IBM Journey to AI blog

NOVEMBER 21, 2023

Apache Kafka is a well-known open-source event store and stream processing platform and has grown to become the de facto standard for data streaming. Apache Kafka transfers data without validating the information in the messages. Kafka does not examine the metadata of your messages.

Apache Kafka 106

Apache Kafka Clustering Data Quality Data Governance 106

The MLOps Blog

FEBRUARY 5, 2023

What comes into your mind when you hear Streaming Data? May be data generated through video streaming platforms like YouTube, but this is not the only thing which qualifies as streaming data. In this article, we will go through the basics of streaming data, what it is, and how it differs from traditional data.

Machine Learning 73

Machine Learning Machine Learning Data Pipeline Apache Kafka 73

IBM Journey to AI blog

NOVEMBER 29, 2023

By leveraging AI for real-time event processing, businesses can connect the dots between disparate events to detect and respond to new trends, threats and opportunities. AI and event processing: a two-way street An event-driven architecture is essential for accelerating the speed of business.

Artificial Intelligence 92

Artificial Intelligence Artificial Intelligence Apache Kafka AI 92

AWS Machine Learning Blog

JANUARY 6, 2023

In this post, we explain how TR used Amazon Personalize to build a scalable, multi-tenanted recommender system that provides the best product subscription plans and associated pricing to their customers. TR’s customers span across the financial, risk, legal, tax, accounting, and media markets. Solution architecture.

AWS 66

AWS Data Warehouse ML ML 66

IBM Journey to AI blog

NOVEMBER 9, 2023

Apache Kafka is a high-performance, highly scalable event streaming platform. To unlock Kafka’s full potential, you need to carefully consider the design of your application. It’s all too easy to write Kafka applications that perform poorly or eventually hit a scalability brick wall.

Apache Kafka 68

Apache Kafka Algorithm Clustering 68

phData

DECEMBER 4, 2023

This blog was originally written by Erik Hyrkas and updated for 2024 by Justin Delisi This isn’t meant to be a technical how-to guide — most of those details are readily available via a quick Google search — but rather an opinionated review of key processes and potential approaches. Want to save this guide for later?

Clustering 98

Clustering Database SQL Data Pipeline 98