Real-Time Data Requirements

Some of the challenges are: processing and delivering real-time data for monitoring (SRE) or predictions (MLOps); and managing latency in data streams. Latency affects incident response (SRE) and model accuracy (MLOps).

The Data Engineering platform assists use stream-processing frameworks like Apache Flink or Google Dataflow.

Delivering real-time data for monitoring (SRE) or predictions (MLOps).

Latency affects incident response (SRE) and model accuracy (MLOps).

The Data Engineering platform uses stream-processing frameworks like Apache Flink or Google Dataflow.

Managing latency in data streams.

Managing data stream latency is a challenge, as it directly impacts real-time decision-making, application reliability, and predictive accuracy.

The Data Engineering platform assists:

• Optimized stream processing: Ensure ultra-low latency for high-speed data workflows.

• Dynamic scaling: Adapt seamlessly to fluctuations in data volume.

• End-to-end monitoring: Gain visibility into stream performance to proactively address bottlenecks.

It can help optimize the organization’s data streams for better performance and reliability.