Use Case Diagram Template. Provide a comprehensive view of your case with this case document template that outlines your background and conditions in a step-by-step manner. Use Case Document Template.
Software Use Case Template Code And NoIdeally, the framework should also be flexible in customizing and extending it to easily adapt to suit enterprise-specific requirements.An actor is a person or other entity external to the software system being specified who interacts with the system and performs use cases to accomplish.A number of low code and no-code solutions exist that allow for visually creating the data pipelines across a variety of sources and sinks. As a result, there is a need for frameworks that build new pipelines, adding additional data sources or data sinks with minimal time and development effort. An actor can be a customer, user, person, system, or organization.A pipeline created for a particular use case may not be reusable for a different one and will require additional development effort to change. Actors are anybody interacting with your system.API and/or SDK support should be available to programmatically create and access the pipelines.Cross Cloud Support: Support for data sources, sinks, and services across different cloud services. The components should be easy to implement for common use cases.Flexible: The framework should be able to integrate with different services/systems across clouds or from on-premises.Extensible: Allow extending existing components to customize as per specific requirements or add new custom components to implement new functionalities.Code First: Provide a programmable way of defining and managing pipelines. Each component of the framework can be used, managed, and enhanced independently.Out-of-the-Box Functionality: Support integration with common data sources and sinks, and perform transformations out of the box. A data platform framework that allows its users to perform the different operations in a consistent way, irrespective of the underlying technology, will greatly reduce time and effort.What do you look for in a low code framework? Here are some suggested requirements.Modular: The framework should be modular in design. Requirements for the FrameworkCreating and maintaining pipelines to move data in and out of the platform is a major consideration.Minor version upgrades and patches are automatically updated and support is provided for major version updates.GUI-based Definition: An intuitive GUI for creating and maintaining the data pipelines will be useful. Provisioning the infrastructure capacity, managing, configuring, and scaling the environment should be managed automatically. The framework should automatically scale the underlying compute in response to changing workloads.Managed Service: The framework should be deployable on a fully managed cloud service.Technology Choice: We recommend a cloud-first approach when it comes to technology. BlueprintWhile designing the framework, it is important to consider the following points: A High-level Overview of the FrameworkThe data platform framework provides the base foundation upon which you can build specific accelerators or tools for data integration and data quality/validation use cases. Hp laptop ctrl key stuckPipeline Template: A DAG template that supports pipeline orchestration for different scenarios. Pipeline Configuration: A custom DSL-based configuration definition allows for reusability of pipeline logic and provides a simple interface for defining the required steps for execution.Here are the building blocks for such a framework: Component Library: Common data processing functionalities should be made available as components that can be used independently or in addition to other components. Go with a managed workflow service that provides a programmable framework, with out-of-box operators for integration, and also allows for adding custom operators as required. Orchestration: Scheduling and executing data pipelines requires a scalable and extensible orchestration solution. Data Processing: Data processing should be based on massively parallel processing solutions that can easily scale as per the requirement in order to support large volumes. Component Library: A suite of functionality code for supporting different processing use cases. Common job flow patterns can be supported through built-in templates. Job Template: A job execution template that supports processing the data using the component library as per user requirements. Software Use Case Template Generator Code HelpsOur DPA consists of a suite of micro-accelerators built on top of a platform framework based on cloud PaaS technologies.We regularly work with our clients to help them with their data journeys. Factory and Generators: Factory and Generator code helps in abstracting the implementation differences across different technologies.At GlobalLogic, we are working on a similar approach as part of the Data Platform Accelerator (DPA). Components: The base processing implementations that perform read/write on various data sources, apply transformations, run data validations, and execute utility tasks.
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