Improved performance. Optimized data movement. Accelerated insights.
Rapid insights from efficient processing
Run your models “in the database” through in-database processing. Take advantage of optimized query execution plans and parallel processing capabilities by moving analytics to the data, resulting in high scalability and faster time to insight.
Say goodbye to manual transformation and scoring steps for advanced analytics use cases, such as predictive modeling and machine learning. Automate model deployment processes with in-database processing and scoring accelerators, boosting data and IT operations team productivity.
Seamless access to superior data quality
Execute data quality functions – such as casing, extraction, match-code generation and identification analysis – inside the database. Eliminate network I/O overhead, and leverage multinode architecture for performance gains and improved security, as data never leaves the database.
Data that's always fresh and consistent
Minimize data movement by layering and co-locating analytics and AI in the database. Disconnected, manual processes and repetitive data movement slow decisions. Eliminate unnecessary data movement to drastically reduce processing time.