Why We Need Data Lakes?
A Traditional Approach to Information Architecture is No Longer Viable
Enterprise data warehousing has fundamentally shaped our way of thinking in regard to information data management and analytics. For more than 30 years, organizations have invested in data integration and analytics platforms to achieve competitive advantage thru quantifiable and strategic benefits. All analytics platforms must be performance-driven.
A traditional approach to information management has become problematic in terms of overall costs, and more importantly an inherent inability to adapt to emerging changes in the proliferation of data, new and emerging technologies and cloud-based integrated service platforms.
From a cost perspective, traditional data warehouses rely on relational databases (RDBMS) as a primary storage option. Initial capital investment for hardware, software licensing for databases, data integration and analytics platform alone. Over 70% of development costs from ETL include effort to consolidate, prepare, standardize and transform data for downstream analytics.
Read the entire article here, Why We Need Data Lakes? Chaos Sumo Smart Object Storage Blog
via the fine folks at Chaos Sumo