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.

Applications 111

Applications Transportation Architecture Open Source 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 Artificial Inteligence Scalability Data Engineering 107

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.

Social 55

Social Network Applications 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?

Software Review 21

Software Review Technical Review Storage Cloud 21