DEV Community: TongWu

qKnow Open-Source Agent Development Platform v2.2.3 Released: User-Defined Tools Enhance Agent-Type Bot Orchestration

TongWu — Fri, 10 Jul 2026 06:15:09 +0000

In enterprise AI agent development, agents are no longer limited to serving as conversational interfaces.

They are increasingly being integrated into business processes, data services, system operations, knowledge collaboration, and other complex enterprise scenarios.

As these use cases expand, agent development platforms need to provide:

More flexible tool integration
Clearer orchestration and management workflows
Lower configuration barriers for business users
Better support for scenario-specific Agent applications

The release of qKnow Open-Source Agent Development Platform v2.2.3 introduces improvements across Agent capabilities, system configuration, front-end usability, and known issue resolution.

The key update in this release is the enhancement of custom tool orchestration for Agent-type Bots.

1. Enhanced Custom Tool Orchestration for Agent-Type Bots

In real-world enterprise scenarios, the capabilities required from Agent Bots often vary across organizations, departments, and business processes.

For example, some Bots may need to:

Query business data
Invoke internal tools
Access enterprise knowledge bases
Connect with workflow systems
Call specific business APIs

Previously, configuring these capabilities often required developers to write code or complete parameter configuration in the back end.

This made it more difficult for business users, solution teams, and platform administrators to adjust Agent capabilities independently.

In qKnow v2.2.3, users can configure custom tools directly when creating an Agent-type Bot.

Based on actual business requirements, users can add and bind relevant tools to the Bot, reducing their dependence on back-end development and configuration.

The goal of this capability is not to turn every platform user into a developer.

Instead, it enables business users, solution specialists, and administrators to combine Bot capabilities more efficiently and align Agent applications with specific scenarios more quickly.

2. Making Tools Easier to Manage, Find, and Orchestrate

As the number of tools within an Agent platform increases, tool management becomes an important part of application development and long-term platform operation.

Providing access to tools is only the first step.

Enterprises also need a clear way to organize, locate, maintain, and reuse those tools.

qKnow v2.2.3 introduces tool category management, allowing platform tools to be grouped into structured categories.

Tools can be organized according to:

Business type
Usage scenario
Functional capability
Department or ownership
Application purpose

This makes it easier for users to locate suitable tools during Agent application development.

It also simplifies tool maintenance and Bot binding, while reducing the operational cost caused by unclear naming, ownership, or classification.

For enterprises operating multiple Agent Bots, categorized tool management can also support a more standardized approach to reusable capability assets.

3. Improving Agent Development Efficiency in Low-Code Scenarios

Enterprise teams building Agent applications often face two common challenges.

First, business requirements change frequently. Agent Bots need to be adjusted as workflows, systems, and application scenarios evolve.

Second, development resources are limited. It is often impractical to rely on engineering schedules for every capability change.

By supporting user-defined tool orchestration, qKnow v2.2.3 moves some configuration tasks from the back-end development layer to the platform user layer.

This brings the Agent Bot development workflow closer to a low-code configuration model.

Within a controlled environment, business teams can complete tasks such as:

Combining tools
Adjusting Bot capabilities
Validating application workflows
Testing scenario-specific configurations
Iterating Agent applications

This helps reduce repetitive communication between business and development teams and provides a foundation for building more scenario-oriented Agent applications.

4. Front-End and System Configuration Improvements

In addition to Agent capability enhancements, qKnow v2.2.3 introduces several improvements to the platform’s daily user experience.

Notification and Announcement Display

The notification and announcement list is now displayed in reverse chronological order.

The latest notices appear first, making it easier for users to view recent platform updates and operational information.

Navigation Bar Optimization

The release fixes an issue that could cause the top navigation bar to collapse unexpectedly.

The responsive layout logic has also been optimized to provide more stable navigation across full-page browsing scenarios.

Open-Source License Statement

The wording and display position of the open-source license statement have been updated.

This improves the presentation of legal disclosure information and supports a more standardized approach to open-source product information.

System Display Name

The system display name has been updated globally to improve consistency across the platform interface.

These changes may appear small, but standardization, stability, and consistency can directly affect everyday platform use, deployment quality, and enterprise delivery.

5. Demo Environment and Administration Fixes

To support smoother product demonstrations and solution validation, qKnow v2.2.3 also standardizes the foundational data used in the demo environment.

The release improves the basic user information and role permission settings of demo accounts.

This reduces the possibility of workflow interruptions caused by permission-related issues during demonstrations.

