What You Will Learn

This guide will explain how a datastore can be added from a file.

For these chapters it is recommended to have the configuration setting: Do a data flow analysis before starting engine? enabled.

1. Prerequisites

  • Have about 10 minutes

  • Have an installed version of the PAK Workflow Executor

  • Have an installed version of the PAK Editor

  • Know how to open a Workflow

  • Have a local workflow

2. What is a datastore file?

There is no datastore only file. However, whenever a workflow is created by the PAK Editor a .modelmeta file is created, containing meta information about the specific workflow (including the datastore).

Going forward, the .modelmeta file will be referenced as just modelmeta!
A modelmeta might have an empty datastore.

2.1. How do I create a datastore file?

Whenever you create and save a workflow in the PAK Editor you also automatically create a modelmeta. Within this file there is a section about every datastore key you created.

To add values to the datastore you can simply do it in this tab:

Figure 1. Datastore tab in the Editor.
1 To enter the datastore tab you can click on the Datastore tab.
2 You can add key value pairs manually by clicking on the + or you can load a .json file that contains a map (Example below).
3 A list of all keys added.
4 A list of all values added.
Example of a .json file suitable for adding a datastore in the Editor:
  "key1": "value1",
  "key2": "value2",
  "keyX": "valueX"

2.2. Where can I find the saved modelmeta?

The modelmeta will be saved under the same path (with the same name). The files are only differentiated based on their endings: .bpmn for a workflow and .modelmeta for meta information files.

3. How do I add a datastore file?

Now as you have your workflow and modelmeta file, you can simply open the workflow.

Figure 2. Opened workflow control panel.
1 Here you see the name and the path of the opened workflow.
2 Here you see the path of the by default opened modelmeta. It opens a modelmeta saved under the workflows directory and name by default.
3 If you have the configuration setting: Do a data flow analysis before starting engine? enabled, you will see the fields prefilled with values provided by the modelmeta datastore. To see optional fields you also have to enable the setting Enable editing of optional values after workflow is opened?.
4 You can clear all fields by pressing the Clear (initial) data button.
5 You can open another modelmeta via a file chooser by pressing the Load Datastore (Optional) button. Alternatively you can also drag and drop a modelmeta into the executor window once a workflow is opened or drop a workflow together with a desired modelmeta into the executor.
You can open any modelmeta file even if they were not created together with the opened workflow. The executor then tries to fill in any fields that the datastore of the modelmeta provides.

Once a modelmeta is loaded, you should see the values of the file loaded into their matching field.

Any changes done to the values in the executor will not be saved to the modelmeta!

Sonatype Nexus

PAK features connectors and commands for Sonatype Nexus. This means the software can directly interact with Nexus repositories for storing and managing artifacts. Through these connectors, PAK can automate tasks like uploading binaries or retrieving dependencies, ensuring efficient artifact management within Nexus.


PAK has connectors and commands for Jenkins. This allows the software to directly communicate with Jenkins servers, enabling the automation of CI/CD (Continuous Integration/Continuous Deployment) tasks. Through these connectors, PAK can trigger builds, fetch build statuses, or manage job configurations, streamlining the CI/CD processes within Jenkins.

Git Hub

PAK possesses connectors and commands for GitHub. This means the software can interface directly with GitHub repositories, facilitating actions like code pushes, pull requests, or issue tracking. Through these connectors, PAK can automate various GitHub operations, enhancing code collaboration and repository management.

Atlassian Confluence

PAK is equipped with connectors and commands for Atlassian Confluence. This enables the software to directly interact with Confluence spaces and pages. Through these connectors, PAK can automate actions such as creating, updating, or retrieving documentation, ensuring efficient content management and collaboration within Confluence.


PAK features connectors and commands for Codebeamer. This allows the software to seamlessly integrate with Codebeamer’s ALM (Application Lifecycle Management) platform. Through these connectors, PAK can automate tasks like issue tracking, test management, or requirements tracing, enhancing the coordination and management of software development processes.

