This session will include a moderated Q&A featuring questions from the live audience.
Data is central to business intelligence, advanced analytics, artificial intelligence, and other strategic enterprise applications. Over the past decade, enterprises everywhere have been migrating their data assets from on-premises environments to cloud-based platforms that offer greater scalability, performance, flexibility, and cost-effectiveness.
Data modernization is an ongoing process, not a one-time migration. Modern cloud platforms are evolving into scalable, distributed, low-latency, on-demand environments for the most sophisticated enterprise data, analytics, and operational applications.
In this session, TDWI senior research director James Kobielus will discuss new frontiers in data modernization, addressing several key trends:
- Openness: Modern cloud platforms present open APIs for access, programming, manipulation, monitoring, and management of all capabilities
- Searchability: Modern cloud data platforms power self-service analytics that enable users to use natural language search for extracting fine-grained data insights and incorporate catalogs for discovery, search, and management of all data and analytics assets
- Distribution: Modern cloud data platforms enable distributed deployment of storage, processing, memory, bandwidth, and other resources for agile provisioning, scaling, and performance optimization
- Composability: Modern cloud data platforms support self-service, visual, low-code programming and operationalizing of capabilities as composable microservices
- Containerization: Modern cloud data platforms enable orchestration of workloads within a containerized cloud-native architecture
- Elasticity: Modern cloud data platforms support elastic provisioning of compute, storage, memory, and network resources to support dynamic changes to workloads and usage patterns
- Simplicity: Modern cloud data platforms incorporate no-copy architectures that simplify data storage, accelerate query performance, and facilitate parallelization of complex machine learning processes