DAS Evaluation Criteria Appendix A.xlsx

XLSX spreadsheet 38 KB Posted Apr 13, 2026

Attached to: ATR IT MODERNIZATION: DATA ANALYTICS SOLUTION Federal contract opportunity
Solicitation number: TMF5-1bRFI
Issued by: Department of Justice Offices Boards and Divisions

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Written by GovTribe AI · Apr 13, 2026

This is an Evaluation Criteria Appendix that provides instructions and definitions for vendors responding to a Data Analytics Solution (DAS) RFI issued by the Department of Justice, Antitrust Division. The appendix establishes a standardized response framework requiring vendors to assess their solutions against 93 requirements across Business, Functional, and Non-Functional categories, using a three-level support scale (Fully Supported, Partially Supported, Not Supported) and four capability levels (Out-of-the-Box, Configurable, Custom Development Required, Not Available). Vendors must complete a response template for each requirement and provide detailed comments addressing dependencies, assumptions, and constraints.

The evaluation criteria span nine primary requirement categories: Data Integration and Interoperability, Data Ingestion/Processing and Analytics, User Experience/Collaboration and Training, Security and Governance, Performance Tracking and Reporting, Performance and Scalability, Cost Management and Optimization, Risk Management and Future Planning, and AI Capabilities. Key performance thresholds include support for batch ingestion of at least 500 GB/hour, streaming throughput of 50,000+ records per second, processing of datasets ≥500 GB with ≤2-minute latency for near real-time workloads, and data ingestion of ≥1 TB within 90 minutes. Requirements also address FISMA compliance, role-based access control, multi-factor authentication, integration with enterprise identity providers (Entra ID, Active Directory, Okta), data cataloging and lineage tracking, automated ETL workflows supporting Python, R, and Shell scripting, AI/ML model lifecycle management, and cost optimization and monitoring capabilities. The document includes threaded comments from government stakeholders clarifying requirement intent and confirming vendor capabilities, particularly regarding Databricks implementation for specific functionalities.

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ATR Data Analytics Solution RFI QA.docx	DOCX document	Apr 24, 2026
Data Analytics Solution (DAS) RFI.docx	DOCX document	Apr 13, 2026

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Instructions Response to Business, Functional, and Non-Functional Requirements (Appendix A).

Key Definitions

Business Requirement: the business goal and outcome that needs to be achieved.

Functional Requirement: the specific behavior or action the system must perform.

Non-Functional Requirement: the quality, performance, or constraint the system must operate within.

1. Vendors must respond to all requirements listed in Appendix A using the provided response template.

2. For each requirement, indicate the level of support in the “Vendor Response” column using one of the following: Fully Supported/Partially Supported/Not Supported

Fully Supported - The requirement is completely met by the proposed solution as described, without the need for custom development or significant workaround. Any necessary configuration is standard and does not materially alter core functionality.

Partially Supported - The requirement is only partially met. Some aspects are available, but gaps remain that may require configuration, integration, workaround processes, or limited custom development. Vendor must clearly identify what is and is not supported.

Not Supported - The requirement is not met by the proposed solution in its current form. No native capability exists, and significant custom development or third-party solutions would be required to address the requirement.

3. In the “Capability Level” column, indicate whether the capability is available Out-of-the-Box, Configurable, requires Custom development or Not Available.

Out-of-the-Box (OOTB) - The capability is delivered natively within the base product and is available for use upon deployment with minimal or no configuration.

Configurable - The capability is available within the product but requires standard configuration (e.g., settings, workflows, rules) to enable or tailor it. No coding or custom development is required.

Custom Development Required - The capability is not available natively and must be built through custom code, scripting, or significant modification of the platform (including use of APIs or development frameworks).

Not Available - The capability does not exist within the product and cannot be delivered without external tools or solutions not included in the vendor’s offering.

4. In the “Comments” column, vendors are encouraged to include any additional relevant information to clarify their response, such as dependencies (e.g., integrations, modules, or third-party components), assumptions or constraints, and relevant product features or capabilities.

