Get emerging insights on innovative technoaight to your inbox. At ai Cloud we are building a cloud agnostic, open source next generation CloudFoundry/Heroku-like PaaS, Pipeline, while running several big data workloads natively on Kubernetes. Apache Kafka is one of the cloud native workloads we support out-of-the-box, alongside Apache Spark and Apache Zeppelin. If you’re interested in
Storage Reimagined for a Streaming World Pravega is about a new storage abstraction — a stream — for continuously generated and unbounded data. A Pravega stream stores unbounded parallel sequences of bytes in a durable, elastic and consistent manner while providing unbeatable perfems such as Kafka and Pulsar hav
Introduction Reading and writing is the most basic functionality that Pravega offers. Applications ingest data by writing to one or more Pravega streams and consume data by reading data from one or more streams. To implement applications correctly with Pravega, however, it is crucial that the developer is aware of some additional functionality that complements the core write and read calls. For ex
At Uber, we are seeing an inemand for Kafka at-least-once delivery (asks=all). So far, we are running a dedicated at-least-once Kafka cluster with special settings. With a very low workload, the dedicated at-least-once cluster has been working well for more than a year. When trying to allow at-least-once producing on the regular Kafka clusters, the producing perfain conc
by Brent Rabowsky, Solutions Architect & Itzik Paz, Solutions Architect, itectures become more popular, customers need a framework of patterns to help them identify how they can leverage ithout managing servers or operating systems. This session describes re-ble serverless patterns while considering costs. For each pattern, we provide operati
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
