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Alchemist is an JavaScript ETL(Extract, Transform, Load) engine focused on managing data coming from multiple sources, in an efficient and streamlined way, by using pipelines. Alchemist

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If your data-related needs oscillate around any of the following:

  1. loading data from multiple sources, reacting to triggers,
  2. clean-up and transformation to desired format,
  3. formatting data between stages and engines in a custom data processing workflow,

then Alchemist will be the right tool for you!

The concept of Alchemist

Alchemist was designed with 2 goals in mind:

  1. Be lightweight: Alchemist can be installed and used in any JavaScript project.
  2. Be flexible: Alchemist comes with built-in adapters, while being extensible with custom adapters as needed.

While the typical flow of data in an ETL engine is:

  • E(xtract): read data from source,
  • T(ransform): process the data,
  • L(load): load the data into the next processing stage or engine,

Alchemist offers more flexibility, by storing both valid (parseable) and invalid (malformed) data between steps in a pipeline. This distinction allows for deeper and more detailed processing of valid data, while keeping seemingly invalid data safely stored for further analysis and improvements.

The full data processing pipeline is demonstrated in the following diagram:


Additional resources

A more in depth look about how we use alchemist

Installation TODO

Use npm to add the dependency in your package.json file:

$ npm install alchemist

Or install it globally with:

$ npm install --global alchemist


Each pipeline is an AWS Lambda function, e.g.:

async call() {
  // Step 1: Define input from Kinesis:
  let input = Input.instanceFor(this.adapterRegistry, 'KinesisInput', { events: })

  // Step 2: Define transformations sequence:
  let transformations = [
    Transformation.instanceFor(this.adapterRegistry, 'FirstEventTransformation'),
    Transformation.instanceFor(this.adapterRegistry, 'SecondEventTransformation'),
    Transformation.instanceFor(this.adapterRegistry, 'ThirdEventTransformation')

  // Step 3: Define output to Kinesis:
  let output = Output.instanceFor(this.adapterRegistry, 'KinesisOutput', {stream_name: 'output-kinesis-stream'})

  // Step 4: Use console for invalid output
  // Note: ConsoleOutput is a preregistered Adapter!
  let invalidOutput = Output.instanceFor(this.adapterRegistry, 'ConsoleOutput', { })

  // Step 5: Execute the pipeline:
  let pipeline = new Pipeline({
    input:            input,
    transformations:  transformations,
    output:           output,
    invalidOutput:    invalidOutput


Review a complete example to learn more about the complete pipeline structure.


After checking out the repo, run yarn install to install dependencies. Then, run docker-compose run --rm test to run the tests.

To install this library onto your local machine, run npm install. To release a new version, run npm version <update_type> to update the version number in package.json, and then run npm publish, which will push the library to


The npm package is available as open source under the terms of the MIT License.

Code of Conduct

Everyone interacting in the Alchemist project’s codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.

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