This slide talk about more details about Docker and what is Docker Swarm mode.
There are also some examples in this slide, you can follow them to practice.
This slide talk about more details about Docker and what is Docker Swarm mode.
There are also some examples in this slide, you can follow them to practice.
Jamie Allen, Björn Antonsson and Patrik Nordwall discuss patterns of building Akka systems, including the Sentinel for handling failure above a supervisor, flow control and distributed workers. Patrik also built a template for Typesafe Activator v0.2.1 allowing you to try out this distributed workers pattern on your own machine.
Apache Kafka, and the Rise of Stream ProcessingGuozhang Wang
For a long time, a substantial portion of data processing that companies did ran as big batch jobs. But businesses operate in real-time and the software they run is catching up. Today, processing data in a streaming fashion becomes more and more popular in many companies over the more "traditional" way of batch-processing big data sets available as a whole.
Building Realtim Data Pipelines with Kafka Connect and Spark StreamingGuozhang Wang
Spark Streaming makes it easy to build scalable, robust stream processing applications — but only once you’ve made your data accessible to the framework. Spark Streaming solves the realtime data processing problem, but to build large scale data pipeline we need to combine it with another tool that addresses data integration challenges. The Apache Kafka project recently introduced a new tool, Kafka Connect, to make data import/export to and from Kafka easier.
Building a Replicated Logging System with Apache KafkaGuozhang Wang
Apache Kafka is a scalable publish-subscribe messaging system
with its core architecture as a distributed commit log.
It was originally built as its centralized event
pipelining platform for online data integration tasks. Over
the past years developing and operating Kafka, we extend
its log-structured architecture as a replicated logging backbone
for much wider application scopes in the distributed
environment. I am going to talk about our design
and engineering experience to replicate Kafka logs for various
distributed data-driven systems, including
source-of-truth data storage and stream processing.
Building Stream Infrastructure across Multiple Data Centers with Apache KafkaGuozhang Wang
To manage the ever-increasing volume and velocity of data within your company, you have successfully made the transition from single machines and one-off solutions to large distributed stream infrastructures in your data center, powered by Apache Kafka. But what if one data center is not enough? I will describe building resilient data pipelines with Apache Kafka that span multiple data centers and points of presence, and provide an overview of best practices and common patterns while covering key areas such as architecture guidelines, data replication, and mirroring as well as disaster scenarios and failure handling.
Building Reactive Distributed Systems For Streaming Big Data, Analytics & Mac...Helena Edelson
Building Self Healing, Intelligent Platforms, systems that learn, multi-datacenter, removing human intervention with ML. Reactive Summit 2016 @helenaedelson
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
In the last year, we've gone from millions of pieces of data to billions of pieces of data. I will speak on a solution for scaling up and about the challenges presented. Also covered will be the future of data at Qihoo 360 with MongoDB.