Data Product Development Life-cycle

The Data Product Development Life Cycle is a step-by-step process for creating and managing a Data Product to provide value. To know more about each phase, please explore below sections.

Design

The Design phase of the Data Product life-cycle is pivotal in aligning business objectives with actionable solutions. It begins with a comprehensive understanding of business goals and use cases, forming the basis for developing a robust solution architecture. DataOS resources required to design a Data Product are Instance Secret, Depot, Cluster, and Stacks. DataOS also provides interfaces that help to design the Data Product such as Data Product Hub where you can explore existing Data Products, Workbench to query your data source without data movement, and Metis to get insight about the meta data. To know more in detail, please refer to How to Design a Data Product.

Build

The Build phase involves coding, configurations, and integrations to build data pipelines, application logic, and interfaces according to the solution architecture of the Data Product. DataOS Resources required to build the Data Product are Instance Secret, Secret, Depot, Cluster, Workflow, Worker, Service, Stacks, Policy, SODA Checks, Monitor, Pager, and Bundle. Rigorous testing during the build phase of the Data Product ensures functionality, performance, and reliability, with ongoing stakeholder collaboration to validate that the built product aligns with business objectives and technical specifications. To know more in detail, please refer to How to Build a Data Product.

Deploy

The Deploy phase of the Data Product life-cycle is crucial for transitioning the developed Data Product from a testing into the DataOS enviroment. This phase focuses on making the Data Product accessible and usable for the intended personas. Effective deployment ensures that the Data Product is operational, meets performance standards, and is ready for use by end-users. For more information, please refer to How to Deploy a Data Product.

Iterate

The Iterate phase is focused on ongoing improvement and optimization of the Data Product. This phase is driven by continuous feedback from users, performance analytics, and changing business requirements. Activities in this phase include collecting and analyzing user feedback, monitoring performance metrics, and identifying potential areas for enhancement. The goal is to refine and evolve the Data Product based on real-world usage and feedback. This often involves making iterative updates, testing new features or improvements, and re-deploying updated versions. For more information, please refer to How to Iterate the Data Product.