Modernize your data development lifecycle with biGENIUS-X and improve productivity with automation.
biGENIUS-X addresses the challenges of traditional data development processes with features that help data teams adopt modern practices to create a more efficient and reliable data development lifecycle, overcoming bottlenecks and improving overall productivity.
Data development processes traditionally follow a linear, waterfall-like approach that typically involves gathering requirements, designing the data model, developing the ETL processes, testing, and finally deploying the solution. However, this approach can make it difficult to adapt to changing business needs, because the overall output of the data team is directly dependent on the productivity of the team members with the necessary expertise, any efficiency losses at this critical stage will have a corresponding impact on the entire process.
The discrepancy between expectations and execution is one of the main obstacles holding data teams back from delivering data solutions timely and accurately, making the shift towards more agile and iterative data development methodologies that emphasize collaboration, automation, and continuous improvement necessary.
The low-code automated data transformation solution that modernizes your data development processes.
biGENIUS-X modeling wizards enable data teams to create and manage data models using an intuitive, visual interface. This low-code approach reduces the reliance on specialized expertise, allowing a wider range of team members to contribute to the data development process. The wizards guide users through the modeling process step-by-step, ensuring consistency and adherence to best practices. This not only accelerates the development cycle but also minimizes the risk of errors and inconsistencies that can arise from manual coding.
With biGENIUS-X, data team members can safely experiment with new features and changes in dedicated branches without affecting the main data pipeline. This not only provides a clean, isolated workspace for individual tasks but also enables parallel development efforts. By working in separate branches, teams can break free from the constraints of a linear data development process, allowing for faster iteration and more flexibility.
biGENIUS-X enables data teams to version control their data models, transformations, and business logic with Git. This allows for better collaboration, as multiple team members can safely work on the same project simultaneously. Version control also provides a clear audit trail of changes made to the data pipeline, making it easier to identify and resolve issues.
biGENIUS-X comes equipped with best-practice generator templates to automatically transform business models into technical implementations, significantly reducing the need for manual testing. These templates encapsulate proven design patterns and coding practices, ensuring that the generated code is reliable, efficient, and adheres to industry standards.
When users customize their generator templates, biGENIUS-X provides an intuitive template editor with IntelliSense capabilities: intelligent code completion, syntax highlighting, and context-aware suggestions, making the coding process more efficient and less error-prone. By catching issues early in the development process, biGENIUS-X reduces the likelihood of bugs propagating downstream, leading to a more stable and reliable data solution.
The generated code artifacts for the chosen target platform are stored in your dedicated biGENIUS-X Git repository as the basis for the CI/CD pipeline. These artifacts include the code of your data solutions and load control scripts, ensuring that your data team can quickly and easily deploy changes and improvements to your analytical data solution.
Once signed in, you can either use the projects provided by biGENIUS or start with your own.
Start turning your business logic into models and experience the speed of biGENIUS-X automation.
Generate and deploy the code artifacts for your data product to your chosen target platform.
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