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NoSQL, Hadoop and MapReduce: Building a Modern Data Infrastructure that Works

Jeffrey Kelly, Wikibon; Joey Jablonski, Kitenga; Christopher Biow, 10gen; Ron Bodkin, Think Big Analytics; John Akred, SVDS
In a whirlwind of big data tools like MapReduce, NoSQL, Hadoop, and their cousins and brothers, it’s difficult to understand the stack you need to make your data as useful as possible. How do you decide which tools to use, and once you do decide, how do you make the jump?

Join this roundtable led by big data infrastructure experts to:
*Understand the ingredients of a modern data infrastructure
*Learn how to assess your needs
*Make a blueprint for building a modern data architecture that works for you
Aug 21 2013
60 mins
NoSQL, Hadoop and MapReduce: Building a Modern Data Infrastructure that Works
hadoop mapreduce nosql
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  • My Favourite Pie (chart): Simple Rules for Clear and Attractive Visuals Jul 15 2015 9:00 am UTC 45 mins
    Markus Ehrenmueller, Business Intelligence Architect, Runtastic
    Do you want to deliver information in an effective and efficient way? Even when the attractiveness of a report is important, beauty is in the eye of the beholder. Join this session where Markus will show you some simple rules for helping end-users to understand the story their data is trying to tell.

    You will see how you can implement those rules with different tools from Microsoft’s BI stack – resulting in clear and concise information delivered through beautiful dashboards. You will also learn how to identify sub-optimal dashboards and what you can do to improve them.
  • Experiments in Deep Learning May 28 2015 6:00 pm UTC 60 mins
    Patrick Hall, Senior Associate Research Statistician Developer, SAS
    The human brain makes it look easy. What our eyes see, we decode immediately and effortlessly. But is it that simple? In truth, how we process images is staggeringly complex. Inspired in part by our remarkable neurons, deep learning is a fast-growing area in machine learning research that shows promising breakthroughs in speech, text and image recognition. It’s based on endowing a neural network with many hidden layers, enabling a computer to learn tasks, organize information and find patterns on its own.

    Recently, SAS took on a classical problem in machine learning research, the MNIST database, a data set containing thousands of handwritten digit images. Learn how we did – and what it reveals about the future of deep learning.
  • Ask, Measure, Learn May 28 2015 4:00 pm UTC 45 mins
    Lutz Finger, Director of Data Science and Data Engineering, LinkedIn; Author, "Ask, Measure, Learn"
    We do not want Big Data! We want the right data to answer the right questions!

    Data is changing our world. Predictions using massive data not only have improved many products. At the same time, they have, in some industries, disrupted business models and created new ones.

    What does an organization need to do to generate a new competitive advantage out of data? The answer might be surprising. “Change the state of mind.”

    Companies often do not need big data. They essentially want small and actionable advice. Some predictions will need big data to surface relevant information, but not all. The key to success for many companies, however, is to enable “data­driven” decision making. Lutz will discuss the steps he has used in starting and developing his own company (later sold to WPP), as well as how he leads LinkedIn’s data science team.

    A) Change the state of mind!
    Enable everyone in the company to ask “data driven” questions. Lutz will show how this is the hardest part of the on­going exercise, but why most businesses actually can achieve this with their current strategic abilities. Using examples we will learn what is the best way to formulate the “Ask”.

    B) What data?
    Data can be a source of disruption & innovation. Business models change because new data sources and enhanced computational power allows new services or improve old services. But which data to use? Domain knowledge is often more important than having “Big Data". Lutz will introduce a framework on how to think about data.

    C) How to build a Data Team?
    How can organizations build up data capabilities within your team. Contrary to the common discussion that a data scientist are not ‘hard to find’. Lutz will explain how every company can create a data science organization by just mixing the right skillets.
  • Statistical Computing-R & Visual Analytics: Data Science at the Speed of Thought May 28 2015 4:00 pm UTC 45 mins
    Bora Beran, PhD, Program Manager, Tableau
    Do you spend days on data science projects, only to struggle building them into presentations that management can understand? Do you spend more time exploring and understanding your data, before even beginning to write one line of a model? Do you have a hard time working through other departments to get to your data in the first place?

    Tableau is a visual reporting application that connects directly to R. It’s designed for you, the domain expert who understands the data. Its drag-and-drop interface allows you effortlessly connect to libraries and packages, import saved models, or write new ones directly into calculations, visualizing them in seconds.

