Adopting Feature Driven Development (FDD) for Crafting Dynamic Data as a Service (DaaS) — Part 2
Table of Contents
Steps in Feature Driven Development for Data as a Service
Building upon the foundation laid in part 1 of this series, we will now take a deeper dive into the specifics of each of these steps, customizing them to the unique context of data development within the framework of Feature Driven Development (FDD).
Step 1: Domain Object Modeling for Data
- In the context of data development, domain object modeling involves creating a visual representation of the data domain.
- This includes defining data entities, their attributes, and relationships, providing a clear understanding of the data landscape.
- It’s essential for data professionals to collaborate with domain experts to accurately model the data, ensuring it aligns with business objectives.
Step 2: Feature Identification and Prioritization (Data Sets, Transformations, Analytics)
- Feature identification in data development entails recognizing specific data-related functionalities, such as data ingestion, transformation, analytics, or reporting.
- Prioritization involves assessing the business value of each data feature. For instance, high-priority features might include real-time data processing or critical data transformations.
- Prioritizing data features ensures that the most valuable data functionalities are developed first, meeting immediate business needs.
Step 3: Short Iterations in Data Development
- Short iterations in data development involve breaking down data-related work into manageable units, typically lasting a few weeks.
- Each iteration could focus on developing specific data pipelines, analytics modules, or reporting dashboards.
- Short iterations allow for rapid progress and flexibility in accommodating changes in data requirements.
Step 4: Design and Build of Data Features
- Data feature development follows the design and build principles of FDD. Cross-functional teams collaborate to design and implement data features.
- Design encompasses data schema design, data transformation logic, or data visualization layouts.
- Building data features involves coding, testing, and ensuring that data is processed accurately and efficiently.
Step 5: Role-Based Collaboration for Data Excellence
- FDD’s role-based collaboration extends to data development. Key roles may include Data Architect, Data Engineer, Data Scientist, and Business Analyst.
- Each role has specific responsibilities, such as data modeling, ETL (Extract, Transform, Load) development, data analysis, and defining business requirements for data.
- Collaborative teamwork ensures that data features are developed with expertise and efficiency.
Step 6: Continuous Integration and Data Quality Assurance
- Continuous integration in data development entails regularly merging developed data features into the data pipeline or data repository.
- Automated data quality assurance processes are crucial to identify data inconsistencies, anomalies, or errors.
- Continuous integration and data quality checks maintain data accuracy and reliability throughout development.
Step 7: Tracking Data Usage and Performance Metrics
- In data development, tracking data usage involves monitoring how data features are utilized by end-users or downstream applications.
- Performance metrics assess the efficiency of data processing, analytics performance, and reporting speed.
- By tracking data usage and performance, teams can refine data features to better meet user needs and optimize data pipelines for efficiency.
Open-Source and Free Feature Driven Development Tools for Data as a Service
Domain Modeling Tools
Modelio: Modelio is an open source extensible modeling environment supporting: UML, BPMN, ArchiMate, SysML, …
Feature Management Tools
Trello: Trello offers a free plan that can be used for feature tracking and management.
Version Control Systems
Git: Git is open-source and freely available, making it a popular choice for version control.
IDEs for Data Development
Jupyter Notebook: Jupyter is an open-source tool for data exploration and analysis.
Data Integration and ETL Tools
Apache Nifi: Apache Nifi is an open-source data integration tool.
Database Management Systems:
PostgreSQL: PostgreSQL is a powerful open-source relational database management system.
Continuous Integration/Continuous Deployment (CI/CD) Tools
Jenkins: Jenkins is open-source and free to use for automation of integration and deployment tasks.
Collaboration and Communication Tools
Slack: Slack offers a free tier for team collaboration and communication.
Data Quality and Testing Tools
Great Expectations (GX): Great Expectations is an open-source library for data validation.
Monitoring and Metrics Tools
Prometheus and Grafana: Both Prometheus and Grafana are open-source tools for monitoring and visualizing data.
Reporting and Visualization Tools
Metabase: Metabase (On-prem) is an open-source tool for creating interactive data visualizations and reports.
Docusaurus: Docusaurus is an open-source documentation tool that can be used for project documentation.
Many cloud providers offer free tiers with limited usage, allowing you to use cloud services like AWS or Azure at no cost within certain limits.