<![CDATA[The volume of transaction data in the banking industry is growing with the increase in customers and transaction complexity. Ineffi-cient data management can lead to server overload, affect system performance, and hinder the delivery of fast, accurate services. Housekeeping processes are needed to move inactive data to sepa-rate storage, allowing the main server to function more efficiently. Pentaho Data Integration (PDI) offers an effective solution for man-aging the ETL (Extract, Transform, Load) process, which is crucial for data housekeeping. This research aims to optimize the manage-ment of banking transaction data using PDI to reduce server load and improve operational efficiency. This quantitative study applies an experimental method, with the ETL process managing Bank XYZ’s transaction data older than six months. The study uses trans-action data from Bank XYZ’s MySQL server, which will be trans-ferred to a data warehouse. The analysis applies clustering algo-rithms to filter and separate active from inactive transactions. The implementation of PDI for housekeeping effectively reduces server load and improves data management efficiency, significantly lower-ing processing time. The combined use of clustering algorithms and PDI delivers substantial improvements in managing banking transac-tion data, enhancing operational efficiency while significantly re-ducing the load on the main server]]>
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