AISWare DataOS

Spanning across data integrating, developing, governing, protecting, opening, to operating, AISWare DataOS provides a full-lifecycle and full-process operation capability in a panoramic view by shielding technologies and data diversities. It builds an enterprise-level data intelligence center with data integration, capability sharing and service innovation, which enables digital transformation of enterprises among a wide range of industries, such as telecom, finance, government, transportation, and energy.

Product superiority

Product value


Unified Convergence of Data Resources

  • Integrated multi-source heterogeneous data to break data silos.
  • Unified and standardized data catalogs to facilitate internal and external data discovery, understanding and use.

Cost-Effective & Enhanced-Efficiency Data Development

  • Integrated stream-batch development without construction duplication in lower costs.
  • Distributed scheduling architecture for stable and efficient task execution.
  • Lineage analysis for fast problem location and resolution.

Long-Acting Data Quality Assurance

  • Various open methods such as APIs, library tables, and files.
  • Table-level, column-level, and row-level data authorization for reliable security management.

Efficient & Compliant Open Data

  • Various open methods such as APIs, library tables, and files.
  • Table-level, column-level, and row-level data authorization for reliable security management.

Data Resource Value Delivery

  • Clear data resources and deep exploration of data value.
  • Data distillation from resources to assets.

Full-Coverage on Data Controls

  • Data architecture, process control, data standards, and development specifications provided in the form of system tools.
  • Organic synthesis into data integrating, developing, opening and O&M.

Application scenario

Agile & Collaborative Delivery
  • Design in Visualization
  • Multi-Player Collaboration
  • Global Coherence
Integrated Real-Time/Offline/ Developing
  • CDC & Minifile Capture
  • Real-Time Data Push kafka
  • Real-Time/Batch Data Storage
Systematic Model Management
  • Model Control System
  • Overall Model Control
  • Improved Modeling Efficiency
  • Model Knowledge Accumulation
Data Distribution & Sharing
  • Data Sharing Services
  • Data Exchange Capability
Integrated Modele Operations
  • Operation Experience Accumulation
  • Model Co-Creation & Sharing
  • Best Practices of Smart Operation

Customer success case

Data Middle Office for Telco in Zhejiang

A telco in Zhejiang with advanced technology and leading scale required to build an enterprise-level data middle office with “data integration, capability sharing, and application innovation,” achieving unified management of data assets from various domains, continuously innovating in business, improving operations, optimizing management, promoting open sharing, and supporting the development of digital service curves. With AISWare DataOS, it saves 30% in labor costs, increases development efficiency by 50%, improves the problem verification rate by 50%, and prevented fraud totaling more than 60 million yuan annually.

  • 150 million Monthly average of service interface calls
  • 5PB Data scale
  • 100,000 Established customer labels
Data Resource Management for Government Affairs in a Municipality

The government expects to build a system to carry out consulting and planning, system construction and governance, as well as operation of the platform, so as to unify the convergence, governance and sharing of data for all units in the government. With AISWare DataOS, it has developed three subsystems, covering data developing, opening and O&M, with task loads exceeding 10,000; in terms of operation performance, it has generated data quality reports more than 100, handled data volumes over 10 billion, with interfaces exceeding 1,000, and supported health QR Code.

  • 377 Indicator program development
  • 231 Shared interface construction
  • 366 Support for shared applications
AISWare DataOS provides a bank with accurate and efficient data positioning and transparent and standardized data management

A large joint-stock commercial bank was seeking transformation from a cost center to an efficiency center and ultimately to a value center by establishing a data middle office. During the platform implementation stage, the company has achieved intelligent data inventory, sorted out data relationships through lineage analysis, and been capable to construct resource catalogs and metadata service; during the platform operation stage, it has generated nearly 100 data quality reports for customer-specific projects.

  • 5w+ management data models
  • 60% development efficiency

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