DC Code
DC Code is a key complement to the DataChain® platform, enabling the integration and development of AI algorithms directly within the solution.
It enhances DataChain®’s functionality with a secure, scalable, and multi-language notebook development environment.
Gartner® Magic Quadrant™
Open
DC Code provides a guarantee of openness and peace of mind thanks to its flexibility and adaptability to all types of needs.
Security
Aligned with DataChain®’s overarching security framework, DC Code ensures that rights and permissions are consistently applied across all users and groups within the module.
Data Science
DC Code enables data scientists and developers to leverage their range of expertise within the DataChain® platform. Supporting multi-environment and multi-language capabilities, it integrates seamlessly with MLflow and Git repositories.
How does it work?
DC Code enables algorithm development directly within the DataChain® platform. Complementing the platform’s NoCode/LowCode approach, it integrates seamlessly with all other modules.
Using data tables from the central database, DC Code allows users to modify and reintegrate data via a simplified API. Within a secure and integrated JupyterHub environment, users can:
• Develop algorithms in Python, R, Scala, and more.
• Connect to Git repositories for version control.
• Manage and customise virtual environments.
DC Code’s services enable seamless communication and integration with the platform’s other modules.
Open to data science
DC Code enhances the platform’s functionality by enabling secure interaction with data tables through its API connection to the DataChain® core module. Algorithms developed in Python, R, or Scala environments can be executed seamlessly, with automation capabilities provided by the DC Maestro module.
Customised environments
DC Code allows users to configure their environments and interpreters to meet specific project requirements. User-associated resources can be easily adapted, providing flexibility and supporting a gradual transition to scale.
Data science: an indispensable link
Data scientists are a crucial link in the data value chain,
occupying a strategic position at the intersection of multiple domains.
DataChain® features for data scientists
Multiple Python environments.
Efficient collaboration with business stakeholders.
Consistent rights and permissions policy.
Model generation capabilities.
Scalable infrastructure for growth.
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DC Code
Generation of actionable insights.
Development of AI models.
Drives innovation and creation of added value.
Expertise
DC Code integrates the expertise of data scientists directly into the DataChain® platform’s value chain. DC Code enables the development of advanced, highly specialised services, including deep learning (DL), machine learning (ML), and large language models (LLMs).
Innovation
DC Code enables the development of new services by leveraging data-driven insights. It empowers data scientists to apply advanced analytical techniques, driving innovation within the platform.
Collaboration and Synergy
DC Code supports the design and development of predictive models and other algorithms. DC Code improves collaboration with other stakeholders across the data value chain.
How does DC Code integrate with the platform?
DC Code offers full, native integration with the DataChain® platform, ensuring consistent application of rights and permissions within the development environment.
Can DC Code environments be customised?
Yes, DC Code supports customisation of environments and interpreters to accommodate the varying needs of Python or R projects. It simplifies dependency management and allows the use of private repositories for Python and R packages.
How can I automate the execution of my notebooks?
Notebook execution can be automated through the DC Maestro module, integrating it as a workflow task. This ensures comprehensive management of processing chain automation.
