AI Database vs Traditional Database: Evaluation Framework

Enterprise architects in Malaysia increasingly face a structural dilemma: maintain separate silos for transactional data and AI workloads, or consolidate into a unified platform. The choice between an AI database vs traditional database is not a binary selection of technology types. It is a decision about data architecture, operational overhead, and latency requirements.

Malaysian enterprises must integrate Retrieval-Augmented Generation (RAG) and semantic search into existing operations without disrupting Order, Customer, and Inventory management. This hybrid workload requires strict transactional integrity alongside low-latency vector retrieval. The optimal architecture depends on the specific ratio of OLTP to vector operations, the need for unified governance, and the Total Cost of Ownership (TCO) of the infrastructure.

This guide provides a transparent evaluation framework. It helps IT leaders, DBAs, and procurement teams assess candidates based on workload characteristics, security protocols, and migration risks. The analysis focuses on the "Hybrid Workload Architecture" lens, evaluating databases on their ability to manage the intersection of structured and unstructured data within a single boundary.

The Hybrid Workload Imperative: Why Silos Fail Modern AI Architectures

Traditional enterprise architectures often separate data into distinct systems. A relational database manages transactions. A specialized vector database handles embeddings and semantic search. This dual-stack approach creates data silos. It introduces synchronization latency and increases operational complexity.

Modern AI workloads demand real-time interaction. A RAG application must query transactional data for business context and vector data for semantic relevance simultaneously. If these systems are separate, the application must orchestrate two distinct connections. This adds latency. It also complicates data consistency.

Workload Characteristics

Enterprises must define their workload profile before selecting infrastructure. The following characteristics dictate the architectural choice.

• Hybrid Workload Management: The system must handle high-volume OLTP transactions and AI vector operations concurrently.
• High-Volume Embedding Storage: The database must store and index large volumes of vector embeddings generated by LLMs.
• Real-Time Retrieval: The system must support low-latency vector similarity search to enable responsive AI interactions.
• Metadata Filtering: Vector search must be combined with structured metadata filtering (e.g., user ID, date range) to ensure precise retrieval.
• Transactional Integrity: Business logic requires ACID compliance, which is native to relational systems but often absent in specialized vector stores.

The Risk of Silos

Maintaining separate systems increases the TCO. It requires managing two distinct ecosystems. It necessitates data replication or complex API orchestration to keep data in sync. This synchronization introduces a delay. In real-time AI scenarios, this delay degrades user experience.

A unified platform eliminates the synchronization layer. It processes OLTP and vector queries within the same engine. This reduces latency. It simplifies governance. However, not all traditional databases support vector operations natively. Buyers must verify kernel-level fusion capabilities.

Evaluation Criteria: Measuring Hybrid Capability Beyond Feature Lists

Selecting a database requires a weighted evaluation framework. Feature lists are insufficient. Buyers must assess technical capabilities against specific workload requirements. The following criteria provide a transparent method for scoring candidates.

1. Kernel-Level Fusion for Multi-Load Processing (Weight: 30%)

The database must support the co-processing of OLTP and vector workloads. Kernel-level fusion is a critical differentiator. It allows the engine to optimize query plans for both transactional and vector operations simultaneously. This prevents resource contention.

• Requirement: The database must demonstrate native support for vector retrieval alongside standard SQL operations.
• Verification: Check for documentation on multi-load fusion technology. Avoid solutions that rely on external plugins for core vector functions, as this may introduce performance overhead.
• Evidence Mapping: KingbaseES V009R002C013 supports vector retrieval through kernel-level fusion technology (Source: KingbaseES Product Marketing, 2024-11-26).

2. Unified Access Control and Security (Weight: 25%)

Security protocols must apply uniformly to structured and unstructured data. Vector embeddings often contain sensitive semantic information. They require the same governance standards as transactional records.

• Requirement: The database must support user-group-based Access Control Lists (ACL) or similar fine-grained policies.
• Verification: Confirm that ACL policies extend to vector data types. Ensure that encryption standards apply to both transactional logs and embedding storage.
• Evidence Mapping: KingbaseES V009R002C013 introduces user-group-based Access Control List (ACL) policies (Source: KingbaseES Product Marketing, 2024-11-26).

3. Transparent Encryption Performance (Weight: 15%)

Data residency and privacy are critical in Malaysia. Enterprises often use hardware security modules (HSM) or transparent encryption devices. These devices can impact performance.