The release also fixes an issue in the user management module where the operations list displayed no data.

The following elements can now load correctly:

Complete user data
Batch operation buttons
Individual user operation entries

This restores the normal user administration workflow and improves the usability of the platform’s back-end management capabilities.

These fixes provide a more stable foundation for:

Product demonstrations
Private deployment
Trial validation
Internal adoption
User administration

Conclusion

qKnow Open-Source Agent Development Platform v2.2.3 is a practical release focused on improving Agent Bot tool orchestration.

The update is not simply about adding more feature entry points.

Its main value lies in strengthening user-driven configuration, improving tool management efficiency, and helping Agent applications adapt more effectively to real business requirements.

For enterprises exploring AI agent adoption, the key question is gradually changing.

It is no longer only:

Can we build an Agent Bot?

The more important questions are:

Can the Bot adapt to changing business requirements?

Can it integrate reliably with enterprise systems?

Can it participate continuously in real operational workflows?

qKnow will continue to improve its capabilities around enterprise AI agent development, tool orchestration, application configuration, and platform management.

The goal is to reduce Agent application development barriers and help enterprises introduce agent capabilities into more business scenarios in a controlled and practical way.

qData Professional Data Platform System Resource Monitoring Center: Making Platform Operations Clear at a Glance

TongWu — Fri, 10 Jul 2026 06:09:31 +0000

In day-to-day operations and maintenance, administrators often need to switch repeatedly between different modules.

They may need to:

Check whether the Spark Master is available
Confirm whether Flink TaskManagers are online
Investigate failed workflows in the DS scheduler
Review alerts to assess potential platform risks

This approach can work in smaller environments. However, as task volumes increase, more components are introduced, and business workflows become more complex, several challenges gradually emerge:

Platform health requires manual assessment across multiple systems.
Resource availability must be checked item by item.
Task failures often require cross-module troubleshooting.
When alerts are scattered across different systems, priorities are difficult to identify.

The value of the qData Professional Data Platform System Resource Monitoring Center lies in bringing these critical operational states into one unified monitoring interface.

It enables administrators to move beyond simply seeing platform data to clearly understanding what that data means.

A Unified Overview: Determine Platform Stability First

The System Resource Monitoring Center first provides a platform-wide resource monitoring overview.

From the overview page, administrators can quickly review:

The availability of core components
The number of components operating normally
The overall platform health score
Current active alerts

Compared with checking each component individually, the overview page is better suited as the first entry point for routine inspections and initial issue assessment.

For example, when operational fluctuations occur, administrators can first review:

The current platform health status
Changes compared with the previous day
The distribution of alert severity levels

This helps determine whether the issue is limited to an individual component or reflects a broader decline in platform health.

qData also centrally displays the availability, response time, operational status, and instance count of key components, including:

Spark
Flink
The DS scheduler
Metadata databases
Storage services

The purpose of this design is not to add complexity to the interface. It is to shorten the operational decision-making process.

For an enterprise data platform, valuable monitoring is not simply about displaying metrics.

It should help administrators answer three questions more quickly:

Is the platform currently stable?
Where are the risks?
What should be investigated next?

Spark Resource Monitoring: Supporting the Foundation of Batch Processing

In data platform environments, Spark typically supports large volumes of offline computation, batch-processing tasks, and resource-intensive workloads.

The stability of the Spark cluster directly affects task execution efficiency and data delivery.

The Spark resource monitoring capabilities in qData Professional provide a centralized view of:

Master and Worker status
Application execution
Resource utilization
Node conditions

Administrators can review the current Spark Master status, service address, and uptime to confirm whether the core cluster service is operating normally.

They can also review the total number of Workers, including online and offline nodes, to determine whether sufficient execution capacity is available.

Application Execution Status

At the task level, the platform displays the number of active applications and categorizes them by status, including:

Running
Waiting
Completed

This helps administrators understand the current Spark workload.

Resource Utilization

The platform also displays metrics such as:

Total CPU cores
Allocated CPU cores
Total memory
Used memory
CPU utilization
Executor status

By reviewing these indicators, administrators can identify potential issues such as:

Resource constraints
Task queues
Abnormal execution nodes
Insufficient computing capacity

For daily operations, this means Spark is no longer managed only as an independent execution engine.

Instead, it becomes part of a unified data platform operations framework.

Administrators can assess Spark health from multiple perspectives, including cluster architecture, resource utilization, and task execution.

