Despite completing data platform implementations, financial institutions face widespread shortcomings: inadequate data governance, disjointed architectures/processes, and inefficient application development - collectively undermining data-driven operational capabilities.
GienTech´s Data Lake product, part of its Big Data Platform suite, has been developed by a specialized product team, drawing from extensive experience across multiple large-scale big data platform projects. It is designed to address the integrated ingestion, management, and consumption of foundational data within a data platform, enabling the automated onboarding and governance of source-aligned data into the data lake. This provides robust support for subsequent data platform development and application enablement.
The Data Lake end-to-end solution is structured around three key perspectives: system construction, data management, and data application. It delivers a holistic approach to data ingestion, storage, governance, and utilization. During the system construction phase, the solution is carefully aligned with the client's existing data asset management practices, and incorporates a data governance platform to ensure the long-term sustainability of the system. Ultimately, by unlocking the value of data assets, the solution drives continuous business advancement.
GienTech´s Data Lake solution encompasses six major modules: Data Sources, Data Lake, Data Services, Data Applications, Auxiliary Platforms for the Data Lake, and Unified Tools.
The Data Lake serves as the repository for both structured and unstructured data collected from various sources. Through processes such as data integration, cleansing, analysis, and model computation, it delivers refined outputs to data application systems.
Leveraging capabilities including data asset management, data quality management, metadata management, and data standards management, the solution ensures the standardized and compliant construction of the Data Lake.
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Data Sources
Data originates from various business systems, external sources, and supplementary inputs, encompassing both structured and unstructured formats from source systems.
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Data Lake
The Data Lake handles all data processing functions, including the Data Buffer Layer, Source-Aligned Layer, Foundation Layer, Common Processing Layer, Data Mart Layer, as well as dedicated zones for Historical Data and Sandbox Data.
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Data Services
Provides access capabilities for downstream systems, including Data Lake access, DataAPI interfaces, and BI tool integrations.
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Data Applications
Processes data based on application system requirements and delivers results to respective application systems on schedule.
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Auxiliary Platforms
Supports the overall construction of the Data Lake system through development workstations, data quality management, operations monitoring platforms, metadata management, and data dictionaries.
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Unified Tools
Data ingestion into the lake relies on a Data Collection module. The collected data files are transferred via the Data Exchange platform to complete the data onboarding process.
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Speed: Accelerated Data Ingestion
Automates data onboarding through configurable tasks, supporting both real-time and scheduled ingestion to enhance data timeliness.
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Completeness: Full-Type Data Ingestion
Supports the ingestion and storage of all data types, including structured, unstructured, semi-structured, and real-time data.
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Storage: Long-Term Online Storage
Integrates data from all source systems, eliminating data silos. Builds a subject modeling zone based on source-aligned data, leveraging big data technologies for management. Ensures long-term online storage to support historical data preservation.
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Utilization: Data Application Services
Enables application scenarios such as regulatory reporting, confirmation letters, and query requests based on source and historical data. Provides an interactive data asset visualization interface to display asset inventories, basic data attributes, and support data subscription and delivery services.

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