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  • Unlocking Self-Service Big Data Analytics on AWS
    Unlocking Self-Service Big Data Analytics on AWS Darmesh (Dash) Desai (Technology Evangelist at Qubole), Rahul Bhartia (Solutions Architect at Amazon Web Services) Recorded: Aug 12 2015 55 mins
    Storing, accessing, and analyzing large amounts of data from diverse sources and making it easily accessible to deliver actionable insights for users can be challenging for data driven organizations. The solution for customers is to optimize scaling and create a unified interface to simplify analysis. Qubole helps customers simplify their big data analytics with speed and scalability, while providing data analysts and scientists self-service access on the AWS Cloud. Join Qubole and AWS to discuss how Auto Scaling and Amazon EC2 Spot pricing can enable customers to efficiently turn data into insights. We'll talk about best practices for migrating from an on-premises Big Data architecture to the AWS Cloud.
  • Migrating Big Data to the Cloud - WANdisco, GigaOM and Qubole
    Migrating Big Data to the Cloud - WANdisco, GigaOM and Qubole William McKnight of GigaOM Research and experts from Qubole and WANdisco Recorded: Aug 12 2015 59 mins
    Join William McKnight of GigaOM Research and experts from Qubole and WANdisco It’s not complicated why it is that the cloud offers impressive advantages over on-premises computing for many applications. Scalability, lower cost, and a wide array of service offerings make cloud a very tempting tool for big data projects. What is often very complicated though is navigating the diverse landscape of cloud providers and services, analytics engines, scaling strategies, and cloud resource management tools to assemble a cloud solution that will actually deliver and perform impressively in practice. For anyone to successfully make the case in their organization for switching from on-premises to cloud or hybrid systems, the strategy of migrating to the cloud must be fully thought through. Otherwise, the endeavour is likely to succumb to one of the numerous organizational and technical pitfalls of major technology shifts like cloud migration, with the advantages of cloud never being realized. Fortunately, while cloud computing for big data projects can still feel like the frontier, the trail blazers, like Qubole, have learned the hardest lessons already and are now making it their business to make cloud migration as easy and widely available as possible. On September 8th, GigaOM Research Analyst William McKnight and experts from Qubole and WANdisco will hold a webinar to discuss the tools and strategies available for making cloud migration smooth and how organizations can actually deliver on the promises of lower cost and scalability attached to cloud and hybrid deployments. Qubole’s history of building Hadoop tools to make cloud big data projects straightforward for multiple types of users will join WANdisco’s specialty knowledge on distributed and cluster computing to offer attendees a comprehensive view of challenges like choosing the right cloud provider and selecting the right combination of cloud and on-premises solutions for a given project.
  • The Rise and Rise of Apache Spark
    The Rise and Rise of Apache Spark Matt Aslett (Research Director at 451 Research), Steve Gotlieb (Big Data Guru at Autodesk), Dharmesh (Dash) Desai (Technology Recorded: Aug 12 2015 61 mins
    Is Spark the answer to all questions posed for Big Data? In a few short years the Apache Spark in-memory data processing engine has risen from nowhere to become one of the most important projects in the Hadoop ecosystem and – for some – the anointed successor to MapReduce as Hadoop’s primary data processing engine.

    In this stimulating webinar, Matt Aslett, Research Director at 451 Research, will lead a discussion around the impact of the rise of Apache Spark on the Big Data ecosystem. He will be joined by Steve Gotlieb, Big Data Guru at Autodesk, who will dive into how developers and data scientists are using Spark Notebooks to prototype data transformations that can be deployed through an automated ETL pipeline, and delivered to data analysts to enable faster time-to-insights. And finally, Dharmesh (Dash) Desai, Technology Evangelist for Qubole, will round out the discussion with a look at the real value of a self-service analytics platform and how this value is realized when both business users and data team members have access to raw and aggregated data from a range of sources.
  • Will Overconfidence Kill Big Data? Review of the "State of DataOps Survey" Resul
    Will Overconfidence Kill Big Data? Review of the "State of DataOps Survey" Resul David Hsieh (Sr. Vice President, Marketing at Qubole), David Gehringer (Principal at Dimensional Research) Recorded: Aug 12 2015 43 mins
    Gartner predicts 70% of big data projects will fail due to lack of skills and integration challenges, yet new results from a survey by Dimensional Research show IT confidence in achieving big data success is 87%. In this webinar David Gehringer from Dimensional Research and David Hsieh from Qubole will discuss the survey results that illuminate big data reality gap and discuss pragmatic actions you can take to avoid becoming part of this alarming trend.

    Take a Deep Dive into the Results with David and David

    Sixty-five percent of IT teams recognize that to get to ubiquitous access to data and analytics, they need to enable a self-service DataOps approach. And most respondents -- 87 percent -- felt confident to extremely confident that they could deliver self-service analytics. Yet, respondents characterized their big data processes as still in the earliest stages of maturity: only 8 percent of respondents consider their big data initiatives to be fully mature.

    Don’t miss this unique opportunity to understand where your company sits compared to others on the Big Data Maturity Model.
  • Data Warehouse Modernization - Big Data in the Cloud Success with Qubole on Bare
    Data Warehouse Modernization - Big Data in the Cloud Success with Qubole on Bare Xing Quan (Senior Director of Product Management at Qubole), Craig Carl (Director of Solutions Architecture at Oracle) Recorded: Aug 12 2015 37 mins
    The effective use of big data is the key to gaining a competitive advantage and outperforming the competition. This change demands that companies consume and blend enormous amount of data created from divergent and inherently mismatched sources, which represents a paradigm shift to the traditional data warehouse.

    Companies need to modernize their data warehouse, augmenting it with a platform that allows storage, processing, exploration and analysis of large and diverse datasets without limiting the ability to deliver the data access, and flexibility responding to the needs of the business. That’s where Oracle Cloud and Qubole work together delivering a new breed of data platform —capable of storing and processing the overwhelming amount of data that on-premises big data deployments cannot handle.

    Watch this on-demand webinar to understand:

    Why deploying big data on-premises is expensive, complex to maintain and limits your ability to scale across new use cases and data sources

    How Oracle Bare Metal Cloud's predictable and fast performance compute and network services deliver the foundation of a cost-effective, high-performance big data platform

    How Qubole leverages Oracle Bare Metal Cloud to provide a turnkey big data service that optimizes cost, performance, and scale, enabling self-service data exploration.

    Qubole delivers a cloud-based, turnkey, self-service big data service that removes the complexity and reduces the cost of doing big data. It leverages Oracle Bare Metal Cloud’s next generation of scalable, inexpensive and performant compute, network and storage public cloud infrastructure to provide a solution that accelerates time to market and reduces the risk of your big data initiatives.

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