Requirement Definitions

Req No.	Req Type	Req Category	Requirement Definition	Vendor Response	Capability Level	Comments
BR 1.01	Business	I. Data Integration and Interoperability	The solution shall provide native interoperability with enterprise productivity and analytics tools, including existing visualization functionality, relational SQL databases, and cloud-based storage services, to enable seamless data sharing across the Agency.
BR 1.02	Business	I. Data Integration and Interoperability	The solution shall integrate with the Agency’s existing Enterprise cloud infrastructure ( e.g. DataLake Storage, Active Directory (AD), and enterprise security services), but be flexible to work with other cloud platforms (e.g. AWS, Google Cloud, etc.).
BR 1.03	Business	II. Data Ingestion, Processing and Analytics	The solution shall implement automated ETL workflows and advanced data engineering to handle diverse datasets with minimal manual intervention. By automating the extraction, transformation, and loading phases, the system shall optimize the end-to-end analysis process.
BR 1.04	Business	II. Data Ingestion, Processing and Analytics	The solution shall natively support languages (e.g. Python, R, and Shell scripting) to enable advanced data processing and predictive analytics.
BR 1.05	Business	II. Data Ingestion, Processing and Analytics	The solution shall provide centralized data management and cataloging capabilities to enable consistent organization, accessibility, and governance of enterprise data assets. This capability shall support the establishment and operation of an enterprise data and analytics center of excellence.
BR 1.06	Business	III. User Experience, Collaboration and Continuous Training	The solution shall offer a user-friendly interface to enhance platform usability, enabling users to efficiently perform data analytics processes and collaborate effectively on projects.
BR 1.07	Business	III. User Experience, Collaboration and Continuous Training	The solution shall enhance collaboration by providing collaborative development features, enabling multiple users to work together efficiently on data analytics projects, and supporting better management of data assets and derived products.
BR 1.08	Business	III. User Experience, Collaboration and Continuous Training	The solution shall support continuous learning and skill development through the enterprise center of excellence by providing policy guidance, technical support, training, and online resources to drive enterprise-wide adoption and proficiency in data analytics capabilities.
BR 1.09	Business	III. User Experience, Collaboration and Continuous Training	The solution shall support scalable analytics capacity on demand to meet growing data volumes and user demand, without being constrained by infrastructure limitations.
BR 1.10	Business	III. User Experience, Collaboration and Continuous Training	The solution shall improve accessibility of data and analytics tools for non-technical users enabling efficient access to data for analysis and decision-making.
BR 1.11	Business	IV. Security and Governance	The solution must support stringent security requirements by implementing robust data security and governance policies, facilitating vulnerability identification, ensuring environmental compliance, and developing an extensible Role-Based Access Control (RBAC) structure for data, code, and other resources.
BR 1.12	Business	IV. Security and Governance	The solution must support cloud-enforced data retention and disposal policies by preserving data for defined retention periods and securely disposing of data in accordance with governance and compliance requirements.

tc={C94EB05C-DBB7-4F5D-A912-6113603FB6DD}: [Threaded comment]

Your version of Excel allows you to read this threaded comment; however, any edits to it will get removed if the file is opened in a newer version of Excel. Learn more: https://go.microsoft.com/fwlink/?linkid=870924

Comment:

is this a current databricks capability?

Reply:

@Fordyce, Diana (ATR) - Databricks can be configured, but currently we're using Azure for this capability. We updated the requirement that "solution must support..."

BR 1.13	Business	IV. Security and Governance	The solution must support secure user access through FISMA-compliant enterprise authentication and access control mechanisms.
BR 1.14	Business	IV. Security and Governance	The solution must enhance data governance and knowledge sharing by centralizing data assets.
BR 1.15	Business	V. Performance Tracking and Reporting	The solution shall provide robust data visualization capabilities, allowing users to create insightful visualizations and dashboards to track performance metrics, generate reports, and facilitate decision-making processes.
BR 1.16	Business	V. Performance Tracking and Reporting	The solution shall incorporate functionality for tracking performance metrics and generating visualizations of platform data to ensure optimal performance, maintenance facilitation, and alignment with strategic goals.

tc={D02B78D8-D24A-4BB7-9CB3-C4410C45FD37}: [Threaded comment]

Comment:

is this current databricks functionality?

Reply:

@Fordyce, Diana (ATR) - Yes, Databricks provides this functionality BR 1.17 Business V. Performance Tracking and Reporting The solution should support the definition, calculation, and tracking of key Return on Investment (ROI) metrics, such as total cost of ownership (TCO), operational and maintenance (O&M) costs, and time-to-decision, and enable integration with reporting and visualization tools to present these metrics in alignment with ATR's strategic goals and objectives.

tc={FC267EC3-3071-49EC-ABF4-6CA610C19405}: [Threaded comment]

Comment:

how does the tool accomplish this?

