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This article sets out to explore some of the essential tools required by organizations in the domain of dataengineering to efficiently improve data quality and triage/analyze data for effective business-centric machine learning analytics, reporting, and anomaly detection.
From a dataengineer's point of view, financial risk management is a series of data analysis activities on financial data. The financial sector imposes its unique requirements on dataengineering. Before they adopted an OLAP engine, they were using Kettle to collect data.
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