Hadoop UK Strata Panel Discussion

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Matt will take a look at the EMR interface, and explore what additional value EMR provides for creating and managing Hadoop clusters. Matt is a principal technologist in the technical IT training team at QA. He is a Microsoft Certified Trainer and an authorized Amazon trainer, focusing on creating and delivering courses about cloud services, service-oriented architectures and enterprise application integration.

Matthew Bishop - A Quick Introduction to AWS Elastic MapReduce

huguk

As Hadoop became mainstream, the need to simplify and speed up analytics processes grew rapidly. Data wrangling emerged as a necessary step in any analytical pipeline, and is often considered to be its crux, taking as much as 80% of an analyst's time. In this presentation we will discuss how data wrangling solutions can be leveraged to streamline, strengthen and improve data analytics initiatives on Hadoop, including use cases from Trifacta customers. Bio: Olivier is EMEA Solutions Lead at Trifacta. He has 7 years experience in analytics with prior roles as technical lead for business analytics at Splunk and quantitative analyst at Accenture and Aon.

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huguk

At Google Cloud Platform, we're combining the Apache Spark and Hadoop ecosystem with our software and hardware innovations. We want to make these awesome tools easier, faster, and more cost-effective, from 3 to 30,000 cores. This presentation will showcase how Google Cloud Platform is innovating with the goal of bringing the Hadoop ecosystem to everyone. Bio: "I love data because it surrounds us - everything is data. I also love open source software, because it shows what is possible when people come together to solve common problems with technology. While they are awesome on their own, I am passionate about combining the power of open source software with the potential unlimited uses of data. That's why I joined Google. I am a product manager for Google Cloud Platform and manage Cloud Dataproc and Apache Beam (incubating). I've previously spent time hanging out at Disney and Amazon. Beyond Google, love data, amateur radio, Disneyland, photography, running and Legos."

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huguk

Using Big Data techniques to query and store OpenStreetMap data. Stephen Knox...

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Apache Hadoop is being adopted across all industries for its ability to store and process an abundance of new types of data in a modern data architecture. But this “Any Data” architecture presents a challenge when organizations must reconcile data management realities and as they bring existing and new data from disparate platforms under management. Apache Atlas proposes to provide governance capabilities in Hadoop that use both a prescriptive and forensic models enriched by business taxonomical metadata. It is designed to exchange metadata with other tools and processes within and outside of the Hadoop stack, thereby enabling platform-agnostic governance.

Apache Atlas. Data Governance for Hadoop. Strata London 2015

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Big organisations have a wealth of rich customer data which opens up huge new opportunities. However, they have the challenge of how to extract value from this data while protecting the privacy of their individual customers. He will talk about the risks organisations face, and what they should do about it. He will survey the techniques which can be used to make data safe for analysis, and talk briefly about how they are solving this problem at Privitar.

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IBM is developing the Watson Ecosystem to leverage its Developer Cloud, APIs, Content Store and Talent Hub. This is part of IBM's recent announcement of the $1B investment in Watson as a new business unit including Silicon Alley NYC headquarters. For the first time, IBM will open up Watson as a development platform in the Cloud to spur innovation and fuel a new ecosystem of entrepreneurial software app providers who will bring forward a new generation of applications infused with Watson's cognitive computing intelligence.

Intelligence Augmented vs Artificial Intelligence. Alex Flamant, IBM Watson

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Stephen Taylor is the community manager for Ether Camp. They provide an analysis tool for the Ethereum blockchain, ‘Block Explorer’ and also an ‘Intergrated Development Environment’ (I.D.E) that empowers developers to build, test and deploy applications in a sandbox environment. This November they are launching their second annual hackathon, hack.ether.camp which is aiming to deliver a more sustained approach to the hackathon ideology, by utilising blockchain technology.