JFrog Artifactory

PAK has connectors and commands for JFrog Artifactory. This means the software can directly interface with Artifactory repositories, enabling actions like artifact storage, retrieval, and management. Through these connectors, PAK can automate tasks such as deploying artifacts or managing repository configurations, streamlining the integration and management of binary artifacts within Artifactory.

Amazon Web Services (AWS)

PAK has connectors and commands for Amazon Web Services (AWS). This means the software possesses specialized interfaces to directly interact with AWS services and execute actions on the AWS platform. Through these connectors, PAK can automate AWS-specific commands, such as launching EC2 instances, managing S3 buckets, or configuring Lambda functions. This allows for efficient integration, management, and automation of AWS resources and services directly from PAK.

Atlassian Jira

PAK features integration tools and capabilities for Atlassian Jira. These tools allow for a direct connection to Jira and the execution of specific actions. Using these integration tools, PAK can automate Jira actions such as adding comments or changing ticket priorities, ensuring seamless handling and coordination of Jira processes.


PAK has connectors and commands for Git. This means it has interfaces to directly communicate with Git and execute actions. Through these connectors, the software can automate Git commands such as retrieving changes or creating branches, enabling efficient integration and management of Git tasks.

Generic Human Tasks

PAK offers you a standard set of commands which require creative input from the user. Enables you to start with automating your workflows, that still need abit of human input.

Generic Commands

PAK offers a standard set of commands giving you the first steps to automate your workflows.

Nexus Maven Command Pool

Nexus is an artifact repository manager for storing binaries, libraries, and artifacts, supporting formats like Maven. Maven, a software project management tool, is based on the Project Object Model (POM) and allows developers to consistently define projects and dependencies. Our Command Pool offers commands for interactions between Maven and Nexus, such as artifact uploads or dependency retrieval.

Artifactory Maven Command Pool

Artifactory allows developers to store, retrieve, and manage binary files and artifacts, providing a
central source for all binaries used in a development process. Apache Maven is a software project
management and comprehension tool that enables developers to consistently describe a project and
its dependencies. Our Command Pool offers a collection of commands used to facilitate interactions
between Maven and Artifactory, such as uploading artifacts or retrieving dependencies.

Open API Command Interpreter

The OpenApi Command Interpreter allows you the automatic parsing of commands from an OpenApi defintion. No additional code needs to be written anymore, just add the address to the definition and our framework does the rest!

Kotlin Command Interpreter

The Kotlin Command Interpreter allows you the parsing and execution of commands within a Kotlin environment to automate various tasks or processes.

Bpmn Interpreter

Workflows come in many shapes and forms. The BPMN (Business Process Model and Notation) Interpreter enables the parsing of worklows defined in the BPMN format into the PAK intern model.

Human Task Interpreter

The Human Task Interpreter allows you the parsing and running of commands within a HTML and Javascript environment. Use this to build commands which need the creative input of a workflow user!

Java Command Interpreter

The Java Command Interpreter allows you the parsing and execution of commands within a Java
environment to automate various tasks or processes.


The heart of the PAK-Framework. Contains the means to run workflows with the PAK engine, but also the possibility to enrich the frameworks interfaces with your own implementations and solutions.

RocksDB Persistence

Data that is generated by a workflow run needs to be saved for short or longer terms. Our solution to the Persistence Interface of the PAK-Framework is to use the high-performance, key-value based RocksDB developed by Facebook.

PAK online

PAK Online is a web based application and provides an Open API based REST API. It enables you to upload workflows and run them periodically or on REST demand.

Command Line App

Run tasks and workflows on the console or as part of a CI/CD Pipeline with our Command Line Interface.

Workflow Editor

With our specially developed editor, a wide variety of workflows can be easily modeled in the wide known BPMN process format.

Workflow Executor

The Workflow Executor is the application to run your workflows. It features a multilingual UI and easy managment of your favorite workflows.


We offer a community website where you can exchange ideas and support each other. For our Pro packages we also offer full support via email.