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Sean Owen – Director of Data Science @Cloudera Building machine learning models is all well and good, but how do they get productionized into a service? It's a long way from a Python script on a laptop, to a fault-tolerant system that learns continuously, serves thousands of queries per second, and scales to terabytes. The confederation of open source technologies we know as Hadoop now offers data scientists the raw materials from which to assemble an answer: the means to build models but also ingest data and serve queries, at scale. This short talk will introduce Oryx 2, a blueprint for building this type of service on Hadoop technologies. It will survey the problem and the standard technologies and ideas that Oryx 2 combines: Apache Spark, Kafka, HDFS, the lambda architecture, PMML, REST APIs. The talk will touch on a key use case for this architecture -- recommendation engines.

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At the end of day, the only thing that data scientists want is tabular data for their analysis. They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data that is being streamed at them from IoT devices and apps, and at the same time add structure to it so that data scientists can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds). Oh… and there are a lot of other data sources that you need to ingest, and the current providers of data are changing their structure. GoPro has massive amounts of heterogeneous data being streamed from their consumer devices and applications, and they have developed the concept of “dynamic DDL” to structure their streamed data on the fly using Spark Streaming, Kafka, HBase, Hive and S3. The idea is simple: Add structure (schema) to the data as soon as possible; allow the providers of the data to dictate the structure; and automatically create event-based and state-based tables (DDL) for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.

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At Databricks, we have a unique view into hundreds different companies using Apache Spark for development and production use-cases, from their support tickets and forum posts. Having seen so many different workflows and applications, some discernible patterns emerge when looking at common manageability, debugging, and visibility issues that our users run into. This talk will first show some representatives of these common issues. Then, we will show you what we have done and have been working on in Databricks to make Spark clusters easier to manage, monitor, and debug.

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Data integration is a really difficult problem. We know this because 80% of the time in every project is spent getting the data you want the way you want it. We know this because this problem remains challenging despite 40 years of attempts to solve it. All we want is a service that will be reliable, handle all kinds of data and integrate with all kinds of systems, be easy to manage and scale as our systems grow. Oh, and it should be super low latency too. Is it too much to ask? In this presentation, we’ll discuss the basic challenges of data integration and introduce few design and architecture patterns that are used to tackle these challenges. We will then explore how these patterns can be implemented using Apache Kafka. Difficult problems are difficult and we offer no silver bullets, but we will share pragmatic solutions that helped many organizations build fast, scalable and manageable data pipelines.

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Since April 2016, Spark-as-a-service has been available to researchers in Sweden from the Swedish ICT SICS Data Center at www.hops.site. Researchers work in an entirely UI-driven environment on a platform built with only open-source software. Spark applications can be either deployed as jobs (batch or streaming) or written and run directly from Apache Zeppelin. Spark applications are run within a project on a YARN cluster with the novel property that Spark applications are metered and charged to projects. Projects are also securely isolated from each other and include support for project-specific Kafka topics. That is, Kafka topics are protected from access by users that are not members of the project. In this talk we will discuss the challenges in building multi-tenant Spark streaming applications on YARN that are metered and easy-to-debug. We show how we use the ELK stack (Elasticsearch, Logstash, and Kibana) for logging and debugging running Spark streaming applications, how we use Graphana and Graphite for monitoring Spark streaming applications, and how users can debug and optimize terminated Spark Streaming jobs using Dr Elephant. We will also discuss the experiences of our users (over 120 users as of Sept 2016): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and our novel solutions for helping researchers debug and optimize Spark applications. To conclude, we will also give an overview on our course ID2223 on Large Scale Learning and Deep Learning, in which 60 students designed and ran SparkML applications on the platform.

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NOTE: This was converted to Powerpoint from Keynote. Slideshare does not play the embedded videos. You can download the powerpoint from slideshare and import it into keynote. The videos should work in the keynote. Abstract: In this presentation, we will describe the "Spark Kernel" which enables applications, such as end-user facing and interactive applications, to interface with Spark clusters. It provides a gateway to define and run Spark tasks and to collect results from a cluster without the friction associated with shipping jars and reading results from peripheral systems. Using the Spark Kernel as a proxy, applications can be hosted remotely from Spark.

