Data Platform - RFI-10.2024.docx

Program Executive Office Command Control Communications-Tactical (PEO C3T) Project Manager Mission Command (PM MC)

Request for Information (RFI) for Data Platform Capability Purpose:

This Request for Information (RFI) is issued by Project Manager Mission Command to gather industry feedback on the current state of Data Platform capabilities that can operate in Cloud, On-premises, Bare metal or Hybrid environments, including Disconnected, Disrupted, Intermittent, and low bandwidth (D-DIL) scenarios.

The Government requests interested sources provide information in the form of a white paper. The whitepaper should not exceed 10 pages, not including the cover. The whitepaper must demonstrate recent experience (within the last three (3) years) and capabilities in providing the requirements described in this RFI.

Intent:

This RFI is for information purposes only and does not constitute an invitation for Bid, Request for Task Execution Plan, Request for Quotation, or Request for Proposal. There is no obligation on the part of the Government to acquire any products or services described in this RFI or its responses. Responses will be treated only as information and market research for the Government to consider. The Government will not pay for information submitted in response to this RFI. Interested parties are responsible for marking proprietary, restricted, or competition-sensitive information contained in their response.

Requirement Overview:

Project Manager Mission Command is seeking a Data Platform capability that includes standard functionalities typically expected of a Data Platform. The solution should be deployable via microservices and offer an easy-to-use automated deployment process.

The Data Platform should provide the following capabilities:

· Data Integration and Management: Seamlessly connect and manage data from various sources.

· Data Governance: Implement robust governance frameworks to ensure data quality, compliance, and security.

· Data Cataloging: Maintain an organized inventory of data assets with metadata tagging for easy discovery.

· Data Lineage and Provenance: Track data origin, movement, and transformations.

· Data Security: Ensure data protection and privacy through role-based access controls (RBAC) and encryption.

· Analytics and AI/ML Integration: Support data analysis, visualization, and integration with AI/ML models.

· Microservices within a non-proprietary Container Ecosystem: Utilize a local registry with digitally signed container images.

· Automated Installation: Employ appropriate open-source automation tooling.

Key Features:

1. Infrastructure Agnostic: Operate efficiently across Cloud, Bare metal, On-premises, or Hybrid environments, including D-DIL scenarios while providing seamless functionality on various platforms and compatibility with existing infrastructure.

2. Microservices Architecture: Utilize a microservice-based design for flexibility and scalability.

3. Automated Deployment: Offer automated deployment and configuration management tools.

4. User-Friendly Interface: Provide an intuitive interface for ease of use by non-technical users.

5. Interoperability: Ensure compatibility with other open source microservices, existing systems and support for standard data formats and protocols.

6. Integration with Data Platform Variants: Capable of integrating with different variants of data platform solutions, allowing for interoperability and seamless data exchange across multiple systems.

Detailed Requirements:

1. Data Integration and Management: The Data Platform solution must provide robust capabilities for data integration and management, ensuring seamless connectivity, interoperability, and efficient handling of data across multiple sources and environments. The solution should encompass the following functionalities:

Data Connectivity:

· Support for a wide range of data sources, including relational databases, object databases, data lakes, data warehouses, and various file formats (e.g., CSV, JSON, XML).

· Capable of connecting to on-premises, Bare metal, cloud-based, and hybrid data environments.

· Integration with popular data integration tools and platforms (e.g., Apache NiFi, Talend, Informatica).

Data Ingestion:

· Automated data ingestion from various sources with support for batch, real-time, and streaming data ingestion methods.

· Ability to handle high-volume and high-velocity data ingestion scenarios while maintaining operational functionality in D-DIL environments.

· Support for event-driven data ingestion and processing.

Data Transformation:

· Advanced data transformation capabilities, including data cleansing, enrichment, normalization, and aggregation.

· Support for ETL (Extract, Transform, Load), ELT (Extract, Load, Transform), and Reverse ETL processes.

· Capability to handle complex data transformations using scripting languages (e.g., Python, R) and graphical user interfaces.

Data Orchestration:

· Workflow automation for data integration tasks, enabling the design, scheduling, and execution of data pipelines.

· Support for dependency management and error handling within data workflows.

· Integration with non-proprietary container orchestration tools such as Apache Airflow, Kubernetes, and Docker.