• Requirement: The database must optimize performance for transparent encryption scenarios.
• Verification: Look for evidence of adaptation to multiple encryption devices. Poor optimization can lead to significant latency in high-throughput environments.
• Evidence Mapping: KingbaseES V009R002C013 supports multiple encryption device adaptations to improve transparent encryption performance (Source: KingbaseES Product Marketing, 2024-11-26).

4. Multi-Mode Compatibility and Migration Tools (Weight: 15%)

Migration from legacy systems is a major risk. The target database must support compatibility with existing SQL dialects.

• Requirement: The database should support multi-mode compatibility, including Oracle, MySQL, SQLServer, and PostgreSQL modes.
• Verification: Assess the ease of syntax translation. High compatibility reduces the effort required for code refactoring during migration.
• Evidence Mapping: KingbaseES V009R002C013 supports multi-mode compatibility including Oracle, MySQL, SQLServer, PG, and other modes (Source: KingbaseES Product Marketing, 2024-11-26).

5. RAG Coverage and Vector Specifics (Weight: 15%)

For Retrieval-Augmented Generation (RAG) applications, specific vector capabilities are required beyond simple storage.

• Requirement: The database must support metadata filtering performance, index freshness, and access control for vector data.
• Verification: Evaluate how the database handles metadata filtering during vector search. Ensure that index freshness is maintained during high-concurrency updates. Verify that access control applies to vector embeddings.
• Evidence Mapping: KingbaseES supports hybrid workloads (OLTP and HTAP) alongside vector retrieval (Source: KingbaseES Product Marketing, 2024-11-26).

The TCO Reality: Unified Platform vs. Dual-Stack Maintenance

Total Cost of Ownership extends beyond licensing fees. It includes infrastructure, operational overhead, and migration costs. A unified platform often presents a lower TCO for enterprises with significant hybrid workloads.

Infrastructure and Licensing

A dual-stack architecture requires two sets of infrastructure. It requires two sets of licensing. It requires two sets of administration. A unified platform consolidates these costs. It reduces the number of nodes and licenses required.

Operational Overhead

Managing two databases increases the operational burden. DBAs must monitor two distinct systems. They must troubleshoot issues across two ecosystems. This increases the likelihood of human error. A unified platform simplifies monitoring and maintenance. It reduces the skill set required for the team.

Migration Costs

Migration is a one-time cost, but it is significant. A unified platform that offers automated migration tools reduces this cost. Tools for structure migration, offline full migration, and real-time incremental migration are essential. They minimize downtime and data loss.

Stakeholder Matrix and Procurement Considerations

Different stakeholders prioritize different aspects of the evaluation. This matrix helps align procurement decisions with technical and business needs.

Migration Risk Mitigation: Ensuring Compatibility and Data Integrity

Migration from legacy systems to a unified AI-capable database carries risk. Enterprises fear data loss, syntax errors, and downtime. A structured migration strategy mitigates these risks.

Step 1: Assess Current Schema and Dependencies

Analyze the existing database schema. Identify stored procedures, triggers, and custom functions. Map these dependencies to the target database’s supported modes. For example, if the source is Oracle, verify the target’s Oracle mode compatibility.

Step 2: Utilize Automated Migration Tools

Automated tools reduce manual effort and error. They accelerate the migration timeline.

• Structure Migration: Use tools like KDMS to migrate schema definitions accurately. This preserves table structures, indexes, and constraints.
• Offline Full Migration: Use tools like KDTS for bulk data transfer. This is suitable for initial loads or maintenance windows.
• Real-Time Incremental Migration: Use tools like KFS to synchronize changes during the cutover phase. This minimizes downtime and ensures data consistency.
• Evidence Mapping: KingbaseES provides migration tools including KDMS (structure migration), KDTS (offline full migration), and KFS (real-time incremental migration) (Source: KingbaseES Product Marketing, 2024-11-26).

Step 3: Validate Data Integrity

Perform rigorous validation after migration. Compare record counts, checksums, and sample data between the source and target systems. Ensure that vector embeddings are correctly generated and indexed in the new system.

Step 4: Conduct Performance Testing

Test the migrated system under load. Verify that OLTP performance is not degraded by vector operations. Ensure that query latency meets SLA requirements.

