Data Management Global

Organizations are dealing with large volumes of data from an array of different data sources. These datasets vary in type and quality. At the same time, they are looking to minimize the cost of data processing and insight extraction while maximizing the efficiency and value. To satisfy these somewhat opposing requirements, they are storing data in a complex, messy landscape of data lakes, data warehouses and data marts. It requires effort, time, and money to maintain this siloed and complex data-analytic ecosystem of questionable data quality and varying data structure to form a source of truth that can be relied upon for analytics and decision making. This ecosystem evolved over decades from bandages applied to existing data management investments without consideration to a holistic approach to the data management lifecycle.

All that is changing.

Read about how to resolve todays data challenges with a lakehouse architecture . Learn  More
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