    This webinar will show you how to:
    - Effortlessly connect your R scripts to a wide variety of data files and databases
    - Build interactive slideshows and presentations of your data in minutes
    - Use dashboards as a front end for R code, allowing viewers to intuitively interact with R models

    Join us to see how you can use drag and drop data visualization alongside R to speed up your data science projects and get them in front of more eyes, leading to smarter, data-driven business decisions.
  • Human-Centered Design and Data Science May 27 2015 3:00 pm UTC 45 mins
    Dean Malmgren, Partner and Data Scientist, Datascope Analytics
    When you hear someone say, “that is a nice infographic” or “check out this sweet dashboard,” many people infer that they are “well-designed.” Creating accessible (or for the cynical, “pretty”) content is only part of what makes good design powerful. The human-centered design process is geared toward solving specific problems. This process has been formalized in many ways (e.g., IDEO’s Human Centered Design, Marc Hassenzahl’s User Experience Design, or Braden Kowitz’s Story-Centered Design), but the basic idea is that you have to explore the breadth of the possible before you can isolate truly innovative ideas.

    In this talk, I'll share some lessons we've learned from the human-centered design process and how those lessons can be used by other data science practitioners.
  • Machine Learning - where to next? May 21 2015 1:00 pm UTC 45 mins
    Peter Morgan, CEO, Zepto Ventures
    We have all probably heard of machine learning by now. Some may even know that it is embedded in hundreds of everyday consumer and business products and services from search to image and speech recognition. In this talk Peter will give a brief overview of what machine learning is, where it came from and where it might take us in the near, medium and far term - two, five and ten years, respectively. He will cover the positive changes it will bring, plus the risks and issues that may result from the widespread adoption of this technology.
  • An Introduction to Machine Learning May 21 2015 11:00 am UTC 45 mins
    Dr. Nilesh Karnik, Chief Data Scientist, Aureus Analytics
    The term machine learning is frequently heard these days in connection with data science. In this talk, I’ll explain what machine learning is and how it is related to some other terms we hear in the context of data science such as predictive modelling or data mining. I’ll also cover key concepts related to machine learning such as supervised and unsupervised learning, and cover some of the commonly used machine learning approaches like regression, decision trees, clustering and artificial neural networks. Finally, with the help of an example, I’ll go over the process of using machine learning to solve a real life problem.
  • Augmenting MySQL with Big Data & NoSQL Options May 13 2015 4:00 pm UTC 60 mins
    David Murphy, Lead DBA , ObjectRocket by Rackspace
    How do you know which technology is right in todays database world, we will explore the range of technologies from MySQL/Percona/Maria to Xtra Cluster to Mongo to Redis and how you can fit all of these together to form a path way for your data. Not one technology fits everything but together you can use the right tool for the right job, and make the data flow seamlessly between them to range from short session data to longer term reporting data and everything between. We are at the new frontier in database lets work on a new toolset to prepare you for it.

    David is currently the Lead DBA for Object Rocket by Rackspace, where he works on sharding, mongo, tool building, very large-scale issues, and high performance mongo architecture. Before that he was the MySQL/NoSQL Architect for Electronic Arts, after working in segments from data centers to high performance advertising with skills ranging from programing to hardware/network design he brings a top down view into troubleshooting and tuning. He enjoys large-scale operational tool building, high performance OS and Database tuning, and extending mongo to improve operations team both large and small.
  • Hadoop and the Enterprise Data Warehouse, Simplified Recorded: Apr 23 2015 58 mins
    Tamara Dull, Dir. Emerging Technologies, SAS Best Practices; Tony Pagliarulo, Partner & Practice Lead, NewVantage Patners
    When Apache Hadoop hit the market eight years ago, it rattled the cages of traditional BI and data warehousing professionals. Many speculated whether Hadoop would replace existing infrastructures, complement them, or become just the latest technology fad.

    We now know Hadoop is not a fad and driving topic of discussion today is around how best to utilize Hadoop - even if you don't have big data.