Data Virtualization:

· Ability to create virtual data layers that provide a unified view of data from disparate sources without physical data movement.

· Support for data federation to enable querying across multiple data sources.

· Real-time data access and on-demand data provisioning.

Data Quality Management:

· Tools for monitoring, profiling, and ensuring the quality of data.

· Automated detection and resolution of data quality issues such as duplicates, inconsistencies, and missing values.

· Generation of data quality reports and metrics to ensure data integrity and reliability.

Metadata Management:

· Comprehensive metadata management capabilities, including metadata capture, storage, and utilization.

· Support for management of automated metadata generation and enrichment.

· Capability to create and manage military glossaries, data dictionaries, and data catalogs.

Data Lineage:

· Detailed tracking of data lineage to monitor data flow from source to destination.

· Visualization tools to represent data lineage graphically, aiding in impact analysis and regulatory compliance.

· Ability to trace and audit data transformations and data usage.

Data Security and Compliance:

· Robust security features to protect data at rest and in transit, including encryption and access controls.

· Compliance with Military standards and regulations.

· Support for RBAC and data masking to ensure data privacy.

Scalability and Performance:

· High-performance data integration and management capabilities to handle large-scale data environments.

· Elastic scalability to accommodate varying workloads and data volumes.

· Optimization techniques to ensure efficient data processing and minimal latency.

Monitoring and Management:

· Comprehensive monitoring tools to track data integration processes, performance metrics, and system health.

· Alerting and notification mechanisms for proactive issue resolution.

· Centralized management console for overseeing data integration activities and resource utilization.

2. Data Governance: Effective data governance ensures that data is accurate, consistent, secure, and used responsibly. The Data Platform solution must include:

Governance Frameworks:

· Establish and enforce policies, procedures, and standards for data management.

· Define roles and responsibilities for data stewardship and governance.

Data Quality Assurance:

· Implement processes for data validation, auditing, and quality control.

· Provide tools for data profiling, cleansing, and standardization.

Compliance and Regulatory Adherence:

· Ensure compliance with Military regulations and standards.

· Maintain audit trails and records for regulatory reporting and transparency.

Data Usage Policies:

· Develop and enforce data usage policies to protect sensitive information.

· Establish a separate RBAC policy for data access.

Data Stewardship:

· Designate data stewards to oversee data quality and governance initiatives.

· Provide tools for data stewards to monitor and manage data assets.

3. Data Cataloging: A well-maintained data catalog enables efficient data discovery and management. The solution should include:

Metadata Tagging:

· Automatically generate and assign metadata to data assets.

· Enable user-defined tagging for custom metadata attributes.

Data Discovery:

· Maintain a centralized inventory of data assets across the organization within a distributed environment.

· Provide a semantic search capability of metadata tags.

Military Glossary:

· Develop a Military glossary to define key terms and data elements.

· Promote a common understanding of data across the organization.

Collaboration and Curation:

· Collect social curation features, such as comments, ratings, and endorsements.

· Support collaborative data curation and sharing among users.

4. Data Lineage and Provenance: Understanding the flow and transformation of data is critical for transparency and compliance. The Data Platform solution should provide:

Data Metrics:

· Provides real-time or historical information on data access.

· Provides usage metrics for data products.

Lineage Tracking:

· Automate the capture of detailed lineage information for data assets from source to destination.

· Track data transformations, including cleansing, aggregation, and enrichment.

Provenance Documentation:

· Document the history and origin of data assets.

· Provide audit trails to support data governance and regulatory compliance.

Impact Analysis:

· Analyze the impact of data changes on downstream processes and applications.

· Identify potential issues and dependencies in data workflows.

Visualization Tools:

· Offer graphical representations of data lineage for easy analysis and interpretation.

· Enable users to drill down into lineage details and explore data dependencies.

5. Data Security: Ensuring the protection and privacy of data is paramount. The Data Platform solution must include:

Access Controls:

· Implement RBAC to restrict data access based on user roles.

· Support fine-grained access permissions for sensitive data through data product attributes through attribute-based access control (ABAC).

Encryption:

· Encrypt data at rest and in transit to protect against unauthorized access.

· Support industry-standard encryption protocols and practices.

Data Masking:

· Apply data masking techniques to protect sensitive information in production environments.

· Support dynamic data masking for real-time data protection.