Flink Resource Monitoring: Improving Visibility into Real-Time Workloads

As enterprise demand for real-time data continues to grow, the stability of Flink jobs has become increasingly important.

Compared with offline tasks, real-time workloads are more sensitive to:

Latency
Throughput
Checkpoints
Backpressure

Any fluctuation may affect real-time dashboards, automated alerts, or integration with business systems.

qData Flink resource monitoring provides visibility into:

JobManager status
TaskManager availability
Running jobs
Key performance metrics

Administrators can review the JobManager status, uptime, and availability to confirm whether the core Flink service is operating normally.

They can also check the number of available TaskManagers to determine whether execution resources are fully online.

Job Execution Overview

At the job level, the platform displays:

Currently running jobs
Total jobs
Completed jobs
Failed jobs

This gives administrators a quick overview of overall Flink job execution.

Real-Time Performance Metrics

qData also centralizes important metrics such as:

CPU utilization
Memory utilization
Checkpoint success rate
Job throughput
Job latency
Backpressure ratio

These indicators help administrators identify potential performance bottlenecks and operational risks in real-time workloads.

For enterprise users, the significance of this monitoring capability is that real-time processing pipelines no longer need to be investigated only after failures occur.

Their status can be continuously observed, assessed, and located during operation.

DS Scheduler Monitoring: Maintaining Stable Task Orchestration and Execution

Data platform operations depend heavily on the scheduling system.

Scheduler stability determines whether:

Workflows are triggered as planned
Tasks are distributed correctly
Failures are identified in time
Scheduling chains continue to operate normally

qData DS scheduler monitoring focuses on:

Scheduler core status
Service-chain availability
Workflow instances
Worker groups
Failed tasks

Administrators can review:

Current Master status
The number of available Workers
Active workflows
Pending tasks
The day’s scheduling success rate

These indicators help determine whether the scheduling system is operating normally.

Scheduling Service Chain

The platform also displays the status of related services, including:

Master services
Worker services
API services
AlertServer
Registry
Metadata databases

This helps administrators determine whether the scheduling service chain is functioning correctly.

Workflow and Task Execution

At the task execution level, the system displays recent workflow instances, including:

Workflow name
Current status
Start time
Duration
Owner
Execution result

For failed tasks and recent alerts, administrators can review:

Issue descriptions
Processing status
Occurrence time
Detailed information

This supports faster identification of scheduling failures, task exceptions, and offline nodes.

This capability is particularly important for enterprise data operations.

Business users may only notice that:

“The data has not arrived.”

Operations teams, however, need to determine whether the cause is:

Insufficient computing resources
A scheduling-chain issue
The failure of a specific task
An offline service or node

DS scheduler monitoring provides a direct entry point for making that assessment.

Unified Monitoring Enables a More Efficient Operations Model

The qData Professional Data Platform System Resource Monitoring Center is not simply a page that combines multiple metrics.

It is designed around the core operational challenges of enterprise data platforms, providing a unified, clear, and actionable view of platform status.

Its value is reflected in three main areas.

Greater Operational Transparency

Core components, platform health, alerts, execution engines, scheduling services, and node resources are presented in one place.

This gives administrators a clearer view of the platform’s current condition.

Faster Issue Assessment

Unified status indicators, key metrics, and operational lists reduce the need to switch repeatedly between Spark, Flink, the DS scheduler, and other systems.

Administrators can begin the investigation from a single overview rather than collecting information from multiple interfaces.

More Focused Troubleshooting

When tasks fail, resources become constrained, or nodes go offline, administrators can use the monitoring interface to narrow the scope of investigation.

This helps improve incident response efficiency and reduces unnecessary cross-module checks.

From Experience-Driven Operations to Centralized Monitoring

For an enterprise data platform, stable operation is a long-term capability.

As data volumes grow, task dependencies become more complex, and real-time and offline processing run in parallel, platform operations need to evolve.

Traditional operations often rely heavily on administrator experience and manual checks.

A more sustainable approach requires operations to become:

More visualized
More systematic
More centralized
Easier to assess
Easier to troubleshoot

The System Resource Monitoring Center supports this transition by consolidating platform health, component status, resource utilization, task performance, and alert information into one operational view.

Final Thoughts

The value of a data platform is not limited to data integration, processing, and service capabilities.

It also depends on whether the platform can continuously and reliably support business operations.

