On one hand, data warehouses provide solid governance, high reliability and performance - on the other, data lakes offer openness, adaptability and machine learning support. Many organizations use both, plus other specialized systems. But due to the incompatibility of these architectures, and with the trend towards multi cloud environments, extracting value from data has significant challenges:
- Siloed stacks increase complexity
- Disconnected systems and proprietary data formats make integration difficult
- Governance for data and AI is complex
- Productivity suffers due to disparate data teams