    If you're a technically savvy business professional who is still trying to understand how big data - and Hadoop in specific - impacts the enterprise data game, this webinar is for you. We'll highlight six common ways Hadoop is being used to support and extend the enterprise data warehouse ecosystem, with or without "big" data.
  • Big Data Solutions: Simplifying Data with Hadoop Recorded: Apr 23 2015 49 mins
    Chandra Salem, Senior Enterprise Data Architect
    Data is now the driving force behind business success. Embrace it and the rewards can be an ever-increasing competitive advantage, significant revenue growth and bottom-line boosting profit margins.

    What is your big data challenge?

    · Is your existing infrastructure too small to go big?

    · Are you facing ever sky-rocketing capital expenditure just to support your infrastructure?

    · Is there an expertise gap in your business that you are struggling to fill?

    · Are you finding that your external options are limited by vendor lock-in?

    · Are you struggling to move on to the next phase and embrace innovative technologies, such as Hadoop, to capture the insights you need?

    Hear from Rackspace, Data Store experts to understand how to take your business to the next level in the data age.
  • Hadoop and Self-Service Analytics: Embracing Big Data Recorded: Apr 22 2015 49 mins
    Dustin Smith, Tableau
    The maturity of Hadoop as a technology framework suitable for organizations, large and small, to economically store and process vast amounts of data is no longer a prediction, but rather a reality every IT leader understands. But that doesn’t mean Hadoop is done disrupting the data and analytics landscape.

    Self-service analytics solutions capable of leveraging the massive processing and data discovery potential of distributed Hadoop clusters are ushering in a new era of data freedom for business users who are hungry to put data at the heart of their decision making process. With programming and query languages no longer a prerequisite skill for exploring Hadoop environments, organizations everywhere are waking up to the reality that even non-technical users can quickly and easily find insights in even the biggest of Hadoop data sets.

    Attend this webinar to hear how IT groups are adjusting to this new breed of bold and curious data user and learn:
    - How IT is shifting from data protector to data mentor
    - Why business users are so data hungry and so un-afraid of Big Data
    - What true self-service analytics can look like when paired with Hadoop
  • Explore Big Data Analytics with Amazon Redshift Recorded: Apr 22 2015 57 mins
    Rahul Pathak, Senior Product Manager, Amazon Redshift Ted Wasserman, Product Management & Development, Tableau Software
    Amazon Redshift enables customers to innovate quickly using its fully managed and immensely scalable data warehousing solution. Tableau’s ability to connect directly to Redshift and leverage its massive computing power means even the most non-technical business user can quickly discover business insights with easy to use drag and drop visual analytics against mammoth data sets. Join Amazon Web Services (AWS), Mixpo and Tableau Software, an AWS Technology Partner, to learn how customers are leveraging both Tableau and AWS to tackle big data exploration projects and recognize business benefits in record time.
  • Open source analytics in Enterprise-level environment: Opportunities& challenges Recorded: Apr 22 2015 28 mins
    Maciej Zawadziński, CEO, Piwik PRO
    Using real life examples drawn from his work with enterprise clients, Maciej Zawadziński, CEO of Piwik PRO, will outline possible uses of open source analytics platforms in Enterprise-level environments, also indicating potential opportunities and challenges. A must-attend for anyone serious about enterprise sector and curious about business applications of open source software!
  • Stream Processing 360 in the Hadoop Ecosystem: Use Cases and Best Practices Recorded: Apr 22 2015 40 mins
    Michael Hausenblas, Chief Data Engineer, MapR Technologies
    Processing data from social media streams and sensors devices alike, in real-time, is becoming increasingly prevalent and there are plenty open source solutions to choose from.

    In this Webinar we will help practitioners decide what to use for which use case by comparing three popular ASF open source stream processing frameworks: Apache Storm, Apache Samza and Apache Spark Streaming.Last but not least, we will discuss best practices and review real-world customer use cases from the stream processing domain.
  • Leveraging Hadoop - with or without on-premise infrastructure Recorded: Apr 22 2015 47 mins
    Lee Carter, VP EMEA, Bright Computing
    In this presentation, Lee Carter, VP EMEA at Bright Computing, will talk about Hadoop in the cloud, and how to leverage Hadoop without necessarily having to invest in on-premise infrastructure. Lee will explore the challenges faced when setting up, operating, using and managing Hadoop clusters. He will discuss user demand for Hadoop to be easier, and look at how the shortage of skilled Hadoop resources is impacting the industry. Lee will investigate the idea that better management tools can solve these challenges.
  • HDFS TDE: Native Encryption in Hadoop Recorded: Apr 22 2015 47 mins
    Alberto Romero, Senior Hadoop Technical Architect, Hortonworks
    HDFS Transparent Data Encryption has been added to HDFS 2.6, and it finally provides with a solution to data encryption on a higher level than the OS one whilst remaining native and transparent to Hadoop. It aims cover the gap that existed for privacy and security regulations that many industries require, without having to introduce a third-party solution into the mix. This way, having encryption at HDFS level gives an optimal context for policy definition that is relevant to the industry, while remaining transparent to the applications running on Hadoop.

