Confluent’s Kafka story. Over a decade ago, organizations heavily relied on batch data for analytical workloads. The approach worked, but it meant understanding and driving value only from ...

This lets companies migrate and connect data in real-time to services including Amazon Simple Storage Service (S3), Amazon Redshift, Amazon DynamoDB, Amazon Lambda, and more with 120+ pre ...

streaming data processing software typically analyzes the data incrementally, and performs real-time aggregation and correlation, filtering, or sampling. The stream is often stored as well, so ...

Apache Kafka and other real-time streaming platforms are important to the technology infrastructure as they collect the multiple CDC data streams and move the data to one or more targets. And as ...

In this eBook from AWS and Confluent, discover when and how to deploy Apache Kafka on your enterprise to harness your data, respond in real-time, and make faster, more informed decisions. This ...

CDC: This technology uses log-based extraction to create new streams and perform in-stream analytics. Real-time: Streaming happens in real-time windows on real-time data, one record at a time. By ...

Originally developed at LinkedIn, Apache Kafka is one of the most mature platforms for event streaming. Kafka is used for high-performance data pipelines, streaming analytics, data integration ...

Confluent, founded by the creators of Apache™ Kafka™, today announced the general availability of open source Confluent Platform 3.0. The new release introduces Kafka Streams, a powerful ...

Customers use Apache Kafka to capture and analyze real-time data streams from a range of sources, including IoT devices, website clickstreams, financial systems, and database logs.

Kafka Streams is suited for processing events in Kafka but at LinkedIn, we need an ability to process events from/to different systems such as Azure EventHubs, AWS Kinesis etc.