The enterprise unified data asset portal, acting as a data asset topology map, supports cross-role data query, end-to-end lineage tracing, and transparent exposure of data processing logics. Data assets are automatically cataloged during development, requiring zero manual curation.

Product Features

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  Unified Metadata Management

  The platform supports automated metadata collection from various sources, making it convenient for users to track historical business operation data.

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  Automatic Generation and Service Capability

  Based on enterprise metadata managed uniformly by the platform, users can perform data queries and viewing through the asset catalog.

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  Comprehensive Data Asset Types

  The platform supports the definition, inventory, and planning of various database resources, packaging business knowledge-rich models and logical resources into data products.

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  Graphical Analysis

  The platform provides graphical lineage analysis, impact analysis, and full-chain analysis functions, helping users quickly understand metadata relationships.

Core Capabilities

Data Asset Portal

Data Asset Map for Data Center Platform: Search-driven visualization that reveals data basics, scale, applications, lineage, and quality at a glance.

Metadata Collection

Collects data sources and their relationships through metadata standards, centrally registers and manages them, forming an automatic global view of data sources, enabling automatic data information acquisition based on metadata. For external assets, metadata is collected through interface-based collection methods.

End-to-End Lineage Analysis

Supports end-to-end lineage information analysis, automatically collects ETL metadata, clearly and intuitively discovers data flow relationships, upstream and downstream levels, and table quantities. The lineage flow diagram can locate specific task information and table information.

Asset Inventory

Based on data asset management, statistics on metadata quantity and distribution managed by the platform. Metadata inventory includes: total metadata statistics, popular searches, asset collection rankings, model distribution by data theme, and asset proportion analysis.

Application Scenarios

Success Cases

Conch Profiles

As a key high-tech enterprise in traditional industrial manufacturing, Conch Profiles faces issues such as poor horizontal scalability of existing systems, traditional databases unable to support massive data, unclear boundaries between cloud and edge layers, and insufficient edge data storage capacity in their digital transformation journey. Based on these challenges, they needed to build an enterprise data middle platform to construct a large-scale data-driven and data-intelligent production service system.

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Yinhua Fund

With rapid growth in business data scale and business development, Yinhua Fund Group has higher requirements for its data warehouse system. The main challenges include low development efficiency and high maintenance costs of the original Oracle data warehouse, single service form, reliance on manpower increase to meet business needs, unclear data structure in core business marketing, investment research, and anti-money laundering operations, and significant bottlenecks in big data computation and storage. There is an urgent need to meet business requirements for high-performance data processing, efficient data services, complex business calculations, massive data queries, metadata, and data quality management. Against this business background, the company proposed building a fund data middle platform in 2020.

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AEON

AEON is a leading comprehensive retail and service enterprise group in Asia, with over 500 member companies. In recent years, as AEON's business continues to grow, the role of data in promoting business has become increasingly prominent. The group fully recognizes the importance of data platforms for business development and has initiated a data center platform project to establish a unified data platform connecting the group's front and back-end core business systems. Through the data middle platform, business sector data is aggregated, enabling data interconnection, data standardization, and forming data assets, providing reusable data services and capabilities for business and data development teams based on technology and big data capabilities, creating a data support platform for AEON's digital transformation.

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A Petrochemical Company

The petroleum industry is a knowledge and technology-intensive industry with multiple disciplines and specialties configured, penetrated, and collaboratively researched. The main business of upstream enterprises involves oil and gas exploration, oil and gas field development, drilling engineering, downhole operations, surface construction, material management, business management, water and electricity, communications, medical and health, and more. Information technology in the petroleum industry has consistently accompanied the development of petroleum enterprises and played a huge role. Petroleum industry data has several significant characteristics: extremely dispersed data distribution, massive data volume, complex data structure, high data utilization value, multiple storage media, complex formats, and complex software usage environments...

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