Reply:

@Fordyce, Diana (ATR) - It can be accomplished using Databricks dashboards. We updated the requirement to clarify it.

Reply:

just to confirm - the dashboards give you ROI metrics for how well it performed? how much it costs to host the data in the system, and how much it costs for maintenance support? all in the dashboard?

Reply:

@Fordyce, Diana (ATR) - Yes—Databricks can meet this requirement, but typically not out-of-the-box as a single turnkey “ROI dashboard.” It requires configuration and integration of its analytics, monitoring, and BI capabilities.

Updated to "should" and changed "dashboards" to "support".

	BR 1.18	Business	V. Performance Tracking and Reporting	The solution shall support scalable, fault-tolerant data ingestion and processing for batch and streaming workloads and meet the following baseline requirements:
•	Support ingestion of at least 500 GB/hour for batch workloads
•	Support streaming throughput of at least 50,000 records per second
•	Maintain end-to-end processing latency of ≤ 2 minutes for near real-time workloads

If available, Vendors should provide:

•	Minimum, average, and maximum supported throughput
•	Latency ranges for batch and streaming processing
•	Maximum supported data volumes
•	Any dependencies or configuration considerations impacting performance

tc={557F3843-43B7-4059-A3D4-56F391AD4858}: [Threaded comment]

Comment:

how is this evaluated?

Reply:

@Fordyce, Diana (ATR) - We propose to replace this requirement with "The solution shall ensure reliable, efficient, and timely data transfer rate or throughput, ingestion, latency processing by vendor best minimum and maximum data size and quantity." so the vendors can provide their metrics and we can compare.

Reply:

the sentence structure doesn't make sense. can you clarify?

Reply:

typically something like this would be defined by your RPO and RTO threshholds. Have you defined what those are - expected uptimes for the system? If you are going for something other than overall reliability then I get your point, but it's not clear from this sentence.

Reply:

@Fordyce, Diana (ATR) - This requirement is for the throughput. We added non-functional req for DR and uptime: NFR 1.17

	BR 1.19	Business	VI. Performance and Scalability	The solution shall support scalable data processing and advanced analytics for large datasets and:
•	Support processing of datasets of at least 500 GB in size
•	Support concurrent analytical workloads and users
•	Provide capabilities for statistical analysis, machine learning, and complex query processing

If available, Vendors should provide:

•	Minimum, average, and maximum supported data sizes
•	Performance benchmarks for analytical workloads
•	Any constraints impacting scalability or performance

tc={1C359B4B-7AD4-4ACA-A3C1-0816087F0E19}: [Threaded comment]

Comment:

is there a minimum threshhold the solution must maintain?

Reply:

@Fordyce, Diana (ATR) - We updated so the vendors can provide their matrics.

Reply:

the issue is, there is nothing to measure against because you aren't using quantifiable numbers.

this will make evaluation harder.

BR 1.20	Business	VI. Performance and Scalability	The solution shall reduce time-to-insight by minimizing delays in compute resource availability and automating manual data preparation and cleaning tasks.
BR 1.21	Business	VII. Cost Management and Optimization	The solution should provide capabilities to monitor, analyze, and optimize data and analytics costs, including compute and storage usage, and support integration with enterprise cost management tools to enable a comprehensive view of total costs.

tc={67CC06D0-C211-4410-A45F-BD9597EE973D}: [Threaded comment]

Comment:

how does this solution accomplish this?

Reply:

@Fordyce, Diana (ATR) - We're looking for the vendors to provide their best practice for accomplishing this. We updated the requirement.

Reply:

if you don't know if the currently used solution you wish to procure can provide this, you should use the word should not shall. If they can't meet the requirement and someone else can, this could work against you.

Reply:

@Fordyce, Diana (ATR) - Updated. Confirmed that Databricks can meet this requirement.

