Streaming SQL Foundations: Why I ❤ Streams+Tables
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2rtxaMm. Tyler Akidau explores the relationship between the Beam Model and stream & table theory. He explains what is required to provide robust stream processing support in SQL and discusses concrete efforts that have been made in this area by the Apache Beam, Calcite, and Flink communities, compare to other off
Kafka on Kubernetes - using etcd
The content of this page hasn't been updated for years and might refer to discontinued products and projects. At Banzai 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 Ap
Pravega – A Reliable Stream Storage System
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 performance and automatically tiering data to scale-out storage. Distributed messaging systems such as Kafka and Pulsar hav
Streams in and out of Pravega
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
Stateful processing with Apache Beam
Beam lets you process unbounded, out-of-order, global-scale data with portable high-level pipelines. Stateful processing is a new feature of the Beam model that expands the capabilities of Beam, unlocking new use cases and new efficiencies. In this post, I will guide you through stateful processing in Beam: how it works, how it fits in with the other features of the Beam model, what you might use
Blog Posts | August 6, 2024 - November 10, 2023 | @lightbend
Webinar: Akka 24.05 release highlights Tyler Jewell, CEO, Jonas Boner, founder and CTO, and Michael Nash, CISO, delve into the value and power of these new features and enhancements. Q&A to follow. Lightbend aims to democratize distributed systems for developers Tyler Jewell, CEO of Lightbend, talks about some of the key challenges developers are up against with distributed systems and how Lightbe
Improving Kafka at-least-once performance at Uber
At Uber, we are seeing an increasing demand 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 performance was the main conc
Serverless Architecture and Best Practices
by Brent Rabowsky, Solutions Architect & Itzik Paz, Solutions Architect, AWS As serverless architectures become more popular, customers need a framework of patterns to help them identify how they can leverage AWS to deploy their workloads without managing servers or operating systems. This session describes re-usable serverless patterns while considering costs. For each pattern, we provide operati
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Instaclustr Kafka Meetup Sydney Presentation
Why is My Stream Processing Job Slow? with Xavier Leaute
RealTime Recommendations @Netflix - Spark
Kapacitor - Real Time Data Processing Engine
Stream-Native Processing with Pulsar Functions
Introducing Low-latency Continuous Processing Mode in Structured Streaming in Apache Spark 2.3
Stream-Relational Processing Platforms
Introducing Apache Spark 2.3
Monitoring Apache Kafka
10 Principles for Streaming Services | Confluent
How To Size Your Apache Flink Cluster Back-of-the-Envelope Calculation
Curious Case of the Broken Benchmark: Revisiting Flink vs Databricks