Confluent

MARCH 26, 2019

Apache-Kafka ® -based applications stand out for their ability to decouple producers and consumers using an event log as an intermediate layer. This article describes how to instrument Kafka-based applications with distributed tracing capabilities in order to make dataflows between event-based components more visible.

Kafka 111

Kafka Transportation Metadata Consulting 111

Confluent

FEBRUARY 6, 2019

Uber expanded Michelangelo “to serve any kind of Python model from any source to support other Machine Learning and Deep Learning frameworks like PyTorch and TensorFlow [instead of just using Spark for everything].”. It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way.

Machine Learning 107

Machine Learning Python Kafka Java 107

Rockset

MARCH 19, 2020

Intro In recent years, Kafka has become synonymous with “streaming,” and with features like Kafka Streams, KSQL, joins, and integrations into sinks like Elasticsearch and Druid, there are more ways than ever to build a real-time analytics application around streaming data in Kafka.

Kafka 40

Kafka Database Process SQL 40

Confluent

AUGUST 29, 2019

We know that Apache Kafka ® is great when you’re dealing with streams, allowing you to conveniently look at streams as tables. Stream processing engines like KSQL furthermore give you the ability to manipulate all of this fluently. The approach we’ll use works with any Kafka run though.

Kafka 55

Kafka Process Algorithm Cloud 55

Confluent

SEPTEMBER 20, 2019

As a distributed system for collecting, storing, and processing data at scale, Apache Kafka ® comes with its own deployment complexities. To simplify all of this, different providers have emerged to offer Apache Kafka as a managed service. How do you spot a true fully managed service for Apache Kafka?

Kafka 21

Kafka Management Cloud AWS 21