables directly attached to the pipeline or injected from the runtime, like the cli tool. The variables can be evaluated via a special RDF Datatype or in ES6 Literals.

Definition attached to the Pipeline

Variables attached to the pipeline are useful when steps, step lists or sub pipelines are reused. The definition looks like this:

<#pipeline> a p:Pipeline ;
  p:variables [
    p:variable [ a p:Variable ;
      p:name "filename" ;
      p:value "example.txt"
  ] .

Definition via Command Line Arguments

Not all variables used in the pipeline need to be part of the pipeline definition. These abstract pipelines can use variables provided by the runtime, like the cli tool. In the cli tool, this can be done via the --variable argument. See the usage for more details. The variable from the previous example could be defined like this in the cli:

barnard59 run pipeline.json --pipeline --variable filename=example.txt

In a CI/CD environment you might want to pass sensitive arguments like usernames and passwords via environment-variables. This can be done by referencing it like this:

barnard59 run pipeline.json --pipeline --variable password=$PASSWORD

where $PASSWORDis a valid environment-variable.

Using Variables via Datatype

The values of the variable can be use via a literal with a p:VariableName datatype. In the example below, the argument will be replaced with the value of the variable filename:

<#readFile> a p:Step;
  code:implementedBy [ a code:EcmaScript ;
    code:link <node:fs#createReadStream>    
  ] ;
  code:arguments ("filename"^^p:VariableName) .

Using Variables in Template Literals

Variables can be also used in Template Literals. The variable values are injected with their corresponding name, like shown in the following example:

<#readFile> a p:Step ;
  code:implementedBy [ a code:EcmaScript ;
    code:link <node:fs#createReadStream>
  ] ;
  code:arguments ( "${basePath}/input.txt"^^code:EcmaScriptTemplateLiteral ) .

Using Variables in ECMAScript Code

Variables are also injected into the context provided to ECMAScript code. The context can be accessed in the standard JavaScript way using this. The variables are attached in a Set as this.variables. In the following example you can see how the variable prefix can be accessed in the JavaScript code:

<#map> a p:Step ;
  code:implementedBy [ a code:EcmaScript ;
    code:link <node:barnard59-base#map>
  ] ;
  code:arguments ("""row => {
    return this.variables.get('prefix') + row
  }"""^^code:EcmaScript) .

JavaScript Arguments

Part of the pipeline concept are JavaScript code arguments where describing a logic in a declarative way would be very complex, but is easy to code in one or few lines of JavaScript code. Also for this usage the Code Ontology is used. The RDF Code Loader package provides this functionality. See the docs of the package for more details.


Here is a small example for a step which is using the barnard59-base#map operation:

<#map> a p:Step ;
  code:implementedBy [ a code:EcmaScript ;
    code:link <node:barnard59-base#map>
  ] ;
  code:arguments ("""row => {
    // replace all ö umlauts with oe
    return row.split('ö').join('oe')
  }"""^^code:EcmaScript) .

EcmaScript Template Literal

Also Template Literals are supported. The defined variables can be used directly. A typical use case are file name patterns, derivative from a variable value, like in the following example:

<#readFile> a p:Step ;
  code:implementedBy [ a code:EcmaScript ;
    code:link <node:fs#createReadStream>
  ] ;
  code:arguments ( "${basePath}/input.txt"^^code:EcmaScriptTemplateLiteral ) .



A very simple pipeline which fetches a JSON document from the URL defined as variable in the pipeline. In a map step the JSON is transformed into a valid JSON-LD structure using the context defined in a variable. The JSON-LD parser generates RDFJS quads out of the JSON structure. As last step the quads are serialized into N-Triples.

The same pipeline definition is provided as JSON-LD and Turtle for a comparison.

The JSON-LD version of the pipeline can be run with the following command:

node bin/barnard59.js run examples/fetch-json-to-ntriples.json --pipeline

The --pipeline parameter is required, cause the definition contains two pipelines.

The Turtle version of the pipeline requires the --format parameter to parse the definition:

node bin/barnard59.js run examples/fetch-json-to-ntriples.ttl --format text/turtle --pipeline

The command line tool pipes the pipeline stream to stdout by default. With the --output parameter, the output can be also written to a file:

node bin/barnard59.js run examples/fetch-json-to-ntriples.json --pipeline --output test.nt


A simple pipeline to parse a CSV file using CSV on the Web. This example shows how a Pipeline can be used as an argument for a step.

node bin/barnard59.js run examples/parse-csvw.ttl --format=text/turtle --pipeline=


barnard59 is a toolkit to automate extract, transform and load (ETL) like tasks for creating RDF




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