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Big Data Management

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  • RIDE Containerized Data Science IDE server For Enterprise
    RIDE Containerized Data Science IDE server For Enterprise Ali Marami, Data Science Advisor at R-Brain Recorded: Dec 14 2017 45 mins
    RIDE is an all-in-one, multi-user, multi-tenant, secure and scalable platform for developing and sharing Data Science and Analytics, Machine Learning (ML) and Artificial Intelligence (AI) solutions in R, Python and SQL.

    RIDE supports developing in notebooks, editor, RMarkdown, shiny app, Bokeh and other frameworks. Supported by R-Brain’s optimized kernels, R and Python 3 have full language support, IntelliSense, debugger and data view. Autocomplete and content assistant are available for SQL and Python 2 kernels. Spark (standalone) and Tesnsorflow images are also provided.

    Using Docker in managing workspaces, this platform provides an enhanced secure and stable development environment for users with a powerful admin control for controlling resources and level of access including memory usage, CPU usage, and Idle time.

    The latest stable version of IDE is always available for all users without any need of upgrading or additional DevOps work. R-Brain also delivers customized development environment for organizations who are able to set up their own Docker registry to use their customized images.

    The RIDE Platform is a turnkey solution that increases efficiency in your data science projects by enabling data science teams to work collaboratively without a need to switch between tools. Explore and visualize data, share analyses, all in one IDE with root access, connection to git repositories and databases.
  • Enterprise Analytics Journey, the IBM point of view for IBM Z customers
    Enterprise Analytics Journey, the IBM point of view for IBM Z customers Hélène Lyon, IBM, Distinguished Engineer, IBM Z Solutions Architect Recorded: Dec 14 2017 41 mins
    IT is a key player in the digital and cognitive transformation of business processes delivering solutions for improved business value with analytics. This session will step by step explain the journey to secure production while adopting new analytics technologies leveraging mainframe core business assets
  • From Big Data to AI: Building Machine Learning Applications
    From Big Data to AI: Building Machine Learning Applications Maloy Manna Data engineering PM, AXA Data Innovation Lab Recorded: Dec 12 2017 49 mins
    The newest buzzword after Big Data is AI. From Google search to Facebook messenger bots, AI is also everywhere.

    Machine learning has gone mainstream. Organizations are trying to build competitive advantage with AI and Big Data.

    But, what does it take to build Machine Learning applications? Beyond the unicorn data scientists and PhDs, how do you build on your big data architecture and apply Machine Learning to what you do?

    This talk will discuss technical options to implement machine learning on big data architectures and how to move forward.
  • Applying Machine Learning to unstructured files and data for research
    Applying Machine Learning to unstructured files and data for research Dr Tom Parsons, Research Director and Dr Stuart Bowe, Data Scientist from Spotlight Data Recorded: Dec 12 2017 45 mins
    Researchers generate huge amounts of valuable unstructured data and articles from research every day. The potential for this information is huge: cancer and pharmaceutical breakthroughs, advances in technology and cultural research that can improve the world we live in.

    This webinar discusses how text mining and Machine Learning can be used to make connections across this broad range of files and help drive innovation and research. We discuss using Kubernetes microservices to analyse the data and then applying Machine Learning and graph databases to simplify the reuse of the data.
  • From Data with Love: How the data economy is impacting the insurance sector
    From Data with Love: How the data economy is impacting the insurance sector JS Gourevitch, Luca Schnettler, Petra Wildermann, Anil Celik, Thomas Lethenborg Recorded: Nov 20 2017 60 mins
    The data economy and digital technologies are deeply transforming almost all areas of our lives. One of the most heavily transformed revolve around insurance and healthcare with a number of really interesting development possibly redefining the way we take care of ourselves and the way we consumer and use insurance as well.

    From harnessing the power of data to better help mental health patients, carers and medical personnel with their treatments to assessing the risk of developing broad range of illnesses and engaging better with users to propose them personalised healthy life plans to using big data and analytics to track down and prepare for epidemics to using data to better cover cars and drivers with car insurances and finally using social media data for insurers to better engage with customers, this webinar will propose a fascinating exploration of the opportunities, risks, new models supporting the digital transformation in banking.

