Lakehouse_Holistically Enhancing Enterprise-Wide Data Value

Lakehouse

A new open architecture that integrates data warehouses and data lakes, combining the high performance and management capabilities of data warehouses with the flexibility of data lakes. It supports multiple data types at the underlying layer, enables data sharing accessible via a unified interface, and facilitates real-time queries, bringing greater convenience to enterprise data governance.

A new open architecture that bridges data warehouses and data lakes, merging high performance with flexibility, and provides greater convenience for enterprise data governance.

Industry Pain Points

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Low Scenario Integration Efficiency

Integrating multiple scenarios accelerates the transition to general AI. For instance, multidimensional analysis, predictive analytics, data science, and machine learning deliver value across the entire business.

Breaking Down Data Silos

It breaks down the barriers between lakes and warehouses, integrating them and effectively resolving data redundancy.

Waste of Computing and Storage Resources

The Lakehouse architecture enables true storage-compute separation at an enterprise level, maximizing the utilization of all computing resources.

Value Proposition

Unified Resources

It provides containerized operations based on cloud-native technologies, compatible with the Information Technology Application Innovation ecosystem of sovereign hardware below, and offers a unified resource scheduling framework above. Through container orchestration, it can uniformly schedule various fundamental resources like computing, storage, and networking.

Unified Storage

Unified storage management reduces operational costs and prevents data silos. The plugin feature of the distributed data management system supports flexible business expansion and allows on-demand integration of other storage engines.

Stream-Batch Unification

The Lakehouse platform supports batch and stream processing unification, enabling cross-modal data fusion analysis. It handles both real-time and offline computing scenarios within the enterprise, further mining and enhancing the overall value of enterprise data.

Unified Resources

It provides containerized operations based on cloud-native technologies, compatible with the Information Technology Application Innovation ecosystem of sovereign hardware below, and offers a unified resource scheduling framework above. Through container orchestration, it can uniformly schedule various fundamental resources like computing, storage, and networking.

Unified Storage

Unified storage management reduces operational costs and prevents data silos. The plugin feature of the distributed data management system supports flexible business expansion and allows on-demand integration of other storage engines.

Stream-Batch Unification

The Lakehouse platform supports batch and stream processing unification, enabling cross-modal data fusion analysis. It handles both real-time and offline computing scenarios within the enterprise, further mining and enhancing the overall value of enterprise data.

Solution Architecture

Customer Cases

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Lakehouse Platform for a Large Retailer

To meet the goals of building a data foundation for digital transformation and operations at a large retailer, this project involves constructing a foundational Lakehouse platform on public cloud. This includes setting up functional components like data ingestion, cleansing, metric calculation, storage, and modeling. It aims to streamline and optimize data analysis for sales and wholesale, providing unified data services and APIs via the data lake. Concurrently, it involves reviewing and analyzing enterprise master data, optimizing its models and governance rules within the new platform, and refining overall data governance processes and standards.

To meet the goals of building a data foundation for digital transformation and operations at a large retailer, this project involves constructing a foundational Lakehouse platform on public cloud. This includes setting up functional components like data ingestion, cleansing, metric calculation, storage, and modeling. It aims to streamline and optimize data analysis for sales and wholesale, providing unified data services and APIs via the data lake. Concurrently, it involves reviewing and analyzing enterprise master data, optimizing its models and governance rules within the new platform, and refining overall data governance processes and standards.