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The Briefing Room with Robin Bloor and HP Vertica Live Webcast on August 26, 2014 Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=3dd6d1b068fe395f665c75adb682ac41 Hadoop has long passed the point of being a nascent technology, but many users have found that when left to its own devices, Hadoop can be a one trick pony. To get the most out of Hadoop, organizations need a flexible platform that empowers analysts and data managers with a complete set of information lifecycle management and analytics tools without a performance tradeoff. Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he outlines Hadoop’s role in a big data architecture. He’ll be briefed by Walt Maguire of HP Vertica, who will showcase his company’s big data solutions, including HAVEn and the HP Big Data Platform. He will demonstrate how HP Vertica acts as a complement to Hadoop, and how the combination of the two provides a versatile and highly performant solution. Visit InsideAnlaysis.com for more information.

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Leading entrepreneurial outfits are disrupting traditional companies by rapidly building data-driven apps. They employ top software talent and effectively use storage, analytics and app-dev tools from various open source ecosystems. We show how companies of all sizes are now transforming into data-driven enterprises using their existing software skill sets by leveraging a single platform that combines flexible data storage systems, advanced analytics and agile app-dev PaaS frameworks, all available now in open source forums.

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Explore the applications of BIG Data & Hadoop in Social Media via Skillspeed. BIG Data & Hadoop in Social Media is a key differentiator, especially in terms of generating memorable customer experiences. Herein, we discuss how leading social networks such as Facebook, Twitter, Pinterest, LinkedIN, Instagram & Stumble Upon utilize Hadoop. To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com

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Who is contributing to the Hadoop ecosystem, what are they contributing, and why? Who are the vendors that are supplying Hadoop-related products and services and what do they want from Hadoop? How is the expanding ecosystem benefiting or damaging the Apache Hadoop project? What are the emerging alternatives to Hadoop and what chance do they have? In this session, the 451 Group will seek to answer these questions based on their latest research and present their perspective of where Hadoop fits in the total data management landscape.

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Pivotal is a trusted partner for IT innovation and transformation. From the technology, to the people, to the way people interact with technology, Pivotal is transforming how the world builds software. At Strata NYC 2015, Pivotal, announced it will Supercharge the Hadoop Ecosystem by contributing the HAWQ advanced SQL on Hadoop analytics and MADlib machine learning technologies to The Apache Software Foundation.

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Join Cloudian, Hortonworks and 451 Research for a panel-style Q&A discussion about the latest trends and technology innovations in Big Data and Analytics. Matt Aslett, Data Platforms and Analytics Research Director at 451 Research, John Kreisa, Vice President of Strategic Marketing at Hortonworks, and Paul Turner, Chief Marketing Officer at Cloudian, will answer your toughest questions about data storage, data analytics, log data, sensor data and the Internet of Things. Bring your questions or just come and listen!

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As you walk into your office on Monday morning, before you've even had a chance to grab a cup of coffee, your CEO asks to see you. He's worried: both customer churn and fraudulent transactions have increased over the past 6 months. As Data Manager, you have 6 months to solve this problem. As Data Manager, you know the challenges ahead: - Multitudes of technology choices to make - Building a team and solving the skill-set disconnect - Data can be deceiving... - Figuring out what the successful data product must be Florian works in the “data” field since 01’, back when it was not yet big. He worked in successful startups in search engine, advertising, and gaming industries, holding various data or CTO roles. He started Dataiku in 2013, his first venture as a CEO, with the goal of alleviating the daily pains encountered by data teams all around.

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As you walk into your office on Monday morning, before you've even had a chance to grab a cup of coffee, your CEO asks to see you. He's worried: both customer churn and fraudulent transactions have increased over the past 6 months. As Data Manager, you have 6 months to solve that. As Data Manager, you know the challenges ahead: Multitudes of technology choices to make Building a team and solving the skill-set disconnect Data can be deceiving... Figuring out what the successful data product must be The goal of this talk is to provide some perspective to these topics Florian works in the “data” field since 01’, back when it was not yet big. He worked in successful startups in search engine, advertising and gaming industries, holding various data or CTO’s role. He started Dataiku in 2013, his first venture as a CEO, with the goal of alleviating the daily pains from the data enthusiasts and let them express their creativity.