The qData Professional Data Platform System Resource Monitoring Center provides a centralized view of:

Core component status
Resource utilization
Task performance
Scheduling-chain health
Platform alerts

It helps administrators understand platform operations more clearly and identify and locate issues more promptly.

By moving from fragmented status checks to unified monitoring, faster assessment, and more efficient troubleshooting, qData is helping enterprises build a more stable, controllable, and sustainable data platform operations capability.

Moving Beyond Manual Records: A Water Resources Management Platform Connects the Full Workflow from Diversion Verification to Suspension Review

TongWu — Fri, 10 Jul 2026 06:01:36 +0000

Canal and river water diversion management may appear to be a routine operational task, but it involves a series of closely connected processes:

Daily water-condition data collection
Diversion review
Conveyance-loss allocation
Daily verification
Monthly settlement
Water-suspension handling

Each stage directly affects the accuracy of water-volume data and the fairness of settlement results.

In practice, however, intake and discharge data is often distributed across different personnel and systems.

Automated monitoring values must be compared manually with entered figures. Review, verification, and settlement processes may lack strict workflow controls. At the end of each month, repeated checks, recalculations, and confirmations are still common.

These challenges are not necessarily caused by a lack of technical capability.

More often, they result from fragmented processes, inconsistent data management, and insufficient coordination between different operational roles.

A water resources management platform can address these issues by connecting data entry, review, verification, settlement, and exception handling within a controlled and traceable workflow.

1. A Unified Entry Point: Making Daily Water Data Traceable

Daily river and canal diversion data entry is the starting point of the entire workflow.

The platform centrally manages water levels and flow rates according to:

Date
Management organization
Monitoring station
Intake or discharge outlet type

This helps standardize data definitions at the source.

Automated monitoring values and manually entered figures are displayed on the same page. Management personnel can therefore identify missing records, abnormal values, and data awaiting review without waiting until month-end settlement.

The data-entry stage addresses a fundamental question:

Is each day’s intake and discharge data complete, traceable, and already included in the review workflow?

By consolidating data at the beginning of the process, the platform provides a clearer foundation for subsequent review, verification, and settlement.

2. Review Controls: Deciding Whether Data Can Proceed

Once data has been entered, it must be reviewed before it can move into verification and settlement.

Within the diversion review function, the following information is displayed together:

Automated monitoring data
Manually entered data
Data variances
Relevant coefficients
Reviewer information
Review timestamps

Review personnel can first verify the data source and on-site conditions before deciding whether the recorded water volume should enter the next stage of the workflow.

Reviewed data then becomes the basis for monthly statistics.

When an issue occurs, users can trace it back to the specific review stage and determine whether it was caused by:

Abnormal source collection
Incorrect manual entry
Missing or incomplete review
Improper parameter or coefficient configuration

The value of the review stage lies in establishing a clear control point:

Not all entered data should automatically proceed to settlement. It must first be confirmed.

This prevents unchecked records from directly affecting monthly results.

3. Verification and Settlement: Converting Daily Data into Monthly Results

Verification and settlement are the key stages in converting daily operational data into monthly outcomes.

Daily Diversion Verification

Daily verification rechecks each day’s records.

Unreviewed data cannot proceed directly to settlement, ensuring that incomplete or unconfirmed records do not enter the monthly calculation process.

For a small number of records, users can make adjustments directly on the page.

For larger volumes of data, corrections can be completed through export and import, balancing operational efficiency with data accuracy.

Monthly Settlement

Monthly settlement consolidates:

Reviewed water volumes
Settlement volumes
Reporting status
Submission status

Once results are submitted, they are locked to prevent arbitrary changes.

Monthly settlement-volume verification summarizes data by river basin, year, and month, providing a structured basis for confirming monthly outcomes.

When reviewed water volumes and settlement volumes do not match, users can trace the discrepancy back to:

Daily water-condition data
Shared-allocation coefficients
Water-volume distribution records
Special-period handling records

The overall process follows a clear rule:

Review first, verify second, and submit last.

Each stage remains traceable, and each result can be linked back to its supporting data.

4. Managing Special Periods: Centralized Water-Suspension Handling

Water-condition data generated during suspension periods requires special treatment.

Without clear controls, this data may be incorrectly included in normal settlement, affecting the accuracy of monthly results.

Batch Suspension Review

The batch suspension review function allows users to filter monitoring stations according to:

Management organization
Suspension period
Monitoring station

Users select stations on the left side of the interface and review water levels, flow rates, and current status on the right.

This reduces the need to locate and process records one by one.

