10 Must Haves of Data Warehouse Modernization for Analytics & AI Projects - ERP Spain.com - España y América Latina

Data warehouse modernization (DWM) is a natural next step in the evolution of data management for modern analytics, artificial intelligence (AI), and machine learning (ML) projects. High volume, velocity, and variety of data (big data) disrupted long-standing data management practices. Legacy data warehouses are ill equipped to handle unstructured data complexities, harness the scalability & elasticity of the cloud. Data lakes quickly became the next-generation concept for data management—optimized for big data, embracing NoSQL, powered by cloud technologies, and harnessing the power of open-source technologies such as Apache Spark and Kafka. Data management utopia? Well … not quite. Data lakes didn’t replace data warehouses. Data warehouses (cloud & on-premise) together with data lakes are now the basis for modern data architectures. Modernize your data warehouses for next-generation analytics with these must-have's: • Support multi-cloud and cloud-hybrid • Support batch, near real-time, and streaming data • Foster collaboration among data users • Support for multiple processing engines • Intelligent data management with AI and ML