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The Data Landscape is Fragmented, but Your (Logical) Data Warehouse Doesn’t Have to Be
The current data landscape is fragmented, not just in location but also in terms of shape and processing paradigms: data lakes, IoT architectures, noSQL and graph data stores, SaaS vendors, etc. are found coexisting with relational databases to fuel the needs of modern analytics, ML and AI. In the past, traditional data warehouses were the tool of reference for consolidating data in a single location for analytics. But although they still play a pivotal role in a company strategy, the data available in other systems can’t be ignored. Physical re-consolidation of that data back to a single location, although possible, becomes less appealing by the day.
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