The function is especially useful when multiple monitoring stations are affected during the same suspension period.

Batch Suspension Cancellation

Operational conditions may change, or users may select the wrong time range or monitoring station during processing.

In these situations, the batch suspension cancellation function provides a correction mechanism.

After selecting the relevant management organization and time period, users can review data that has already been processed according to suspension rules.

Records that should not have been included can then be cancelled in batches and restored to the normal processing workflow.

Used together, batch suspension review and batch suspension cancellation support two important requirements:

Efficient handling of special-period data
Timely correction when operational conditions or processing decisions change

This prevents exception handling from becoming a one-way operation without a recovery path.

5. Allocation and Distribution: Clarifying How Water Volumes Are Assigned

Two common challenges arise during river and canal water conveyance:

How should conveyance losses be allocated?
How should water volumes from shared intake outlets be distributed?

The platform addresses both through standardized allocation and distribution functions.

Shared-Allocation Coefficient Management

Shared-allocation coefficient management maintains allocation coefficients by river basin and effective date.

This ensures that loss-allocation rules remain documented and traceable.

These coefficients participate in the review and settlement process. Once approved, they should not be changed arbitrarily, helping prevent inconsistencies between earlier and later calculations.

When a dispute occurs, users can trace:

The coefficient used
Its effective date
The corresponding diversion records
The related settlement results

This makes allocation rules easier to verify and explain.

Shared Intake Outlet Water-Volume Distribution

A shared intake outlet may serve multiple water users.

In these situations, the total water volume must be distributed among different recipients, such as:

Counties
Cities
Townships
Individual water users

The platform maintains allocated volumes, default proportions, and monthly settlement status within the same table.

This makes pre-settlement verification easier and reduces inconsistencies caused by manual calculations across multiple spreadsheets.

It also provides a clearer basis for resolving distribution-related questions or disputes.

Incidental Water-Use Management

Incidental water-use management provides an additional mechanism for handling dispersed water consumption outside standard intake outlets.

Annual queries display cumulative yearly usage, while monthly entry functions supplement the water volumes required for settlement.

The platform retains information such as:

Data-entry operator
Modification time
Settlement status

These records support later review, verification, and accountability.

6. A Closed-Loop Workflow: Full-Process Traceability

The river and canal diversion scenario forms a complete operational loop, covering the entire process from intake outlet master data to monthly settlement and reporting.

Daily Data Collection

Daily intake and discharge data entry consolidates both automated monitoring values and manually entered figures.

Diversion Review

River and canal diversion review confirms whether the data is reliable and whether it can proceed to verification.

Allocation and Distribution

Shared-allocation coefficients, shared intake outlet distribution, and incidental water-use entry standardize settlement rules across different users and scenarios.

Verification and Settlement

Daily diversion verification corrects issues in individual records, while monthly settlement-volume verification generates reviewed and settled water volumes.

Submission and Exception Controls

Submission locking protects confirmed results, while batch suspension review and cancellation provide additional controls for special-period data.

Together, these capabilities transform intake management from:

Manual records, month-end consolidation, and after-the-fact explanations

into:

Daily data entry, online review, process-based verification, monthly settlement, and status locking

This change is not limited to replacing paper forms with digital pages.

It establishes a controlled process in which each stage has clear inputs, outputs, responsibilities, and status records.

7. From Data Recording to Process Coordination

When every intake outlet has a complete data trail, every water volume has a documented review record, and every settlement result is supported by traceable evidence, river and canal diversion management gains a stronger foundation for standardization.

It also improves collaboration between:

Monitoring personnel
Data-entry personnel
Review personnel
Settlement personnel
Management departments

Instead of repeatedly checking fragmented records at the end of the month, teams can identify issues earlier in the workflow.

Missing data can be found during entry. Abnormal values can be addressed during review. Calculation differences can be traced during verification. Special-period records can be handled separately before settlement.

This shifts management from after-the-fact correction toward process-based control.

Conclusion

Digitalization is not the final goal.

The real objective is to make river and canal diversion management more accurate, efficient, and traceable.

When data entry, review, verification, allocation, settlement, and exception handling are connected through a controlled workflow, manual records and repeated spreadsheet reconciliation can gradually be replaced by an online process.