BR 1.22	Business	VIII. Risk Management and Future Planning	The solution should mitigate potential risks by ensuring comprehensive coverage and adoption of data use cases, facilitating thorough planning for onboarding new use cases and tools, and addressing any gaps in policies, practices, guidance, and functionality.
BR 1.22	Business	VIII. Risk Management and Future Planning	The solution shall ensure transparency in AI system decision-making processes, logic and operational parameters to ensure compliance with government laws.
BR 1.22	Business	VIII. Risk Management and Future Planning	The solution shall allow for restriction of human review of Government Data except as strictly necessary to provide the AI System to the Government or respond to incidents (including ensuring that any human access to Government Data must be logged, justified, and limited to the minimum necessary for system functionality) and such access be transparently logged and visible to the Government;
BR 1.23	Business	IX. AI Capabilities	The solution shall ensure trustworthy and reliable AI-generated outputs.
BR 1.24	Business	IX. AI Capabilities	The solution shall enable secure and governed access to AI models and data.
BR 1.25	Business	IX. AI Capabilities	The solution shall support lifecycle management of AI models.
BR 1.26	Business	IX. AI Capabilities	The solution shall enable AI-driven analytics and automation.
BR 1.27	Business	IX. AI Capabilities	The solution shall ensure continuous monitoring of AI performance and data quality.
BR 1.28	Business	IX. AI Capabilities	The solution shall support operational use of AI for real-time and batch decision-making to enable in-depth economic research and predictive analytics.
BR 1.28	Business	IX. AI Capabilities	The solution shall only use AI models from US-based companies.
FR 1.01	Functional	I. Data Integration and Interoperability	The solution shall support import of data both structured and unstructured from internal and external sources (e.g. databases, APIs, file systems, and streaming platforms).
FR 1.02	Functional	I. Data Integration and Interoperability	The solution shall support export of data to various formats ( e.g. Excel, PDF, CSV, MPEG, and MP3) and destinations within Agency environment.
FR 1.03	Functional	I. Data Integration and Interoperability	The solution shall support export of data to various formats ( e.g. Excel, PDF, CSV, MPEG, and MP3) and destinations within Agency environment.
FR 1.04	Functional	I. Data Integration and Interoperability	The solution shall provide APIs, connectors, and seamless integration with existing data analytics tools (e.g., RStudio, SQL databases) and third-party tools.
FR 1.05	Functional	I. Data Integration and Interoperability	The solution shall enable administrators to configure connections to various data sources, manage ingestion pipelines, and oversee data consumption patterns across the organization.
FR 1.06	Functional	I. Data Integration and Interoperability	The solution shall connect to data visualization tools (e.g., PowerBI, Tableau, Cognos) for creating interactive visualizations, managed within an analytics workspace for operational data oversight.
FR 1.07	Functional	I. Data Integration and Interoperability	The solution shall support statistical modeling and simulation capabilities and enable integration with machine learning services for enhanced analysis.
FR 1.08	Functional	I. Data Integration and Interoperability	The solution shall support registration of all data layers in a unified data catalog at any point of the data lifecycle, especially during data acquisition or intake. The following metadata must be supported to provide a clean, standardized "Enterprise View" of data: Lineage & Dependency, Operational Logs and Data Ingestion & Processing Metrics, Data Quality Results, Standardization Rules, Semantic Layer Information, and Performance Metadata (e.g., Indexing, partitioning strategy, and data access patterns).
FR 1.09	Functional	I. Data Integration and Interoperability	The solution shall automate data pipelines with process monitoring and alerting set up to ensure efficient operation and timely issue detection.
FR 1.10	Functional	I. Data Integration and Interoperability	The solution shall integrate the data analytics solution with a Git-based code hosting and collaboration platform designed to store, manage, and track changes to their source code for source control and an enterprise -level integrated development environment for continuous integration, ensuring code quality and a compliant Infrastructure as Code (IaC) tool that allows developers change, build and version infrastructure using declarative configuration files.
FR 1.11	Functional	II. Data Ingestion, Processing and Analytics	The solution shall automate ETL workflows and support scheduling, orchestration, and error handling for processing diverse and large datasets, minimizing manual intervention.
FR 1.12	Functional	II. Data Ingestion, Processing and Analytics	The solution shall support scalable compute for relational databases, enable rapid data transfer from data sources to integrate cloud storage, and manage database interactions through cloud, connecting to data sets, SQL database with security and compliance handled via workspace key vault.
FR 1.13	Functional	II. Data Ingestion, Processing and Analytics	The solution shall support execution environments for various scripting languages (e.g., Python, R, and shell) for data analysis, transformation, and advanced analytics.
FR 1.14	Functional	II. Data Ingestion, Processing and Analytics	The solution shall ingest and process multiple data files simultaneously to improve efficiency and accuracy in data handling.
FR 1.15	Functional	II. Data Ingestion, Processing and Analytics	The solution shall automate data ingestion tasks, including unzipping files, classification, sorting data into predefined categories (e.g., load-ready vs. native production datasets), and labeling datasets during uploads to ensure proper organization.
FR 1.16	Functional	II. Data Ingestion, Processing and Analytics	The solution shall support data ingestion throughput of >= 1TB in less than 90 minutes.
FR 1.17	Functional	II. Data Ingestion, Processing and Analytics	The solution shall implement data cleaning workflows using suitable data processing tools (e.g., PySpark or equivalent), managed and version-controlled with deployment automation capabilities.
FR 1.18	Functional	III. User Experience, Collaboration and Continuous Training	The solution shall offer a web-based, interactive user interface that allows easy navigation, configuration, and execution of data analytics tasks
FR 1.19	Functional	III. User Experience, Collaboration and Continuous Training	The solution shall provide collaborative features ( e.g. shared workspaces, real-time editing, version control integration, and dynamic document creation, enabling multiple users to work together efficiently).
FR 1.20	Functional	III. User Experience, Collaboration and Continuous Training	The solution shall maintain change history and audit trails for collaborative projects to facilitate accountability and traceability.