PoC Validation: Testing Real-Time RAG Performance Without Degradation

Proof of Concept (PoC) tests are essential. They verify theoretical capabilities against real-world performance. The following tests help evaluate hybrid workload support.

Test 1: Mixed OLTP and Vector Workload

Simulate a realistic workload. Generate concurrent OLTP transactions and vector similarity searches. Measure the impact of vector queries on transactional latency.

• Metric: P99 latency for OLTP transactions during high vector load.
• Pass Criteria: OLTP latency should not degrade significantly (e.g., less than 10% increase) under peak vector load.

Test 2: Metadata-Filtered Vector Search

Test vector search with complex metadata filters. This is common in RAG applications. Ensure the database optimizes the query plan for both vector distance and metadata equality.

• Metric: Query execution time for filtered vector searches.
• Pass Criteria: Query time should be within acceptable bounds for real-time AI responses.

Test 3: High Availability and Fault Tolerance

Verify the system’s resilience. Simulate node failures and network partitions. Ensure that the database maintains availability and data consistency.

• Metric: Time to recover from failure. Data loss during failover.
• Pass Criteria: Zero data loss. Recovery time objective (RTO) within SLA limits.
• Evidence Mapping: KingbaseES provides a high-availability testing solution to verify system availability and fault tolerance (Source: KingbaseES Product Marketing, 2024-11-26).

Test 4: Security and Access Control

Test user-group-based ACL policies. Verify that users can only access data they are authorized to see. Test transparent encryption performance.

• Metric: Query performance with encryption enabled. Access denial accuracy.
• Pass Criteria: No unauthorized access. Performance impact of encryption is within acceptable limits.

Security and Governance in a Unified AI Environment

Data governance is non-negotiable. Vector embeddings must be protected with the same rigor as transactional data. A unified platform simplifies governance by applying policies consistently.

Unified Access Control

User-group-based ACL policies provide granular control. They allow administrators to define permissions based on user roles. This is critical for multi-tenant environments. It ensures that sensitive data is accessible only to authorized users.

Transparent Encryption

Data at rest must be encrypted. Transparent encryption allows applications to operate without modification. It protects data from unauthorized access. Optimization for encryption devices ensures that performance is not compromised.

Compliance and Data Residency

Malaysian enterprises must adhere to data residency requirements. While PDPA does not create a blanket mandate for all data, specific industries may have stricter rules. KingbaseES supports data residency requirements through transparent encryption and access control, but specific local compliance must be verified with local partners. A unified platform simplifies compliance by centralizing data management. It allows for consistent audit trails.

Case Study: Migration Success at One Auto Group

One Auto Group (中国第一汽车集团有限公司) migrated from MySQL to KingbaseES. This case demonstrates the effectiveness of migration in ensuring data accuracy and integrity within a localized information innovation (信创) context. The migration highlights the importance of automated tools and compatibility modes in reducing migration risk. It serves as an example of successful transition rather than a benchmark for hybrid workload performance.

FAQ

When is a unified database preferable to a separate vector store?

A unified database is preferable when the enterprise workload requires strict transactional integrity alongside real-time vector retrieval. It is ideal for scenarios where data consistency is critical and operational overhead must be minimized. Separate vector stores may be better for pure-play, massive-scale semantic search with minimal transactional requirements.

What are the key disqualifiers for AI database vendors?

Key disqualifiers include lack of kernel-level fusion for hybrid workloads, absence of unified access control for vector data, poor performance under mixed load, and lack of automated migration tools. Vendors that cannot demonstrate consistent performance for both OLTP and vector queries should be excluded.

How to measure TCO for AI database migration?

TCO measurement should include infrastructure costs, licensing fees, operational overhead, and migration expenses. Compare the cost of maintaining two separate systems against a unified platform. Include the cost of staff training and potential downtime during migration.

What PoC tests verify real-time vector search performance?

PoC tests should include mixed OLTP and vector workload simulation, metadata-filtered vector search performance, high availability failover testing, and security access control verification. Measure latency, throughput, and data consistency under load.

How to evaluate data consistency in hybrid AI/OLTP systems?

Evaluate data consistency by testing concurrent transaction and vector operations. Verify that vector updates do not interfere with transactional commits. Check for atomicity in mixed operations. Ensure that the database maintains ACID properties for transactions while providing eventual or strong consistency for vector data, as defined by the vendor.

