Data_Platform_RFI_Checklist_10.2024.xlsx
XLSX spreadsheet 22 KB Posted
- Attached to
- Data Platform Capability
- Solicitation number
- PMMCTMCDPC2024
- Issued by
-
Department of the Army
About this file
This document is a Request for Information (RFI) issued by the Project Manager Mission Command to gather industry feedback on Data Platform capabilities that can operate in various environments, including Disconnected, Disrupted, Intermittent, and low bandwidth (D-DIL) scenarios.
The RFI outlines detailed requirements for data integration and management, data governance, data cataloging, data lineage and provenance, data security, analytics and AI/ML integration, and microservices within a non-proprietary container ecosystem. The government is requesting interested sources provide a whitepaper demonstrating their recent experience and capabilities in meeting these requirements. The whitepaper should not exceed 10 pages, excluding the cover. The resulting feedback will inform a future solicitation for a Data Platform capability by the Department of the Army.
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Other files
| File | Type | Posted |
|---|---|---|
| DP - RFI Q-Answers-FINAL.xlsx | XLSX spreadsheet | |
| Data Platform - RFI-10.2024.docx | DOCX document |
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Text version
Sheet1
| Requirement | Compliance | Comments |
| Data Integration and Management | ||
| Support for a wide range of data sources (relational and object databases) | ||
| Capability to connect to on-premises, cloud-based, and hybrid environments | ||
| Integration with popular data integration tools and platforms | ||
| Automated data ingestion from various sources (batch, real-time, streaming) | ||
| High-volume and high-velocity data ingestion support over disconnected, denied, intermittent, or limited bandwidth (D-DIL) environments | ||
| Event-driven data ingestion and processing | ||
| Advanced data transformation capabilities (cleansing, enrichment, normalization, aggregation) | ||
| Support for ETL, ELT, and Reverse ETL processes | ||
| Complex data transformations using scripting languages (Python, R) and graphical interfaces | ||
| Workflow automation for data integration tasks | ||
| Dependency management and error handling within data workflows | ||
| Integration with orchestration tools (Apache Airflow, Kubernetes, Docker) | ||
| Creation of virtual data layers (data virtualization) | ||
| Support for data federation that includes distributed queries | ||
| Real-time data access and on-demand provisioning | ||
| Tools for monitoring and ensuring data quality | ||
| Automated detection and resolution of data quality issues such as duplicates, inconsistencies, and missing values | ||
| Data quality reports and metrics generation | ||
| Comprehensive metadata management capabilities | ||
| Automated metadata generation and enrichment | ||
| Management of business glossaries, data dictionaries, and data catalogs | ||
| Detailed tracking of data lineage | ||
| Graphical representation of data lineage | ||
| Ability to trace and audit data transformations and data usage | ||
| Robust security features (encryption, access controls) | ||
| Compliance with Military standards and regulations | ||
| Support for role-based access control (RBAC) and data masking | ||
| High-performance data integration and management capabilities | ||
| Elastic scalability to accommodate varying workloads and data volumes | ||
| Optimization techniques for efficient data processing | ||
| Comprehensive monitoring tools to track data integration processes, performance metrics, and system health | ||
| Alerting and notification mechanisms | ||
| Centralized management console |
| Data Governance |
| Establish and enforce policies, procedures, and standards |
| Support roles and responsibilities for data stewardship and governance |
| Data validation, auditing, and quality control processes |
| Tools for data profiling, cleansing, and standardization |
| Ensure compliance with Military regulations and standards |
| Audit trails and records for transparency |
| Develop and enforce data usage policies to protect sensitive information |
| Access controls and permissions |
| Designated data stewards for governance |
| Provide tools for data stewards to monitor and manage data assets |
| Data Cataloging |
| Automatic metadata tagging |
| Enable user-defined tagging for custom metadata attributes |
| Centralized data asset inventory within a distributed environment |
| Provide a semantic search of metadata tags |
| Military glossary development |
| Collect social curation features, such as comments, ratings, and endorsements |
| Collaboration and curation features |
| Data Lineage and Provenance |
| Automate the capture of detailed lineage information for data assets from source to destination |
| Track data transformations, including cleansing, aggregation, and enrichment |
| Document the history and origin of data assets |
| Provide audit trails to support data governance and regulatory compliance |
| Analyze the impact of data changes on downstream processes and applications |
| Identify potential issues and dependencies in data workflows |
| Visual representations of data lineage for impact analysis and compliance |
| Enable users to drill down into lineage details and explore data dependencies |
| Data Security |
| Role-based access control (RBAC) |
| Fine-grained access permissions through attribute-based access control (ABAC) |
| Data encryption at rest |
| Support industry-standard encryption protocols and practices |
| Apply data masking techniques to protect sensitive information in production environments |
| Support dynamic data masking for real-time data protection |
| Logging and monitoring for security incidents |
| Audit trails for data access and modifications |
| Ensure the solution adheres to relevant regulations and standards |
| Provide tools and features to facilitate compliance reporting and auditing |
| Analytics and AI/ML Integration |
| Data analysis and visualization tools |
| Integration with popular AI/ML analysis and visualization tools |
| AI/ML model development, deployment, and management |
| Support integration with AI/ML frameworks and libraries |
| Automated machine learning (AutoML) support |
| Facilitate the deployment of AI/ML models (inference engines) into production environments |
| Provide tools for monitoring model performance, detecting drift, and managing model lifecycle |
| Enable collaboration among data scientists, analysts, and tactical and strategic users |
| Support version control, sharing, and documentation of AI/ML models and data pipelines |
| Leverage AI/ML models to generate real-time insights and predictive analytics |
| Integrate analytics capabilities into operational workflows to support decision-making |
| Microservices within a non-proprietary Container Ecosystem |
| Maintain a local container registry to store and manage container images. |
| Ensure high availability and redundancy of the registry to prevent downtime |
| Implement digital signatures for container images to verify authenticity and integrity |
| Support signing and verification processes that comply with industry standards |
| Seamlessly integrate with non-proprietary container ecosystem for container orchestration and management. |
| Support automated scaling, self-healing, and rolling updates of microservices |
| Enforce security policies for container images and runtime environments |
| Implement role-based access control (RBAC) and network policies within a non-proprietary container ecosystem to secure microservices |
| Automated Install |
| Implement comprehensive automated installation playbooks to facilitate the installation and configuration of the Data Fabric solution. |
| Ensure playbooks are modular, reusable, and customizable to fit different deployment environments |
| Use playbooks to manage configuration changes and updates across the infrastructure |
| Implement version control and rollback capabilities to handle configuration changes safely |
| Automate the deployment process to reduce manual intervention and minimize errors |
| Ensure automated deployments are idempotent, ensuring consistent results regardless of the number of executions |
| Provide tools to monitor the status and progress of automated installations |
| Generate detailed reports on installation success, failures, and performance metrics |
| Integrate automated installation processes with CI/CD pipelines for continuous delivery and deployment |
| Ensure seamless integration with popular CI/CD tools |
File details come from the government source that posted it. Updated .