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Today, there are several compliance use cases ‒ archiving, e-discovery, supervision and surveillance, to name a few ‒ that appear naturally suited as Hadoop workloads, but haven’t seen wide adoption. In this session, you’ll learn about common limitations, how Apache Spark helps and some new blueprints for modernizing this architecture and disrupt existing solutions. Additionally, we’ll review the rising role of Apache Spark in this ecosystem, leveraging machine learning and advanced analytics in a space that has traditionally been restricted to fairly rote reporting.

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Apache Kafka sits at the center of a technology ecosystem that can be a bit overwhelming to someone just getting started. Fortunately, Apache Kafka is also at the heart of an amazing community that is able and eager to help! So, if you are new, or relatively new, to Apache Kafka, welcome! I’d like to introduce you to the Kafka ecosystem, and present you with a plan for how to learn and be productive with it. I’d also like to introduce you to one of the most helpful and welcoming software communities I’ve ever encountered. I’ll take you through the basics of Kafka—the brokers, the partitions, the topics—and then on and up into the different APIs and tools that are available to work with it. Consider it a Kafka 101, if you will. We’ll stay at a high level, but we’ll cover a lot of ground, with an emphasis on where and how you can dig in deeper. I am still learning myself, so I will share with you what and who have helped me in my journey, and then I’ll invite you to continue that journey with me. It’s going to be a great adventure!

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ReversingLabs is on a mission to deliver threat intelligence to their users by providing complete visibility and insight into every destructive object. To deliver on their commitment, they migrated to Scylla to handle thousands of updates per second in their processing engines. In their talk, they will go over their requirements and show how they tuned the system to handle requests from their API frontend.

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Since mid-2016, Spark-as-a-Service has been available to researchers in Sweden from the Rise SICS ICE Data Center at www.hops.site. In this session, Dowling will discuss the challenges in building multi-tenant Spark structured streaming applications on YARN that are metered and easy-to-debug. The platform, called Hopsworks, is in an entirely UI-driven environment built with only open-source software. Learn how they use the ELK stack (Elasticsearch, Logstash and Kibana) for logging and debugging running Spark streaming applications; how they use Grafana and InfluxDB for monitoring Spark streaming applications; and, finally, how Apache Zeppelin can provide interactive visualizations and charts to end-users. This session will also show how Spark applications are run within a ‘project’ on a YARN cluster with the novel property that Spark applications are metered and charged to projects. Projects are securely isolated from each other and include support for project-specific Kafka topics. That is, Kafka topics are protected from access by users that are not members of the project. In addition, hear about the experiences of their users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and the novel solutions for helping researchers debug and optimize Spark applications.hear about the experiences of their users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and the novel solutions for helping researchers debug and optimize Spark applications.afka topics are protected from access by users that are not members of the project. We will also discuss the experiences of our users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and our novel solutions for helping researchers debug and optimize Spark applications.

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Was ist angesagt? (20)

Reactive dashboard’s using apache spark

Spark Summit EU talk by Ruben Pulido Behar Veliqi

Lambda architecture on Spark, Kafka for real-time large scale ML

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Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...

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Spark-Streaming-as-a-Service with Kafka and YARN: Spark Summit East talk by J...

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Bridging the gap of Relational to Hadoop using Sqoop @ Expedia

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Imagine how you are setting out to implement that awesome idea for a new application. In the back-end you enjoy the horizontal and vertical scalability offered by the Actor model, and its great support for building resilient systems through distribution and supervision hierarchies. In the front-end you love the declarative way of writing rich and interactive web apps that AngularJS gives you. In this presentation we bring these two together, demonstrating how little effort is needed to obtain a responsive user experience with fully consistent and persistent data storage on the server side. See also http://summercamp.trivento.nl/

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In this presentation you will learn how to leverage the features introduced in Akka Persistence: opt-in at-least-once delivery semantics between actors and the ability to recover application state after a crash. Both are implemented by storing immutable facts in a persisted append-only log. We will show you how to create persistent actors using command and event sourcing, replicate events with reliable communication, scale out and improve resilience with clustering.

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Twelve Factor apps are built for agility and rapid deployment. They enable continuous delivery and reduce the time and cost for new developers to join a project. At the same time, they are architected to exploit the principles of modern cloud platforms while permitting maximum portability between them. Finally, they can scale up without significant changes to tooling, architecture or development practices. In this talk, you’ll learn the principles and best practices espoused by the Twelve Factor app. We’ll discuss how to structure your code, manage dependencies, store configuration, run admin tasks, capture log files, and more. You’ll learn how modern Java deployments can benefit

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