FR 1.21	Functional	III. User Experience, Collaboration and Continuous Training	The solution shall offer tools to create, edit, and share data visualizations, including maps and charts.
FR 1.22	Functional	III. User Experience, Collaboration and Continuous Training	The solution shall support exporting visualizations and reports in various formats (e.g., PDF, images, interactive web pages).
FR 1.23	Functional	III. User Experience, Collaboration and Continuous Training	The solution shall support code management and control mechanisms to maintain the integrity and organization of analytical scripts.
FR 1.24	Functional	III. User Experience, Collaboration and Continuous Training	The solution shall support continuous learning and skill development by providing training, resources, and technical support for data analytics and AI capabilities.
FR 1.25	Functional	IV. Security and Governance	The solution must enforce data governance policies through data lineage tracking, versioning and audit logging.
FR 1.26	Functional	IV. Security and Governance	The solution must implement role-based access control (RBAC) and other security measures to comply with security requirements, facilitate vulnerability identification, patch tracking, and compliance reporting.
FR 1.27	Functional	IV. Security and Governance	The solution must integrate with enterprise identity providers (such as Entra ID, Active Directory and Okta) to authenticate users using Single Sign-On (SSO) or multifactor authentication (MFA).
FR 1.28	Functional	IV. Security and Governance	The solution must enforce multi-factor authentication (MFA) during user login and access to sensitive resources.
FR 1.29	Functional	V. Performance Tracking and Reporting	The solution shall allow to build customizable dashboards for monitoring workflows, viewing analytics results, and managing system configurations.
FR 1.30	Functional	V. Performance Tracking and Reporting	The solution shall provide features for generating reports on system performance and metrics, including automated performance tracking and visualization of key performance indicators (KPIs).
FR 1.31	Functional	V. Performance Tracking and Reporting	The solution shall collect, and display performance metrics related to data processing, system usage, and resource utilization.
FR 1.32	Functional	V. Performance Tracking and Reporting	The solution shall generate alerts and notifications when predefined performance thresholds are exceeded or if anomalies are detected.
FR 1.33	Functional	V. Performance Tracking and Reporting	The solution shall log and report on ROI-related metrics (e.g., total cost of ownership (TCO) and time-to-decision for large data portfolios).
FR 1.34	Functional	V. Performance Tracking and Reporting	The solution must support the definition, monitoring, and reporting of service-level metrics across data processing and delivery workflows, including performance tracking and alerting for threshold breaches.
FR 1.35	Functional	VI. Performance and Scalability	The solution shall provide scalable on-demand compute resources for data processing.
FR 1.36	Functional	VI. Performance and Scalability	The solution shall automate data cleaning and transformation workflows.
FR 1.37	Functional	VI. Performance and Scalability	The solution shall enable auto-scaling features enabled, monitored to optimize resource allocation and performance.
FR 1.38	Functional	VII. Cost Management and Optimization	The solution shall track resource usage (e.g., compute time, storage) and provide reports on costs associated with data analytics operations.
FR 1.39	Functional	VII. Cost Management and Optimization	The solution shall offer dashboards or reports summarizing license usage, infrastructure spend, and data storage metrics, supporting cost standardization and consolidation efforts.
FR 1.40	Functional	VIII. Risk Management and Future Planning	The solution shall include a data catalog feature for registering, searching and managing metadata of data assets.
FR 1.41	Functional	IX. AI Capabilities	The solution shall provide dedicated modules or runtime environments to develop, train, and deploy AI/ML models within the platform.
FR 1.42	Functional	IX. AI Capabilities	The solution shall support regression analysis, statistical modeling, simulations, and AI/ML workloads.
FR 1.43	Functional	IX. AI Capabilities	The solution should support validation of responses against source or retrieved context, detection of hallucinations and unsupported content (based on policy definition), monitoring of output accuracy through dashboards, and applying input and output validation controls (e.g., PII and toxicity checks).
FR 1.44	Functional	IX. AI Capabilities	The solution shall ensure secure and governed management of AI assets by enforcing role-based access control (RBAC), managing identities and permissions, providing lineage tracking, and enabling centralized governance of AI models, data, and features.
FR 1.45	Functional	IX. AI Capabilities	The solution shall support AI model lifecycle management by enabling model development, training, validation, deployment, logging model performance metrics, supporting model versioning, and providing automated deployment pipelines.
FR 1.46	Functional	IX. AI Capabilities	The solution shall support AI-driven analytics and intelligent automation by enabling predictive analytics and machine learning workloads, generating automated insights, supporting natural language queries, and integrating AI into analytics workflows.
FR 1.47	Functional	IX. AI Capabilities	The solution shall support AI operational integration by enabling real-time inference, batch processing, workflow integration/orchestration, and API-based consumption of AI capabilities.
NFR 1.01	Non-Functional	I. Data Integration and Interoperability	The solution shall support secure integration with a robust reporting tool via data connectors, ensuring data security with Workspace key vault and monitoring.
NFR 1.02	Non-Functional	I. Data Integration and Interoperability	The solution shall provide audit logging and monitoring for all data integration activities, including data access, pipeline execution, and configuration changes.
NFR 1.03	Non-Functional	I. Data Integration and Interoperability	The solution shall integrate with a version control system for script management and utilize a continuous integration tool to enhance script accessibility and control.
NFR 1.04	Non-Functional	I. Data Integration and Interoperability	The solution shall support scalable data integration processes, capable of handling increasing data volumes and concurrency without degradation in performance.
NFR 1.05	Non-Functional	II. Data Ingestion, Processing and Analytics	The solution shall provide optimized data transfer processes to handle large dataset uploads and downloads efficiently.
NFR 1.06	Non-Functional	II. Data Ingestion, Processing and Analytics	The solution shall support efficient processing of large datasets (≥ 1 TB) and provide high-performance data processing with low latency for both batch and streaming workloads.

