Data management has evolved into a very different discipline from what it was just 10 years ago. As more data lives natively in the cloud, it is assuming new patterns of origination, structuring, distribution, processing, governance, contextualization, and consumption. In this session, James Kobielus will discuss the evolutionary trends that are shifting the shape of enterprise data in this decade.
Kobielus will dissect the following transformative trends:
- Multimodel data is becoming the foundation of the data lakehouse, online transactional analytical processing, and other unification scenarios that hinge on agile blending of relational and nonrelational sources.
- Serverless data is becoming a foundation for the building and deployment of scalable, event-driven, stateless analytics.
- Kubernetes data is becoming the lifeblood of distributed analytics, as innovative platforms are designed to manage persistent application state in spite of container technologies’ limitations in this regard.
- Graph data is becoming the largest, most resource-consumptive information on the planet, as it drives the contextualization upon which IoT, edge, mobility, cybersecurity, and other global applications rely.
- Hyperledger data is finding a sweet spot in enterprise strategies as immutable distributed logs for data transactions, sensor networks, and distributed supply chains.
- Synthetic data is becoming an essential ingredient for boosting productivity of MLOps pipelines while reducing data scientists’ need for sensitive PII and other operational data to train their models.
- Active metadata is driving more autonomous application, system, and networking applications through its processing with embedded machine learning.