Tim Spann of Hortonworks discusses Apache MiniFi, Nifi, and deep learning tools and techniques with Chariot’s Ken Rimple after the IoT Fusion conference. This screencast includes some live demos of the tooling available via Apache and Hortonworks open source developers. Tim Spann can be reached via Twitter as @PaasDev or via Hortonworks.
In this talk we present a business use case where Capital One needs to process customer activities real-time and react to events appropriately as needed. We then present our experience in building a real-time analytics application that serves the business using a set of open source software frameworks with Apache Flink at its core for real-time stream processing engine.
Kafka Streams represents a new design point in the stream processing space. Where most frameworks provide a service for running stream processing applications, Kafka Streams emphasizes low-overhead development that feels more like developing any other application.
In this talk, Benjamin Hindman (one of the original creators of the Apache Mesos project) will discuss Apache Mesos and how it can be used to more easily build and run distributed systems.
I will discuss the experience at LinkedIn and elsewhere moving from batch-oriented ETL to real-time streams using Apache Kafka. I’ll talk about how the design and implementation of Kafka was driven by this goal of acting as a real-time platform for event data.
In this talk we will talk about the design of our Mesos framework and the scheduler. We will focus on the following aspects of the scheduler – Bin packing algorithms, Scaling in and out of clusters, fault tolerance of processes via reconciliation and processing life cycle events, multi-geography/cross data center redundancy.
In this talk, Benjamin Hindman (one of the original creators of the Apache Mesos project) will discuss Apache Mesos and how it can be used to more easily build and run distributed systems.
In this talk we will talk about the design of Netflix’s Mesos framework and the scheduler. We will focus on the following aspects of the scheduler – Bin packing algorithms, scaling in and out of clusters, fault tolerance of processes via reconciliation and processing life cycle events, multi-geography/cross data center redundancy.
This talk presents Apache Spark, Spark Streaming, Apache Kafka, Apache Cassandra and Akka as supporting Lambda architecture in the context of a fault tolerant, streaming big data pipeline. We will walk through the Fault Tolerance story with these technologies to build applications, and how to easily implement and integrate them in a Scala Akka application for real-time delivery of meaning at high velocity, in highly distributed and concurrent environments.
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As always, social bookmarking on delicious.com for the devnews delicious.com/developernews/10 However, here are the links… Links: EIP ‘Loan Broker’ Reference Implementation (Part 1) | SpringSource Team Blog Guillaume Nodet’s blog: Spring-DM, Aries Blueprint and custom namespaces Ehcache 2.0 Offers Grails Integration & Scaling Without Code Changes | IndicThreads Why doesn’t (JPA, JMS, JTA, EJB, JSF, … Read More