The value of the platform lies not only in centralizing data, but also in ensuring that:

Daily records are complete
Review responsibilities are clear
Verification results are traceable
Allocation rules are documented
Settlement outcomes are supported by evidence
Special-period data can be processed and corrected efficiently

By connecting the full workflow from diversion verification to suspension review, the water resources management platform provides a more standardized foundation for long-term operational management.

qData Open-Source or Professional Edition: Understanding the Functional Boundaries and Upgrade Path

TongWu — Fri, 10 Jul 2026 05:44:12 +0000

As enterprises move forward with data platform development, one common question often arises:

If we already have the qData Open-Source Edition, do we still need the Professional Edition?

For many enterprises, building a data platform is not a one-time project. It is a long-term initiative that evolves alongside business growth, increasing data volumes, and higher data governance requirements.

Because of this, the difference between the Open-Source Edition and the Professional Edition should not simply be understood as the difference between a “free version” and a “paid version.”

The choice should be evaluated based on several factors:

The enterprise’s current stage of data development
Business complexity
Internal technical capabilities
Production environment requirements
Security, compliance, and operational expectations

In simple terms:

qData Open-Source Edition is better suited for low-cost entry, technical validation, and lightweight business implementation.
qData Professional Edition is better suited for complex production environments, large-scale data governance, and long-term stable operation.

The two editions are not direct replacements for each other. Instead, they are designed to support enterprises at different stages of development.

1. Open-Source Edition: Getting the Data Platform Up and Running

For enterprises that are just beginning to build a data platform, the priority is often not to establish a complete system all at once.

Instead, teams first need to validate several key questions:

Can the required data sources be connected?
Is the basic data modeling process clear?
Can data development and processing workflows run successfully?
Can data quality checks meet initial requirements?
Does the platform architecture align with the enterprise’s technical roadmap?

The core value of the qData Open-Source Edition is that it helps enterprises complete these early-stage validations at a relatively low cost.

With open-source code and flexible secondary development capabilities, it is suitable for:

Individual developers
Start-ups
Small and medium-sized enterprises
Universities and research institutions
Technical teams evaluating data platform architecture
Teams that require customized development capabilities

With the Open-Source Edition, enterprises can connect commonly used data sources, perform basic data modeling and development, conduct data quality checks, and publish basic data services.

This enables teams to quickly understand and validate the core processes involved in data platform development.

For enterprises at the “zero-to-one” stage, the Open-Source Edition provides an accessible starting point for practical implementation. It helps teams establish, operate, and begin using the fundamental capabilities of a data platform.

2. Professional Edition: Supporting Stable Production Operations

As the number of data sources grows, business workflows become more complex, and more teams begin using the platform, the challenges facing the data platform also change.

At this stage, enterprises are no longer only concerned about whether a feature exists.

They begin asking questions such as:

Can the system operate reliably over the long term?
Can task failures be identified and located quickly?
Can the permission system support collaboration across multiple departments?
Can data security, masking, and auditing meet compliance requirements?
Can data assets be continuously managed and operated?
Is there a clear service response and technical support mechanism when problems occur?

These are the areas that the qData Professional Edition is designed to address.

While the Open-Source Edition focuses more on fundamental capabilities, openness, and flexibility, the Professional Edition is designed for complex enterprise production environments.

It strengthens the platform in areas such as:

System stability
Data security
Compliance
Closed-loop data governance
Operational management
Vendor-provided technical support

Data Integration and Development

The Professional Edition provides broader support for complex data components, including:

Hive
ClickHouse
Flink
Kafka

It also supports capabilities such as guided full-database synchronization, visual job scheduling, and intelligent job orchestration.

These features help enterprises reduce repetitive development work and lower long-term maintenance costs.

Data Governance

In addition to basic data quality checks, the Professional Edition supports enterprise-level governance capabilities such as:

Master data management
Security auditing
Data classification
Sensitive data masking

These capabilities help enterprises establish a more complete and standardized data governance process.

Data Assets

The Professional Edition supports:

End-to-end data lineage analysis
Data asset catalog management
Data asset value evaluation

This helps enterprises move from simply “seeing data” to actively “managing and operating data.”

Data Applications

The Professional Edition integrates BI reporting and low-code dashboard capabilities.

This enables the data capabilities accumulated through development and governance to be transformed into analytical outcomes that business teams can understand, use, and present.

3. Functional Differences Reflect Different Stages of Development

The difference between the Open-Source Edition and the Professional Edition is not simply the number of available features.

More importantly, each edition is designed for a different stage of enterprise development.