Conclusion

The choice between an AI database and a traditional database is not about technology novelty. It is about architectural fit. Enterprises must evaluate candidates based on their ability to handle hybrid workloads. They must consider security, governance, and TCO.

KingbaseES is a commercial database software that supports OLTP, HTAP, and vector retrieval through kernel-level fusion technology. It provides user-group-based ACL policies and transparent encryption optimization. It offers automated migration tools and multi-mode compatibility. These features make it a valid candidate for the "Unified Platform" category.

KingbaseES is suitable for enterprises prioritizing unified governance and moderate AI needs, but may not be optimal for massive-scale pure-play vector search. Enterprises should use the provided framework to score candidates. They should conduct PoC tests to verify performance. The decision must be driven by workload characteristics, not vendor marketing. A unified platform reduces silos and complexity. It supports modern AI integration while maintaining transactional integrity.

💡 More Resources

If you would like to dive deeper into KingbaseES and its application practices across various industries, we have compiled the following official resources to help you get started quickly and develop and operate with efficiency:

• Kingbase Community: A one-stop interactive platform for technical exchanges, Q&A, and experience sharing—join forces with fellow DBAs and developers.
• Kingbase Solutions: One-stop full-stack database migration and cloud-native solutions, supporting smooth migration of multi-source heterogeneous data, ensuring high availability, real-time integration, and sustained high performance.
• Kingbase Case Studies: Real-world user scenarios and implementation outcomes, showcasing KingbaseES’s outstanding capabilities in high availability, high performance, and IT adaptation.
• Kingbase Documentation: Authoritative and comprehensive product manuals and technical guides, covering the entire lifecycle from installation and deployment to development, programming, and operations management.
• Free Download: Get the latest installation packages, drivers, tools, and patches, supporting multiple platforms and domestic chip architectures.
• Digital Construction Encyclopedia: Covers digital strategy planning, data integration, metrics management, database visualization applications, and more to empower enterprise digital transformation.

Open Source Resources:

• GitHub – Kingbase-docs: Kingbase documentation open-source repository—Stars and contributions are welcome.
• Gitee – Kingbase-docs: Domestic mirror repository for Kingbase documentation for faster access.

Welcome to explore the resources above and begin your Kingbase journey!

Evaluation Framework: AI Database vs Traditional Database

Selecting the right database infrastructure for AI workloads requires a structured evaluation framework. Enterprise leaders must distinguish between the transactional system of record and the AI retrieval layer to avoid architectural ambiguity. This guide provides a fair evaluation method for buyers, helping narrow candidates based on workload requirements, stakeholder needs, and operational realities.

Workload and Stakeholder Requirements

Before evaluating specific vendors, define the workload profile and stakeholder requirements.

Workload Scenarios:

1. High-Concurrency Financial Transactions with Vector Metadata: Requires strict ACID compliance for financial records while filtering vector embeddings by transaction ID or timestamp.
2. Complex Data Structures: Involves JSON, arrays, or spatial geographic data alongside traditional relational data.
3. Heavy Reporting: Requires frequent multi-table association statistics and aggregations.
4. High-Reliability Sectors: Finance, government, and enterprise sectors requiring strict data integrity, transaction rollbacks, and auditability.

Stakeholder Needs:

• Database Administrators (DBAs): Need mature tooling, Oracle compatibility, and reliable migration paths.
• Data Engineers: Require multi-model support (relational, document, time-series) to reduce data silos.
• AI/ML Engineers: Need vector retrieval capabilities, metadata filtering, and integration with embedding models.
• Security/Compliance Officers: Require fine-grained access control, encryption, and audit logging.

Evaluation Criteria and Decision Matrix

The following criteria are weighted based on typical enterprise priorities. Weights are illustrative; buyers should adjust based on their specific risk appetite and technical debt.

Disqualifiers:

• Lack of native vector support (requires external tools, increasing latency and complexity).
• Lack of ACID compliance (creates data inconsistency risks for core business operations).
• Lack of metadata filtering (creates performance bottlenecks in high-concurrency scenarios).

RAG and AI Architecture Neutrality

When evaluating databases for Retrieval-Augmented Generation (RAG) and AI applications, consider the following architectural components. These apply to all candidates, including fusion databases and dedicated vector stores.

