Batch processing, a long-established model, involves accumulating data and processing it in periodic batches upon receiving user query requests. Stream processing, on the other hand, continuously ...

In this manner, Lambda satisfied the data processing needs for a certain class of applications that valued high-throughput, low-latency, fault-tolerance, and data accuracy. Many organizations—in ...

As AI shifts from experimental phases to mission-critical roles—such as fraud detection, live recommendation engines, and real-time video analytics—the traditional batch-first data processing approach ...

Lambda architecture has been a popular solution that combines batch and stream processing. Kartik Paramasivam at LinkedIn wrote about how his team addressed stream processing and Lambda ...

Kafka enables the building of streaming data pipelines from “source” to “sink” through the Kafka Connect API and the Kafka Streams API Logs unify batch and stream processing.

On Confluent Cloud for Apache Flink®, snapshot queries combine batch and stream processing to enable AI apps and agents to act on past and present data New private networking and security ...

Snapshot queries, a new feature of Confluent Cloud for Apache Flink, combines batch and stream processing capabilities in one place.