tc={99ABF2C5-C6FF-4C5C-A98C-0B07191BB598}: [Threaded comment]

Comment:

what is considered large?

Reply:

@Fordyce, Diana (ATR) - Updated the requirement. Included 1TB or greater for large datasets

NFR 1.07	Non-Functional	II. Data Ingestion, Processing and Analytics	The solution shall utilize a scalable and parallel data processing with integrated monitoring capabilities for performance tracking.
NFR 1.08	Non-Functional	II. Data Ingestion, Processing and Analytics	The solution shall support algorithms in the cloud to automate data sorting, with all processes and data secured through key vault.
NFR 1.09	Non-Functional	III. User Experience, Collaboration and Continuous Training	The solution shall have a user-friendly interface for users across various roles (e.g., economists, data scientists) to efficiently perform data analytics tasks.
NFR 1.10	Non-Functional	III. User Experience, Collaboration and Continuous Training	The solution shall offer comprehensive training resources to help users leverage the platform’s capabilities effectively.
NFR 1.11	Non-Functional	III. User Experience, Collaboration and Continuous Training	The solution shall be compatible with various integrated development environments (IDEs) to support coding needs and streamline workflows.
NFR 1.12	Non-Functional	III. User Experience, Collaboration and Continuous Training	The solution shall incorporate an enterprise -level integrated development environment for data scientists who need to develop, collaborate, and scale on the data analytics solution, ensuring secure access via cloud based management and deploying code through open-source automation server that enables developers to reliably build, test, deploy software, and facilitating CI/CD.
NFR 1.13	Non-Functional	III. User Experience, Collaboration and Continuous Training	The solution shall create custom training modules hosted in the cloud, with usage analytics tracked for performance and insights.
NFR 1.14	Non-Functional	V. Performance Tracking and Reporting	The solution shall provide benchmarking framework to establish baseline performance for data ingestion and processing.
NFR 1.15	Non-Functional	V. Performance Tracking and Reporting	The solution shall meet defined performance, latency, and reliability thresholds for data processing and delivery, ensuring consistent operation under concurrent workloads and varying demand conditions.
NFR 1.16	Non-Functional	VI. Performance and Scalability	The solution shall scale with larger datasets and support auto scaling features to maintain performance without degradation under increasing loads.
NFR 1.17	Non-Functional	VI. Performance and Scalability	The solution shall support an enterprise -level disaster recovery and business continuity for data and analytics workloads:

