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This document appears to contain technical terms related to data processing and distributed systems. It includes references to components that handle data queues, acknowledgments, data streaming, response processing and data nodes, as well as packets. In summary, it seems to outline key parts of a system for distributed data transmission and processing.

dataQueue ackQueue DataStreamer ResponseProcessor datanode packet

Keynote talk at the International Conference on Supercoming 2009, at IBM Yorktown in New York. This is a major update of a talk first given in New Zealand last January. The abstract follows. The past decade has seen increasingly ambitious and successful methods for outsourcing computing. Approaches such as utility computing, on-demand computing, grid computing, software as a service, and cloud computing all seek to free computer applications from the limiting confines of a single computer. Software that thus runs "outside the box" can be more powerful (think Google, TeraGrid), dynamic (think Animoto, caBIG), and collaborative (think FaceBook, myExperiment). It can also be cheaper, due to economies of scale in hardware and software. The combination of new functionality and new economics inspires new applications, reduces barriers to entry for application providers, and in general disrupts the computing ecosystem. I discuss the new applications that outside-the-box computing enables, in both business and science, and the hardware and software architectures that make these new applications possible.

[OpenInfra Days Korea 2018] (Track 4) CloudEvents 소개 - 상호 운용 가능성을 극대화한 이벤트 데이...

OpenStack Korea Community

NYC* Tech Day - New Cassandra Drivers in DepthMichaël Figuière

Data Streaming Ecosystem Management at Booking.com

confluent

(Alex Mironov, Booking.com) Kafka Summit SF 2018 Since its original introduction at Booking.com, Apache Kafka and overall concept of real-time data streaming have come a long way from being a complicated novelty to a common tool, used by a multitude of internal users ranging in their importance from the ad-hoc consumers to business-critical services powering up our property search engine. Over the course of this talk we’ll dive deep into how a relatively small team of SREs is successfully managing a multi-cluster, multi-tenant setup of Kafka and its surrounding ecosystem capable of transporting millions of messages per day. We’ll discuss challenges they faced along their way while building this platform and take a close look not only at application but also at architectural-level decisions they made to overcome them. Surely, we will also review what kind of tooling and automation team is using to stay sane during the day and sleep well during the night.

Dataservices: Processing Big Data the Microservice Way

QAware GmbH

O'Reilly Software Architecture Conference 2018, New York (USA): Talk by Mario-Leander Reimer (@LeanderReimer, Principal Software Architect at QAware) Abstract: Big data processing, microservices, and cloud-native technology are a match made in computing heaven, enabling microservices to be used to build a flexible, scalable, and distributed system of loosely coupled data processing tasks, called data services. Mario-Leander Reimer explores key JEE technologies that can be used to build JEE-powered data services and walks you through implementing the individual data processing tasks of a simplified showcase application. You’ll then deploy and orchestrate the individual data services using OpenShift, illustrating the scalability of the overall processing pipeline. The context and content is taken from a real-world project for a major German car manufacturer, implementing a microservices-based processing pipeline that uses car-related event data (sensor data, traffic events, and other real-time data) for a traffic information management and route optimization system.