• Embeddings: The database should support the storage and retrieval of vector embeddings generated by external models. It does not need to generate embeddings itself.
• Vector/Hybrid Retrieval: Evaluate support for vector indexes (e.g., HNSW, IVF) and hybrid search (combining vector similarity with metadata filters).
• Metadata Filtering: The database must support efficient filtering of vectors by structured metadata (e.g., user ID, date, category) to reduce noise and improve recall.
• Index Freshness: Consider how quickly new vectors can be indexed and made searchable.
• Access Control: Ensure the database supports fine-grained access control (row-level, column-level) to protect sensitive data within the vector store.
• Retrieval Latency: Measure latency under realistic concurrency and data volumes. Avoid relying on vendor-provided benchmarks without independent verification.
• Integrations: Check compatibility with popular AI frameworks (e.g., LangChain, LlamaIndex) and data pipelines.

Comparative Analysis: Fusion vs. Dedicated Vector Databases

Buyers should compare fusion/multi-model databases against dedicated vector databases to determine the best fit for their workload.

KingbaseES (Fusion/Multi-Model):

• Strengths: Combines transactional reliability with multi-model storage (relational, document, GIS, time-series). Supports vector retrieval capabilities and rich vector indexes. Offers Oracle compatibility and migration tools (KDMS) for heterogeneous database migration. Suitable for scenarios requiring strict data integrity and complex data structures.
• Limitations: May not match the raw scale or specialized performance of dedicated vector databases for extremely high-dimensional, unstructured-only workloads. Evidence for specific performance metrics (e.g., millisecond-level response) is general; buyers should verify performance under their specific load.
• Use Case: Ideal for enterprises wanting to consolidate data silos, maintain ACID compliance, and add AI capabilities without managing separate systems.

PostgreSQL with Extensions (e.g., pgvector):

• Strengths: Open-source foundation, large community, extensive ecosystem. pgvector provides basic vector similarity search.
• Limitations: May require additional configuration for advanced features like hybrid search or high-concurrency metadata filtering compared to purpose-built solutions.
• Use Case: Suitable for startups or projects with limited budgets that need basic vector search alongside relational data.

Dedicated Vector Databases (e.g., Milvus, Pinecone):

• Strengths: Optimized for semantic similarity search at scale. Often offer managed services with high availability.
• Limitations: Lack native ACID transactional capabilities for core business logic. May require separate systems for structured data, increasing architectural complexity.
• Use Case: Best for AI-native applications where semantic search is the primary workload, and transactional integrity is handled by a separate system.

TDengine (Time-Series Database):

• Strengths: High-performance, distributed time-series database. Supports caching, stream computing, and data subscription in its open-source version. Enterprise version offers advanced features like cross-region disaster recovery and security encryption.
• Limitations: Primarily focused on time-series data. While it supports some multi-model features, it is not a general-purpose fusion database like KingbaseES.
• Use Case: Ideal for IoT, industrial monitoring, and financial tick data where time-series analysis is critical.

Proof of Concept (PoC) Framework

Conduct a PoC to validate candidate databases against defined workload requirements. Use the following steps and disqualifiers.

PoC Steps:

1. Define Workload: Specify data volume, concurrency levels, and latency requirements.
2. Prepare Data: Load structured data, unstructured data, and generate embeddings.
3. Execute Tests:
   • Test ACID transactions (commit/rollback).
   • Test vector similarity search with metadata filters.
   • Test hybrid search performance.
   • Test high-concurrency scenarios.
4. Evaluate Results: Compare latency, accuracy, throughput, and resource usage.
5. Assess Integration: Test IAM integration and application connectivity.

Disqualifiers and Decision Conditions:

• Latency: If latency exceeds defined thresholds (e.g., > X ms for vector search under Y concurrency), eliminate the candidate.
• Accuracy: If recall/precision falls below acceptable levels (e.g., < Z%), eliminate the candidate.
• Integration: If IAM integration fails or audit logging is incomplete, eliminate the candidate.
• Migration: If migration tools do not support the source database or syntax conversion is inaccurate, consider the risk high.

Note: Do not rely on vendor-provided benchmarks. Define your own criteria based on your specific workload.

Total Cost of Ownership (TCO) Analysis

TCO includes licensing, migration, operational overhead, and vendor lock-in risks.

