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Data Warehouse Upgrade Plan

Challenges

(1) Device test data grows by 10TB per year, and storage capacity is tight.

(2) Historical data cannot be effectively accessed and used after archiving.

(3) Massive data causes low database query performance.

(4) Traditional report statistics cannot locate defects and assess their impact.

Solving Problems

To meet the increasing business needs and solve various technical problems arising from business development

  • Technical Problems

    Enable close collaboration between
    staff and data warehouse support staff


  • Data Integration Issues

    Data extraction, transformation,
    and loading

  • Performance and Growth Issues

    Users' large-scale data query and
    consulting needs

Architecture Design

Program Value

(1) The optimized data model is a data model that is being referenced and analyzed for massive data, which greatly improves the performance of customer data query and analysis.

(2) Achieving effective data life cycle management, and solving the problem of excessively fast data increments that have long plagued customers.

(3) With the introduction of complete BI tools, data analysts can quickly and flexibly query and analyze data.

(4) Optimize the database architecture (tables, views, indexes, partitions), improve the overall performance by 20 times, general data query pages, displayed within 2 seconds, far exceeding customer expectations.

(5) According to tests, the overall query performance of the system is 20 times that of the original system, far exceeding the customer's expectations.

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