    Join this webinar to learn:

    -where HDFS Transparent Encryption sits within the Hadoop security framework
    - an introduction to the technical details including how to create Encryption Keys and Encryption Zones
    - Interaction with the Apache Key Management System (KMS) and the encryption/decryption data flow
    - Future work in the space of Hadoop security in general, and encryption in particular
  • Developing Recommendation Systems Using SAS Recorded: Apr 21 2015 28 mins
    Wayne Thompson, Ph.D Manager, Data Sciences Technologies, at SAS
    What if you could analyze customer behavior and make real-time recommendations while they’re shopping? You can - with the help of a recommendation system.

    From investment choices to movie choices, a recommendation system automatically suggests additional products or services that enhance the initial offer the customer selected.

    This webinar will discuss examples and case studies of recommendation systems, including how to:
    • Make it easier and more likely for customers to buy.
    • Reduce customer uncertainty associated with the purchase of unfamiliar products.
    •Increase retention and loyalty.
    •Integrate recommendations into all customer touch points.
    Plus, we’ll demonstrate how SAS® In-Memory Statistics for Hadoop can help you quickly build accurate recommendation systems in a single, interactive programming environment.
  • Interactive In-Memory Statistics for Hadoop Recorded: Apr 21 2015 47 mins
    Sascha Schubert, Marketing Director at SAS and Mike Ames, Director, Product Management, SAS
    Once you can easily uncover insights from data stored in Hadoop – faster than your competitors – you’ll start noticing something. Complex problems aren’t quite as complex.

    That’s why SAS has expanded your ability to create value from structured and unstructured data in Hadoop. Rather than requiring organizations to move the data from Hadoop to a SAS® environment, we bring analytics to the Hadoop environment.

    View this webinar to:
    • Learn about SAS In-Memory Statistics for Hadoop, which provides a single interactive programming environment for analytics on Hadoop.
    • See demonstrations on supervised learning and recommendation systems.
  • Big Data Virtualization with SAS Federation Server Recorded: Apr 15 2015 24 mins
    Matthew Magne, Product Marketing Manager, Data Management, SAS and Johnny Starling, Senior Technical Architect, SAS
    Here’s the good news. While the number of data sources organizations face is rapidly increasing, the cost to store data has never been cheaper. This trend – as well as big data technologies like Hadoop – has led to more valuable insights for organizations.

    But it’s also led to IT departments overburdened with requests from the business users they support. So where do we go from here?

    View this webinar that discusses data federation, a type of data virtualization that:

    • Provides a centralized governance and security layer.
    • Speeds the creation of virtual data views.
    • Makes it easier for business users to access the data they need.

    We’ll also demonstrate technologies like big data virtualization, federated data-as-a-service and data masking.
  • Demystifying In-Memory Analytics Recorded: Apr 15 2015 38 mins
    Scott Chastain, Systems Engineer Manager, SAS and Tapan Patel, Product Marketing Manager, SAS
    SAS® In-Memory Analytics is built for speed. It enables you to access unstructured and structured data and tackle complex analytical computations blazingly fast. But it doesn’t end there.

    SAS In-Memory Analytics also delivers incremental value from big data so that you can find more lucrative opportunities, detect risks and improve targeted marketing.

    View this webinar to learn why SAS In-Memory Analytics can help you:

    • Tackle problems never before considered due to computing constraints.
    • Draw timely insights from Hadoop.
    • Perform self-service data discovery.
    • Run iterative and interactive analytics scenarios.
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  • Title: NoSQL, Hadoop and MapReduce: Building a Modern Data Infrastructure that Works
  • Live at: Aug 21 2013 6:00 pm
  • Presented by: Jeffrey Kelly, Wikibon; Joey Jablonski, Kitenga; Christopher Biow, 10gen; Ron Bodkin, Think Big Analytics; John Akred, SVDS
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