RTO: <= 1 hour RPO: <+ 5 min.

Availability: >= 99.95% uptime

If available, vendors should specify:

-Supported RTO and RPO ranges -Data replication and failover mechanisms -Multi-region or cross-zone recovery capabilities -Any dependencies or limitations impacting recovery objectives tc={72EFB4FD-8922-4AB1-9662-10AB5B6BAB23}: [Threaded comment]

Comment:

@Fordyce, Diana (ATR) - New requirement tc={D02B78D8-D24A-4BB7-9CB3-C4410C45FD37}: [Threaded comment]

Comment:

is this current databricks functionality?

Reply:

@Fordyce, Diana (ATR) - Yes, Databricks provides this functionality tc={FC267EC3-3071-49EC-ABF4-6CA610C19405}: [Threaded comment]

Comment:

how does the tool accomplish this?

Reply:

@Fordyce, Diana (ATR) - It can be accomplished using Databricks dashboards. We updated the requirement to clarify it.

Reply:

just to confirm - the dashboards give you ROI metrics for how well it performed? how much it costs to host the data in the system, and how much it costs for maintenance support? all in the dashboard?

Reply:

Updated to "should" and changed "dashboards" to "support".

tc={557F3843-43B7-4059-A3D4-56F391AD4858}: [Threaded comment]

Comment:

how is this evaluated?

Reply:

the sentence structure doesn't make sense. can you clarify?

Reply:

@Fordyce, Diana (ATR) - This requirement is for the throughput. We added non-functional req for DR and uptime: NFR 1.17 tc={1C359B4B-7AD4-4ACA-A3C1-0816087F0E19}: [Threaded comment]

Comment:

is there a minimum threshhold the solution must maintain?

Reply:

@Fordyce, Diana (ATR) - We updated so the vendors can provide their matrics.

Reply:

the issue is, there is nothing to measure against because you aren't using quantifiable numbers.

this will make evaluation harder.

tc={99ABF2C5-C6FF-4C5C-A98C-0B07191BB598}: [Threaded comment]

Comment:

what is considered large?

Reply:

@Fordyce, Diana (ATR) - Updated the requirement. Included 1TB or greater for large datasets tc={67CC06D0-C211-4410-A45F-BD9597EE973D}: [Threaded comment]

Comment:

how does this solution accomplish this?

Reply:

@Fordyce, Diana (ATR) - We're looking for the vendors to provide their best practice for accomplishing this. We updated the requirement.

Reply:

@Fordyce, Diana (ATR) - Updated. Confirmed that Databricks can meet this requirement.

tc={C94EB05C-DBB7-4F5D-A912-6113603FB6DD}: [Threaded comment]

Comment:

is this a current databricks capability?

