This interactive whiteboard presentation discusses use cases leveraging the Apache Kafka® open source ecosystem as a streaming platform to process IoT data. The session shows architectural alternatives of how devices like cars, machines or mobile devices connect to Apache Kafka via IoT standards like MQTT or OPC-UA.
Apache Kafka® is used by thousands of companies across the world but, how difficult is it to operate? Which parameters do you need to set? What can go wrong? This online talk is based on real-world experience of Kafka deployments and explores a collection of common mistakes that are made when running Kafka in production and some best practices to avoid them.
The next generation architecture for exploring and visualizing event-driven data in real-time requires the right technology. Microservices deliver significant deployment and development agility, but raise questions of how data will move between services and how it will be analyzed.
Real-time data has value. But how do you quantify that value in order to create a business case for becoming data, or event driven? This talk explores why valuing Kafka is important - but covers some of the problems in quantifying the value of a data infrastructure platform.
Microservices, events, containers, and orchestrators are dominating our vernacular today. As operations teams adapt to support these technologies in production, cloud-native platforms like Pivotal Cloud Foundry and Kubernetes have quickly risen to serve as force multipliers of automation, productivity and value.
Apache Kafka® has become the de facto technology for real-time event streaming. Confluent Platform, developed by the creators of Apache Kafka, is an event-streaming platform that enables the ingest and processing of massive amounts of data in real time.
Join Tom Green, Solution Engineer at Confluent for this online talk covering KSQL. Confluent KSQL is the streaming SQL engine that enables real-time data processing against Apache Kafka®. It provides an easy-to-use, yet powerful interactive SQL interface for stream processing on Kafka, without the need to write code in a programming language such as Java or Python.
In private and public clouds, stream analytics commonly means stateless processing systems organized around Apache Kafka® or a similar distributed log service. GCP took a somewhat different tack, with Cloud Pub/Sub, Dataflow, and BigQuery, distributing the responsibility for processing among ingestion, processing and database technologies.
Open Banking regulations compel the UK’s largest banks and building societies to enable their customers to share personal information with other regulated companies securely. At the same time, customers’ demands are evolving, expecting seamless, real-time banking experiences.
In this all too fabulous talk with Ticketmaster, we will be addressing the wonderful and new wonders of KSQL vs. KStreams. If you are new-ish to Apache Kafka® you may ask yourself, “What is a large Apache Kafka deployment?
Companies new and old are all recognizing the importance of a low-latency, scalable, fault-tolerant data backbone, in the form of the Apache Kafka® streaming platform. With Apache Kafka, developers can integrate multiple sources and systems, which enables low latency analytics, event-driven architectures and the population of multiple downstream systems.
Express Scripts is reimagining its data architecture to bring best-in-class user experience and provide the foundation of next-generation applications. The challenge lies in the ability to efficiently and cost-effectively access the ever-increasing amount of data.