    Moderated by Jean-Stéphane Gourévitch
    With:
    Luca Schnettler, CEO and Founder, HealthyHealth (UK)
    Petra Wildermann, Business Development Director, Metabiota (Switzerland)
    Anil Celik, Co-founder and CEO Urbanstats (US)
    Thomas Lethenborg, CEO, Monsenso (Denmark)
  • Associative Difference: Augmented intelligence across all your data
    Associative Difference: Augmented intelligence across all your data Robert Fleming, Vice-President of International Marketing and Global Campaigns, Qlik Recorded: Nov 15 2017 69 mins
    It's important to be aware of and respond to the key trends in Data Visualization now that we're heading into 2018.

    Join this video panel, where you will learn:

    -How to augment your intelligence across all your data, people and ideas
    -Uncover and take advantage of new data sources
    -Adjusting to the shift towards real-time enterprise
    -Deployment of new workloads like IoT towards the cloud
    -Why on-remise is still the predominant deployment model
    -Democratising access to data


    Panelists:
    •Anthony Deighton, Chief Technology Officer and Senior Vice President, Products, Qlik
    •Helena Schwenk, Big Data and Analytics Research Manager, IDC
    •Stephen Line, Regional Vice-President, North EMEA, Cloudera
    •Mike Prorock, CTO and Founder, mesur.io
    •Eduardo Di Monte, CEO of OYLO Trust Engineering, Cybersecurity Advisor for the Utilities sector.
    •Geertjan Woltjes, Chief Operations Officer, Quooker
  • Three Ways To Accelerate Your Data Lake Migration To Cloud
    Three Ways To Accelerate Your Data Lake Migration To Cloud Kelly Stirman, VP Strategy, Dremio Recorded: Oct 25 2017 45 mins
    Public cloud deployments have become irresistible in terms of flexibility, low barriers to entry, security, and developer friendliness. But the sheer inertia of traditional data lakes make them difficult to transition to cloud. In this talk we'll look at examples of how leading companies have made the transition using open source technologies and hybrid strategies.

    Instead of following a "lift and shift" strategy for moving data lake workloads to the cloud, there are new considerations unique to cloud that should be considered alongside traditional approaches related to compute (eg, GPU, FPGA), storage (object store vs. file store), integrations, and security.

    Viewers will take away techniques they can immediately apply to their own projects.
  • Becoming Data Driven: Building the Foundation of Digital Success
    Becoming Data Driven: Building the Foundation of Digital Success Nigel Turner Recorded: Oct 25 2017 54 mins
    Many organisations aspire to become digital, data driven enterprises. In these organisations data is viewed as a critical asset, both to generate new digitally based products and services, and to guide and improve business operations and decision making. But many companies are failing to live up to this aspiration. They struggle to develop and implement data strategies that align with, and help to deliver, new business strategies.


    This webinar will explore what becoming ‘data driven’ really means, examines some of the reasons why many organisations are failing to realise their ambitions, and propose ways of overcoming the challenges. Key to these is a strong emphasis on the increasingly critical importance of established data management disciplines, especially Data Governance, Data Quality and MDM, which all have a critical role to play in the digital business of the future.

    This session will explore:


    •What is a data driven organisation and how does it differ from a traditional company?
    •The main challenges of creating a data driven organisation
    •Building a data driven capability - the role of business and IT
    •The central importance of a business aligned Data Strategy and how to achieve it
    •Why a successful data strategy needs an integrated focus on Data Governance, Data Quality and MDM
  • Designing Data Lakes: Architecture options with open source tools
    Designing Data Lakes: Architecture options with open source tools Maloy Manna, PM Engineering, AXA Data Innovation Lab, Paris Recorded: Oct 25 2017 63 mins
    The concept of Data lakes evolved to address challenges and opportunities in managing big data.

    Organizations are investing massive amounts of time and money to upgrade existing data infrastructures and build data lakes whether on-premises or in the cloud.

    This talk will discuss architectures and design options to implement data lakes with open source tools. Also covered are challenges of upgrade & migration from existing data warehouses, metadata management, supporting self-service and managing production deployments.
  • Virtual Data Lake: A Reality
    Virtual Data Lake: A Reality Hélène Lyon, IBM, Distinguished Engineer, IBM Z Solutions Architect Recorded: Oct 25 2017 42 mins
    As an Enterprise customer, you are potentially using IBM Z in a hybrid cloud implementation. Let's understand how to benefit from cloud access to mainframe data without moving it outside z; thereby improving security, reducing integration challenges and answering your GDPR auditor's needs.

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