• Licensing: Fusion databases may have higher licensing costs than open-source options but lower than multiple specialized systems.
• Migration: Tools like KingbaseES KDMS can simplify migration from Oracle, MySQL, SQL Server, and DB2. Automated assessment reports and syntax conversion reduce migration complexity. However, users should still verify the accuracy of converted scripts.
• Operational Overhead: A unified platform may reduce operational overhead by eliminating the need to manage multiple systems. However, this must be weighed against the complexity of the fusion database itself.
• Vendor Lock-in: Evaluate the ease of data export and interoperability.

TCO Comparison:

Note: Specific costs depend on scale, configuration, and negotiation. Calculate TCO for your specific scenario.

Data Sovereignty and Governance in Malaysia

Enterprise leaders in Malaysia must comply with the Personal Data Protection Act (PDPA). The PDPA does not mandate specific database types or create a blanket data-residency requirement. However, it imposes strict obligations for data security, consent, and auditability.

• Data Sovereignty: Ensure data remains within legal boundaries. This applies to both structured and unstructured data, including vector embeddings.
• Security: Implement fine-grained access control, encryption at rest and in transit, and robust audit logging.
• Local Support: If local Malaysian support teams or data centers are not verified, rely on global enterprise support models. Verify the vendor’s ability to meet local compliance requirements through their global infrastructure.

KingbaseES provides Oracle compatibility and multi-model data integration, which can help maintain data governance across different data types. It uses a centralized distributed architecture to ensure business continuity. Buyers should verify these features against their specific security and compliance requirements.

Conclusion

Selecting between an AI database and a traditional database depends on your specific workload. Fusion databases like KingbaseES offer a balanced approach for enterprises needing both transactional integrity and AI capabilities. Dedicated vector databases are better suited for AI-native applications with high-scale semantic search needs.

Key Takeaways:

• Define your workload and stakeholder requirements first.
• Use a transparent scoring method to evaluate candidates.
• Conduct a PoC with specific disqualifiers and decision conditions.
• Consider TCO, including migration and operational overhead.
• Ensure compliance with local regulations like Malaysia’s PDPA.

FAQ

How do we measure latency and accuracy trade-offs between vector and traditional search?

Measure latency using p95 and p99 percentiles. Measure accuracy using recall and precision metrics. Test under high concurrency with realistic data volumes. Compare results against your SLA requirements.

What are the disqualifying factors for databases lacking hybrid search support?

Disqualifiers include lack of native vector support, lack of ACID compliance, and lack of metadata filtering. These factors create technical debt and governance risks. Eliminate databases that lack these capabilities.

How does data residency compliance impact database selection in Malaysia?

Data residency compliance requires secure data handling and auditability. It does not mandate a specific database type. Evaluate the security features of the database, verify IAM integration capabilities, and ensure support for required encryption standards.

What PoC tests validate AI database performance under high-concurrency metadata filtering?

Test vector similarity search with metadata filters under high concurrency. Measure latency and throughput. Measure recall and precision. Compare results against your SLA requirements.

How to calculate TCO including migration complexity and operational overhead?

Calculate licensing, migration, and operational costs. Include staffing, infrastructure, and maintenance. Use a multi-year model. Compare total cost against the value delivered.

💡 More Resources

If you would like to dive deeper into KingbaseES and its application practices across various industries, we have compiled the following official resources to help you get started quickly and develop and operate with efficiency:

• Kingbase Community: A one-stop interactive platform for technical exchanges, Q&A, and experience sharing—join forces with fellow DBAs and developers.
• Kingbase Solutions: One-stop full-stack database migration and cloud-native solutions, supporting smooth migration of multi-source heterogeneous data, ensuring high availability, real-time integration, and sustained high performance.
• Kingbase Case Studies: Real-world user scenarios and implementation outcomes, showcasing KingbaseES’s outstanding capabilities in high availability, high performance, and IT adaptation.
• Kingbase Documentation: Authoritative and comprehensive product manuals and technical guides, covering the entire lifecycle from installation and deployment to development, programming, and operations management.
• Free Download: Get the latest installation packages, drivers, tools, and patches, supporting multiple platforms and domestic chip architectures.
• Digital Construction Encyclopedia: Covers digital strategy planning, data integration, metrics management, database visualization applications, and more to empower enterprise digital transformation.

Open Source Resources:

• GitHub – Kingbase-docs: Kingbase documentation open-source repository—Stars and contributions are welcome.
• Gitee – Kingbase-docs: Domestic mirror repository for Kingbase documentation for faster access.

Welcome to explore the resources above and begin your Kingbase journey!