Audit and Monitoring:

· Implement logging and monitoring to detect and respond to security incidents.

· Provide audit trails for data access and modifications to support compliance and investigations.

Compliance Support:

· Ensure the solution adheres to relevant regulations and standards (e.g., NIST 800-53 Rev 5, AR 25-2 (Army Cybersecurity)/ Risk Management Framework (RMF) standards).

· Provide tools and features to facilitate compliance reporting and auditing.

6. Analytics and AI/ML Integration: Supporting data analysis, visualization, and integration with AI/ML models is crucial for deriving actionable insights. The Data Platform solution should include:

Data Analysis and Visualization:

· Provide tools for data exploration, analysis, and visualization.

· Support integration with popular data analysis and visualization tools (e.g., Tableau, Power BI, Jupyter Notebook).

AI/ML Model Integration:

· Enable the development, deployment, and management of AI/ML models within the data Platform.

· Support integration with AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).

Model Training and Fine-Tuning:

· Provide capabilities for training and tuning AI/ML models using integrated data.

· Support automated machine learning (AutoML) for model development and optimization.

Model Deployment and Monitoring:

· Facilitate the deployment of AI/ML models (inference engines) into production environments.

· Provide tools for monitoring model performance, detecting drift, and managing model lifecycle.

Collaborative Data Science:

· Enable collaboration among data scientists, analysts, tactical and strategic users.

· Support version control, sharing, documentation of AI/ML models, and data pipelines.

Operational Insights:

· Leverage AI/ML models to generate real-time insights and predictive analytics.

· Integrate analytic capabilities into operational workflows to support decision-making.

7. Microservices within a non-proprietary container ecosystem: To ensure flexibility, scalability, and security, the Data Platform solution must utilize microservices within a non-proprietary container ecosystem. This should include:

Local Registry:

· Maintain a local container registry to store and manage container images.

· Ensure high availability and redundancy of the registry to prevent downtime.

Digitally Signed Container Images:

· Implement digital signatures for container images to verify authenticity and integrity.

· Support signing and verification processes that comply with industry standards (e.g., Notary, Cosign).

Container Ecosystem Integration:

· Seamlessly integrate with non-proprietary container ecosystem for container orchestration and management.

· Support automated scaling, self-healing, and rolling updates of microservices.

Security and Compliance:

· Enforce security policies for container images and runtime environments.

· Implement RBAC and network policies within a non-proprietary container ecosystem to secure microservices.

8. Automated Install: The Data Platform solution must provide automated installation capabilities using open-source automation tools. This should include:

Installation Playbooks:

· Implement comprehensive automated installation playbooks to facilitate the installation and configuration of the Data Platform solution.

· Ensure playbooks are modular, reusable, and customizable to fit different deployment environments.

Configuration Management:

· Use playbooks to manage configuration changes and updates across the infrastructure.

· Implement version control and rollback capabilities to handle configuration changes safely.

Deployment Automation:

· 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.

Monitoring and Reporting:

· Provide tools to monitor the status and progress of automated installations.

· Generate detailed reports on installation success, failures, and performance metrics.

Integration with CI/CD Pipelines:

· Integrate automated installation processes with CI/CD pipelines for continuous delivery and deployment.

· Ensure seamless integration with popular CI/CD tools (e.g., Jenkins, GitLab CI/CD, CircleCI).

9. Application API layer migration:

· Maintenance and Support: Please provide details on the maintenance and support plan for APIs and any service level agreements or support agreements that can be provided to ensure that APIs are maintained and supported in a timely and effective manner.

· Proxy or Extensible API, migration path for POR/WFA requirements: We request additional details regarding the strategy and support for onboarding existing Program of Record (POR) or WarFighter Application (WFA) data to your solution. Specifically, we are interested in understanding how the product facilitates this transition while avoiding the issues encountered in typical migrations. This includes any tools, frameworks, or methodologies that are provided to ensure an efficient and non-disruptive migration process.

· Migration Strategy: What is the recommended approach for migrating from the existing API set to the new one, while minimizing disruption and ensuring compatibility? Are there predefined pathways, documentation, or guidelines to assist in this process, with a focus on minimizing the impact on existing systems and applications?

· Compatibility and Integration: How does the product ensure compatibility between the old and new APIs so they are interoperable between systems, without introducing new issues or causing disruption? Are there any interim solutions or compatibility layers that can be employed during the transition period to maintain functionality and minimize the impact on ongoing operations?