Open-Source Edition: Better Suited for the Exploration Stage

Typical scenarios include:

Enterprises that want to validate their data platform strategy at a relatively low cost
Technical teams that want to study data platform architecture
Small and medium-sized enterprises building foundational data infrastructure
Universities and research institutions using the platform for teaching and experimentation
Innovation teams that need to build prototypes quickly

These scenarios generally share several characteristics:

The business scale is relatively manageable
The technical team has a certain level of independent maintenance capability
Enterprise-level service support is not yet a critical requirement
Strict compliance and security requirements are still limited
The platform is primarily being used for validation, experimentation, or lightweight implementation

Professional Edition: Better Suited for the Production Stage

Typical scenarios include:

Financial, healthcare, and government organizations with higher data security and auditing requirements
Group enterprises that need collaboration across multiple organizations, departments, and business lines
Medium-sized and large enterprises building unified data governance and data asset management systems
Core business systems requiring high availability, high concurrency, disaster recovery, monitoring, and alerting

These scenarios generally involve:

Larger data volumes
More complex business workflows
More users and departments
Higher system stability requirements
Clearer security and compliance responsibilities
Greater demand for ongoing technical support

Therefore, the Open-Source Edition addresses the question:

How can an enterprise get started at a lower cost?

The Professional Edition addresses another question:

How can the platform operate securely, reliably, compliantly, and at scale?

4. From Open Source to Professional: A More Practical Upgrade Path

For most enterprises, choosing between the qData Open-Source Edition and the Professional Edition does not have to be an either-or decision.

A more practical approach is to move forward in stages based on the enterprise’s data development maturity.

Stage 1: Use the Open-Source Edition for Technical Validation

Enterprises can begin with the qData Open-Source Edition for technical research and small-scale pilot projects.

The focus at this stage should be on validating core processes such as:

Data source connectivity
Basic data modeling
Data development
Data quality checks
Data service publishing

The objective is to reduce early-stage trial-and-error costs and help the team determine whether the platform’s capabilities align with its technical roadmap.

Stage 2: Reassess Production Requirements as the Business Expands

As the number of data sources increases, more teams begin using the platform, and business scenarios expand, enterprises need to reassess the platform’s capabilities.

Typical signals include:

Permission management becomes more complex
Cross-team collaboration becomes difficult
Data governance requirements increase
Security and compliance become mandatory
Task scheduling and monitoring affect operational efficiency
Troubleshooting requires more time and resources
The platform begins supporting important business workflows

When these requirements begin to affect operational efficiency, it usually indicates that the data platform has moved from exploration into the production development stage.

Stage 3: Upgrade to the Professional Edition for Long-Term Stable Operation

When the data platform begins supporting core business operations or enters a complex production environment, enterprises can upgrade to the qData Professional Edition.

The Professional Edition provides more comprehensive capabilities across:

Data governance
Data assets
Data services
Data visualization
Security and compliance
Intelligent operations
Monitoring and troubleshooting

Enterprises can also gain access to vendor-provided expert services, SLA-based support, and long-term operations and maintenance assistance.

This staged approach helps control initial investment while supporting future large-scale development.

It prevents enterprises from making an excessive investment at the beginning, while also reducing the risk of repeated redevelopment when the original platform capabilities can no longer support business growth.

5. The Key Consideration Is Not the Edition, but the Stage

Building a data platform is, by nature, a process of continuous evolution.

At the early stage, enterprises need a low-cost, accessible, and verifiable entry point.

At the large-scale implementation stage, enterprises need a stable, secure, compliant, and operationally manageable production platform.

The qData Open-Source Edition helps enterprises understand how a data platform can be built.

The qData Professional Edition helps enterprises build it more reliably, use it more extensively, and manage it more effectively.

For enterprises currently developing a data platform, the more practical path is neither to pursue an overly comprehensive system from the very beginning nor to remain indefinitely at the pilot stage.

Instead, enterprises can evolve gradually based on business development:

Start with the Open-Source Edition
Complete technical practice and solution validation
Observe changes in data volume, users, and business complexity
Reassess security, governance, and operational requirements
Upgrade to the Professional Edition when production requirements increase

In other words:

Validate at a lower cost first, then move toward production-grade implementation.

This is also the core value of qData providing both Open-Source and Professional editions for enterprises at different stages of development.

qModel Open-Source Algorithm Model Platform v1.1.0 Released: Dynamic Token Authentication Now Available for API Model Integration

TongWu — Fri, 10 Jul 2026 03:57:53 +0000

The qModel Open-Source Algorithm Model Platform v1.1.0 has officially been released.

