Benefits of Real-Time Transaction Processing in Banking and Financial Services

Data Hydration covers the activities that support AI models being supplied with an adequate supply of essential information to allow them to thrive.

As enterprises deploy AI agents across procurement, customer service, and financial operations, the observability gap is becoming a governance crisis.

Data immediacy goes beyond the concepts of streaming data and data-in-motion. It adds the dimension that data has a very specific time value. The less recent the data is, the less valuable it is. Here are the issues you should consider to implement data immediacy and realize its benefits.

Tomer Shiran, Co-Founder of Dremio, provides an insider’s perspective about Apache Iceberg, its emergence as a standard table format, and how AI is optimizing query performance.

Running AI experiments as ventures, such as distinct, startup-like entities with their own mandates, teams, and freedom to imagine, is one of the few mechanisms that gives a scaled company a real shot at genuinely new thinking.

Organizations that treat AI as a standalone initiative see incremental results. Those that treat it as a data architecture transformation, however, unlock sustained performance improvements.

Drone programs have already proven their value as a data collection tool across construction, agriculture, energy, and infrastructure. The next frontier is making that data work harder.

For AI to work in production, models are not enough. Systems need access to current, external information that can be used directly in decision-making.