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Major changes in industries have been brought about by the emergence of data-driven discoveries and applications. Many organizations are bringing together their data, and looking to drive change. But the ability to generate new insights in real time from a massive sets of data is still far from commonplace. At this event, data technology experts and data scientists from Pivotal provided the latest business perspective on how data science and engineering can be used to accelerate the generation of new insights. For information about upcoming Pivotal events, please visit: http://pivotal.io/news-events/#events

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In this talk about Apache Flink we will touch on three main things, an introductory look at Flink, a look under the hood and a demo. * In the introduction we will briefly look at the history of Flink and then go on to the API and different use cases. Here we will also see how it can be deployed in practice and what some of the pitfalls in a cluster setting can be. * In the second section we will look at the streaming execution engine that lies at the heart of Flink. Here we will see what makes it tick and also what distinguishes it from other approaches, such as the mini-batch execution model. Ufuk Celebi - PMC member at Apache Flink and co-founder and software engineer at data Artisans * In the final section we will see a live demo of a fault-tolerant streaming job that performs analysis of the wikipedia edit-stream.

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huguk

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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Martin Oberhuber and Eliano Marques, Senior Data Scientists @Think Big International In this talk Think Big International Lead Data Scientists will discuss the options that exist today for engineering and data science teams aiming to use big data patterns to solve new business problems. With the enterprise adoption of the Hadoop ecosystem and the emerging momentum of open source projects like Spark it is becoming mandatory to have an approach that solves for business results but remains flexible to adapt and change with the open source market.

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This talk will cover the design and implementation decisions that have been key to the success of Apache Spark over other competing cluster computing frameworks. It will be delving into the whitepaper behind Spark and cover the design of Spark RDDs, the abstraction enables the Spark execution engine to be extended to support a wide variety of use cases: Spark SQL, Spark Streaming, MLib and GraphX. RDDs allow Spark to outperform existing models by up to 100x in multi-pass analytics.

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Technical developments in the area of data warehousing have allowed companies to push their analysis a step further and, therefore, allowed data scientists to deliver more value to business areas. In that session, we will focus on the case of performance marketing at King and demonstrate how we use Hadoop capabilities to exploit user-level data efficiently. That approach results in obtaining a more holistic view in a return-on-investment analysis of TV advertisement.

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By Kevin Schmidt (Head of Data Science at Mind Candy) Luis Vicente (Senior Data Engineer at Mind Candy) For mobile games, constant tweaks are the difference between success and failure. Data and analytics have to be available in real-time, but calculating, for example, uniqueness or newness of a data point requires a list of seen data points - both memory intensive and tricky when using real-time stream processing like Spark Streaming. Probabilistic data structures allow approximation of these properties with a fixed memory representation, and are very well suited for this kind of stream processing. Getting from the theory of approximation to a useful metric at a low error rate even for many millions of users is another story. In our talk we will look at practical ways of achieving this: which approximation we used for selection of useful metrics, why we picked a specific probabilistic data structure, how we stored it in Cassandra as a time series and how we implemented it in Spark Streaming.

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The task of “data profiling”—assessing the overall content and quality of a data set—is a core aspect of the analytic experience. Traditionally, profiling was a fairly cut-and-dried task: load the raw numbers into a stat package, run some basic descriptive statistics, and report the output in a summary file or perhaps a simple data visualization. However, data volumes can be so large today that traditional tools and methods for computing descriptive statistics become intractable; even with scalable infrastructure like Hadoop, aggressive optimization and statistical approximation techniques must be used. In this talk Sean will cover technical challenges in keeping data profiling agile in the Big Data era. He will discuss both research results and real-world best practices used by analysts in the field, including methods for sampling, summarizing and sketching data, and the pros and cons of using these various approaches. Sean is Trifacta’s Chief Technical Officer. He completed his Ph.D. at Stanford University, where his research focused on user interfaces for database systems. At Stanford, Sean led development of new tools for data transformation and discovery, such as Data Wrangler. He previously worked as a data analyst at Citadel Investment Group.

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