In the era of big data, where 2.5 quintillion bytes of data are generated each day, the complexities and limitations of traditional data management systems become starkly evident. If data is the new oil, then effective data management is the refinery. Machine learning, the practice that empowers computers to learn from data, stands as a compelling tool to augment these refineries.

The Pillars of Data Management

The essence of data management lies in its pillars: data collection, storage, and retrieval. These have evolved over the years, shifting from relational SQL databases to NoSQL for handling unstructured data and on to advanced paradigms like Data Warehouses, Data Lakes, and Data Mesh. Traditional ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes have been essential for data integration and transformation, setting the stage for further analytics.

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