Dataflow

Graphically design and create each dataflow, choosing from a palette of operators that accommodate the needs of business applications while ensuring consistency across development and production environments.

• Consume and integrate data from both internal and external sources, eliminating the need to build intermediary data stores or ETL pipelines.
• Eliminate data silos, leverage the full richness of your customer information, and build more powerful business applications by harnessing data from multiple sources.
• Accelerate the build-out of new applications and provide a jumpstart for leveraging existing artifacts by sharing and reusing dataflows across your organization.

Embed analytic and machine learning models built using Python, R, and PMML into any dataflow.

• Improve business outcomes and achieve more rapid results by fully operationalizing your AI in business applications.
• Add constrained optimization models to perform in-situ tradeoffs for specified business scenarios.

A modular, flexible, and decomposed architecture enables massively parallel computing with observability, high-availability, and failover.

• Fine tune parallel computing resources and avoid brute force methods that unnecessarily assign resources to the entire application and can result in runaway compute costs by accessing observable metrics and visual cues to remove bottlenecks.
• Protect your sensitive applications by ensuring they can function in active-active architectures that are unaffected if an infrastructure-level failure were to occur.
• Make processing more cost-effective with support for multiple processing modalities, including real-time, always-on batch, and transient batch.