· Support for Extensibility and Customization: Does the product offer extensibility features that can accommodate specific POR/WFA requirements during migration, while minimizing the impact on existing systems and applications? Can the APIs be customized or extended to align with unique program needs, without causing disruption or introducing new issues?

· Testing and Validation: What tools or support does the product provide for testing and validating the migration, while minimizing the impact on ongoing operations? Are there automated testing solutions or sandbox environments available to ensure that the migration is successful without disrupting ongoing operations or introducing new issues?

Enclosure A: Data Platform Requirements

· Provide detailed information on how your solution meets each requirement listed in the Data Platform Requirements Summary.

· Include a self-assessment checklist indicating which requirements the proposed solution can fulfill.

Name: Data Platform (Data Platform)

· Integrate Data Sources

· Virtualize Data Sources

· Govern Data

· Catalog Data

· Lineage Tracking

· Secure Data

· Analytics Integration

· Microservices within a non-proprietary container ecosystem

· Automated Install

Additional Requirements:

· Company Profile: Narrative describing the company’s core business; Company name, NAICS, CAGE code, and DUNS number; Company size and locations; Traditional/non-traditional defense contractor; Point of contact and email address

· Corporate Experience: Detail prior/current United States Department of Defense (DoD) and/or foreign defense department corporate experience performing efforts of similar size and scope within the last three years, including contract number, organization supported, indication of whether as a prime or subcontractor, contract value, government point of contact, and a brief description of the specific task areas as they relate to the services described herein.

· Capabilities: Describe experience and capabilities in managing DoD tasks of this nature and size, including system engineering, software development, software upgrades, testing, integration, training, and help desk support for any new software components.

· Security Requirements: Indicate the ability to meet security requirements, including Secret Security Clearance for personnel and Secret Facility Clearance and Secret Safeguarding.

· Cybersecurity: Describe experience with Risk Management Framework accreditations, Information Assurance Vulnerability Alert updates, and Security Technical Implementation Guides.

Submission of Responses:

Submission Deadline and Contact Information:

Responses shall be submitted electronically no later than noon (12 PM) EST on 15 November 2024. All questions and responses shall be submitted via email to virginia.a.pippen.civ@army.mil and shall not exceed 10 pages, using a 12-point Times New Roman font presented in a in a .docx or .pdf format. An additional, single, cover page may be submitted. If the vendor so chooses, the Vendor may complete the requirements checklist showing how their product meets the indicated requirements.

Additional Discussions:

The Government may choose to reach out to select respondents for additional discussions as a result of this RFI to obtain further information as part of the market research effort.

Questions:

Questions regarding this RFI shall be submitted in writing by email NLT 18 October 2024 to the contact listed above. As questions are submitted, they will be collected and all questions will be answered at the same time, posted on SAM.gov by close of business on 25 October 2024. The Government does not guarantee the ability to answer questions received after 18 October 2024.

Disclaimer:

This RFI is issued solely for informational and planning purposes. This RFI is not a solicitation. This RFI is not to be considered a commitment by the Government to issue a solicitation or award a contract. Responses to this RFI are not proposals and cannot be accepted by the Government to form a binding contract. Federal Acquisition Regulation (FAR) clause 52.215-3, “Request for Information or Solicitation for Planning Purposes”, is incorporated by reference. The Government does not intend to reimburse respondents for any costs associated with the submissions of their responses to this RFI; respondents to this RFI are solely responsible for all expenses associated with responding. Proprietary information and trade secrets, if any, must be clearly marked on all materials. All information received in response to this RFI that is marked “Proprietary” will be handled accordingly. Please be advised that all submissions become Government property and will not be returned. The Government will not be confirming receipt of submissions. In accordance with FAR 15.201(e), responses to this RFI are not offers and cannot be accepted by the Government to form a binding contract.

Responses from this RFI will be used to formatively shape broad Army guidance for acquiring software solutions. Response content may be aggregated and anonymously published into summary documentation to facilitate such guidance. Any publications resulting from this RFI will be non-attributional. RFI respondents consent to their submissions being used for such purposes.

Note: Ensure proprietary information and trade secrets are clearly marked on all materials. All submissions become Government property and will not be returned nor will receipt be confirmed.