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Discovery to design the data architecture and ML platform for a major payment service in Uzbekistan
2 years
10
An assessment of the project's labor costs and budget has been completed.
key stakeholders
for
The case for a data platform has been developed for 10 key stakeholders
Customer
The largest payment company in the Uzbek market
Task
Development of a target architecture and roadmap for the implementation of a data platform based on stakeholder requirements and the Data Lakehouse approach, with an assessment of resources, timelines, and budget.
Challenges/Characteristics
- Multiple stakeholders: The project involved the CIO, CDO, Data team, CCO, CSO, deputy chairmen of the board, CMO, CPO, and CFO.
- High competition: Growing competition among payment companies in Uzbekistan.
- Need for modernization: It was necessary to implement modern methods, technologies, and data tools to find new sources of revenue and customers, and reduce costs.
The target architecture is based on open-source technologies.
The solution is based on the Data Lakehouse approach, separating the data processing layer from the storage layer.
The architecture proposes the use of the following technologies: Apache Airflow, Iceberg, Trino, S3, Spark, Clickhouse, OpenMetadata, Apache Superset, and others.
The solution is based on the Data Lakehouse approach, separating the data processing layer from the storage layer.
The architecture proposes the use of the following technologies: Apache Airflow, Iceberg, Trino, S3, Spark, Clickhouse, OpenMetadata, Apache Superset, and others.
Technical solution
Business values
Gathering requirements:
The main requirements for the platform from various stakeholders have been identified.
Transparency of changes:
A clear understanding of the current situation and planned changes is ensured.
Stakeholder rationale:
The case for the need for a data platform has been developed for 10 key stakeholders.
Resource saving:
An assessment of the project's labor costs and budget for 2 years has been completed.
The foundations for cost optimization have been laid.
Process modeling:
Models of business processes in AS IS (as is) and TO BE (as will be) states have been developed.
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Development of software
and Big Data solutions
and Big Data solutions
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