As data volumes, velocity and variety grew and analytics and artificial intelligence (AI) needs increased, a Hadoop-based data lake approach gained credence in the past ten years. Strongly driven by specific business-led big-data, analytical and, more recently, AI projects-gold taps-Hadoop open-source projects have been slow to address data governance and management requirements. In this article by Dr. Barry Devlin of 9sight, you'll learn why:
- Data lakes are not fit to maintain data quality and consistency needed for successful AI and analytics apps required for digital transformation
- A modern enterprise data warehouse (EDW) is the key component of a high-quality infrastructure to support analytics and AI
- Businesses should consider migrating workloads from Hadoop
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