Reply:

@Fordyce, Diana (ATR) - Databricks can be configured, but currently we're using Azure for this capability. We updated the requirement that "solution must support..."	NFR 1.18	Non-Functional	IV. Security and Governance	The solution must comply with FedRAMP security and compliance standards.
	NFR 1.19	Non-Functional	IV. Security and Governance	The solution must support Single Sign-On (SSO) for user authentication in alignment with enterprise identity standards.
	NFR 1.20	Non-Functional	IV. Security and Governance	The solution must support Multi-Factor Authentication (MFA) to ensure secure user access and compliance with security policies.
	NFR 1.21	Non-Functional	IV. Security and Governance	The solution must support infrastructure managed through Infrastructure as Code (IaC) and data security enforced through cloud based security solution.
	NFR 1.22	Non-Functional	VII. Cost Management and Optimization	The solution should minimize operational costs while ensuring optimal performance for all analytical software.
	NFR 1.23	Non-Functional	VII. Cost Management and Optimization	The solution shall manage Infrastructure as Code (IaC), ensuring cost-effective resource provisioning in the cloud with cost monitoring set up for resource usage and Management.
	NFR 1.24	Non-Functional	VIII. Risk Management and Future Planning	The solution shall provide dedicated sandbox environments for experimental tool access, managed through cloud-native security and configuration controls.
	NFR 1.25	Non-Functional	IX. AI Capabilities	The solution shall ensure reliable and scalable AI model lifecycle operations by supporting model reproducibility.
	NFR 1.26	Non-Functional	IX. AI Capabilities	The solution shall ensure reliable and scalable AI model lifecycle operations by maintaining system reliability.
	NFR 1.27	Non-Functional	IX. AI Capabilities	The solution shall ensure reliable and scalable AI model lifecycle operations by ensuring that lifecycle activities are auditable.
	NFR 1.28	Non-Functional	IX. AI Capabilities	The solution shall ensure reliable and scalable AI model lifecycle operations by enabling scalability.
	NFR 1.29	Non-Functional	IX. AI Capabilities	The solution shall enforce AI security controls for protecting sensitive data.
	NFR 1.30	Non-Functional	IX. AI Capabilities	The solution shall enforce AI compliance by enforcing security policies.
	NFR 1.31	Non-Functional	IX. AI Capabilities	The solution shall enforce AI compliance by maintaining consistent access controls.
	NFR 1.32	Non-Functional	IX. AI Capabilities	The solution shall ensure AI maintaining audit trails for all access and activities.
	NFR 1.33	Non-Functional	IX. AI Capabilities	The solution shall ensure AI executes validation processes within acceptable response time limits and maintain reliability.
	NFR 1.34	Non-Functional	IX. AI Capabilities	The solution shall ensure performant and reliable AI analytics by delivering insights within defined latency thresholds, supporting high concurrency, maintaining reliability, and sustaining performance under large data volumes.
	NFR 1.35	Non-Functional	IX. AI Capabilities	The solution shall ensure continuous and reliable AI monitoring by running monitoring processes continuously, generating timely alerts, maintaining high availability of monitoring services, and retaining monitoring data for analysis and audit purposes.
	NFR 1.36	Non-Functional	IX. AI Capabilities	The solution shall ensure efficient and reliable AI operations by meeting real-time inference latency SLAs, supporting high throughput, maintaining high availability, and enabling dynamic scalability based on workload demand.

is this a current databricks capability?

@Fordyce, Diana (ATR) - Databricks can be configured, but currently we're using Azure for this capability. We updated the requirement that "solution must support..."

is this current databricks functionality?

@Fordyce, Diana (ATR) - Yes, Databricks provides this functionality how does the tool accomplish this?

@Fordyce, Diana (ATR) - It can be accomplished using Databricks dashboards. We updated the requirement to clarify it.

just to confirm - the dashboards give you ROI metrics for how well it performed? how much it costs to host the data in the system, and how much it costs for maintenance support? all in the dashboard?

Updated to "should" and changed "dashboards" to "support".

how is this evaluated?

the sentence structure doesn't make sense. can you clarify?

@Fordyce, Diana (ATR) - This requirement is for the throughput. We added non-functional req for DR and uptime: NFR 1.17 is there a minimum threshhold the solution must maintain?

@Fordyce, Diana (ATR) - We updated so the vendors can provide their matrics.

the issue is, there is nothing to measure against because you aren't using quantifiable numbers.

this will make evaluation harder.

how does this solution accomplish this?

@Fordyce, Diana (ATR) - We're looking for the vendors to provide their best practice for accomplishing this. We updated the requirement.

@Fordyce, Diana (ATR) - Updated. Confirmed that Databricks can meet this requirement.

what is considered large?

@Fordyce, Diana (ATR) - Updated the requirement. Included 1TB or greater for large datasets

@Fordyce, Diana (ATR) - New requirement

File details come from the government source that posted it. Updated Apr 22, 2026.