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实例探究 > How we built BI on Clickhouse with row-level security in Deutsche Bank Technology Centre

How we built BI on Clickhouse with row-level security in Deutsche Bank Technology Centre

技术
  • 分析与建模 - 大数据分析
  • 分析与建模 - 预测分析
  • 应用基础设施与中间件 - 数据交换与集成
  • 应用基础设施与中间件 - 数据库管理和存储
适用行业
  • 金融与保险
适用功能
  • 商业运营
  • 质量保证
服务
  • 数据科学服务
  • 软件设计与工程服务
  • 系统集成
挑战
Deutsche Bank Technology Centre faced significant challenges in managing and analyzing data within its Investment Bank division. The natural data silos and the need for a robust Business Intelligence (BI) system were evident. The existing data warehouse solutions were either too expensive or too slow, such as Vertica and Hive, respectively. Additionally, the bank required a data-driven access control mechanism that could provide record-level granularity and full access to SQL, while reusing existing bank-wide access rules. The challenge was to find a solution that could handle heterogeneous data from 72 different data sources and systems, manage over 100 ETL jobs, and provide a seamless user experience across various UIs.
关于客户
Deutsche Bank Technology Centre is a part of Deutsche Bank, one of the world's leading financial service providers. The Technology Centre focuses on developing and implementing cutting-edge technology solutions to support the bank's operations and services. With a global presence and a diverse range of financial products, Deutsche Bank serves millions of customers worldwide. The Technology Centre plays a crucial role in ensuring the bank's technological infrastructure is robust, secure, and capable of handling the complex demands of the financial industry. The centre is responsible for managing data, developing software solutions, and ensuring compliance with regulatory requirements.
解决方案
To address these challenges, Deutsche Bank Technology Centre implemented a combination of Spark, ClickHouse, and Tableau/RShiny for their BI needs. In 2017, they adopted Spark for its powerful data processing capabilities and ClickHouse for its high-performance columnar storage. Tableau and RShiny were used for data visualization and reporting. By 2018, the solution evolved to include Alpakka for data integration, Kafka for real-time data streaming, and a Web UI for user interaction. The Access-Based Access Control (ABAC) system was integrated to provide data-driven access control with record-level granularity. This comprehensive solution allowed the bank to track changes, manage investment planning, and improve client interaction quality. The use of ClickHouse enabled fast query performance, while Spark and Kafka ensured efficient data processing and real-time analytics.
运营影响
  • The implementation of ClickHouse and Spark significantly improved the bank's ability to manage and analyze data from multiple sources.
  • The integration of ABAC provided a robust security mechanism, ensuring data-driven access control with record-level granularity.
  • The use of Tableau and RShiny for data visualization enhanced the reporting capabilities, making it easier for users to interact with the data.
数量效益
  • Managed data from 72 different data sources and systems.
  • Handled over 100 ETL jobs efficiently.
  • Utilized 4 servers with a total storage capacity of 500GB/26TB.

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