This release focuses on three areas:

Upgrading model integration capabilities
Improving the model management experience
Standardizing system pages and interactions

The core update is a comprehensive reconstruction of the API-based model integration capability. qModel now provides more clearly categorized authentication options, including support for dynamic Token authentication.

The model management interface has also been redesigned to make model configuration, debugging, and daily management more intuitive.

Together, these updates make it easier to integrate external models, improve the clarity of platform operations, and provide a more consistent user experience.

Why Model Integration Needs a More Standardized Workflow

As enterprise intelligent application scenarios continue to expand, model capabilities are evolving from standalone algorithm services toward:

Unified integration
Centralized management
Multi-scenario invocation

For enterprises, model services may come from different sources, including internally developed algorithms and third-party providers.

Efficiently connecting these services and reliably applying them to business systems, AI agent applications, and algorithm workflows has become a fundamental requirement when building a model platform.

qModel Open-Source Algorithm Model Platform v1.1.0 addresses these requirements through systematic improvements to the model integration and management workflow.

1. Reconstructed API-Based Model Integration

In v1.1.0, qModel introduces a major reconstruction of its API-based model integration capability.

The updated workflow now covers:

Model information registration
API parameter configuration
Authentication configuration
Custom input and output definitions
Online API debugging
Model validation

This creates a more complete workflow from initial model registration to integration testing and validation.

For enterprise users, the new process helps reduce the complexity involved in connecting external models.

Whether users are integrating internally developed algorithm models or third-party model services, registration, configuration, and debugging can now be completed through a more standardized process.

This helps reduce repetitive integration work and improves the efficiency of deploying model services in real-world applications.

2. Three API Authentication Modes

Different model services often use different authentication mechanisms.

Some APIs can be accessed without credentials. Others require a fixed Token or API Key. Certain services require clients to request a temporary access token before sending a model request.

To support these different integration scenarios, qModel v1.1.0 categorizes API authentication into three modes.

No Authentication

Designed for model APIs that can be accessed directly without credentials.

Fixed Credential Authentication

Supports static authentication configurations, including:

Fixed Tokens
API Keys
Other persistent credential values

Dynamic Credential Authentication

Supports authentication through dynamic Token APIs.

This mode is suitable for model services that require the platform to obtain an access token in real time before making an API request.

By providing clearer authentication configuration options, qModel can adapt more flexibly to different types of model APIs.

It also improves compatibility with both third-party model services and internally developed enterprise model services.

3. Redesigned Model Management Interface

This release also introduces a comprehensive redesign of the model management interface.

The updated interface improves both the page structure and the operational workflow, making the following tasks more intuitive:

Viewing model information
Checking model status
Configuring model parameters
Locating API debugging tools
Managing integration details

Users can now understand model status, configuration information, and debugging entry points more clearly, reducing the learning and interpretation required during daily operations.

For enterprise model platforms, the number and variety of models usually increase as business requirements evolve.

A clearer interface structure and a more intuitive workflow can improve daily management efficiency while making the platform easier to maintain over the long term.

4. Standardized System Pages

In addition to the core functional upgrades, qModel v1.1.0 introduces standardized adjustments across the platform.

This update unifies:

Page layouts
Component styles
Interaction logic
Visual presentation across modules

The goal is to provide a more consistent experience when users move between different parts of the platform.

For enterprise users, standardized page design can reduce the learning cost associated with working across multiple modules.

It also makes the overall platform clearer, more structured, and easier to use.

System page standardization is not only a visual improvement. It also provides a stronger foundation for future feature expansion and continuous platform iteration.

5. Strengthening the Foundation for Enterprise Model Applications

Overall, qModel Open-Source Algorithm Model Platform v1.1.0 focuses on strengthening the fundamental capabilities of model integration and model management.

The main updates include:

A reconstructed API-based model integration workflow
Three clearly defined API authentication modes
Dynamic Token authentication support
A redesigned model management interface
Standardized system pages and interaction patterns

The reconstructed integration process improves the external model onboarding workflow.

The introduction of multiple authentication modes enhances compatibility with different model services.

The redesigned interface and standardized pages improve the overall platform experience and provide a more maintainable foundation for future updates.

What’s Next

qModel will continue to improve its capabilities around:

Model integration
Model management
Model debugging
Model service delivery

These improvements will continue to be developed around real-world enterprise model application scenarios.

The goal is to help enterprises connect model capabilities from multiple sources more efficiently and establish a stable, clear, and scalable foundation for algorithm model management.