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Case Study in Big Data and Data Science: University of Georgia

Join this webinar to learn how the University of Georgia (UGA) uses Apache Spark and other tools for Big Data analytics and data science research.

UGA needs to give its students and faculty the ability to do hands-on data analysis, with instant access to their own Spark clusters and other Big Data applications.

So how do they provide on-demand Big Data infrastructure and applications for a wide range of data science use cases? How do they give their users the flexibility to try different tools without excessive overhead or cost?

In this webinar, you’ll learn how to:

- Spin up new Spark and Hadoop clusters within minutes, and quickly upgrade to new versions

- Make it easy for users to build and tinker with their own end-to-end data science environments

- Deploy cost-effective, on-premises elastic infrastructure for Big Data analytics and research
Recorded May 11 2016 61 mins
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Presented by
Shannon Quinn, Assistant Professor at University of Georgia; and Nanda Vijaydev, Director of Solutions Management at BlueData
Presentation preview: Case Study in Big Data and Data Science: University of Georgia

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  • Big Data Analytics on AWS: Getting Started with Big-Data-as-a-Service Dec 14 2016 6:00 pm UTC 60 mins
    Anant Chintamaneni, Vice President, Products, BlueData; Tom Phelan, Chief Architect, BlueData
    So you want to use Cloudera, Hortonworks, and MapR on AWS. Or maybe Spark with Jupyter or Zeppelin; plus Kafka and Cassandra. Now you can, all from one easy-to-use interface. Best of all, it doesn't require DevOps or AWS expertise.

    In this webinar, we’ll discuss:

    -Onboarding multiple teams onto AWS, with security and cost controls in a multi-tenant architecture
    -Accelerating the creation of data pipelines, with instant clusters for Spark, Hadoop, Kafka, and Cassandra
    -Providing data scientists with choice and flexibility for their preferred Big Data frameworks, distributions, and tools
    -Running analytics using data in Amazon S3 and on-premises storage, with pre-built integration and connectors

    Don’t miss this webinar on how to quickly and easily deploy Spark, Hadoop, and more on AWS – without DevOps or AWS-specific skills.
  • Distributed Data Science and Machine Learning - With Python, R, Spark, & More Recorded: Nov 2 2016 63 mins
    Nanda Vijaydev, Director of Solutions Management, BlueData; and Anant Chintamaneni, VP of Products, BlueData
    Implementing data science and machine learning at scale is challenging for developers, data engineers, and data analysts. Methods used on a single laptop need to be redesigned for a distributed pipeline with multiple users and multi-node clusters. So how do you make it work?

    In this webinar, we’ll dive into a real-world use case and discuss:

    - Requirements and tools such as R, Python, Spark, H2O, and others
    - Infrastructure complexity, gaps in skill sets, and other challenges
    - Tips for getting data engineers, SQL developers, and data scientists to collaborate
    - How to provide a user-friendly, scalable, and elastic platform for distributed data science

    Join this webinar and learn how to get started with a large-scale distributed platform for data science and machine learning.
  • DevOps and Big Data: Rapid Prototyping for Data Science and Analytics Recorded: Sep 15 2016 61 mins
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    Join this webinar with Cisco and BlueData to learn how to deliver greater agility and flexibility for Big Data analytics with Big-Data-as-a-Service.

    Your data scientists and developers want the latest Big Data tools for iterative prototyping and dev/test environments. Your IT teams need to keep up with the constant evolution of new tools including Hadoop, Spark, Kafka, and other frameworks.

    The DevOps approach is helping to bridge this gap between other developers and IT teams. Can DevOps agility and automation be applied to Big Data?

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    - A way to extend the benefits of DevOps to Big Data, using Docker containers to provide Big-Data-as-a-Service.
    -How data scientists and developers can spin up instant self-service clusters for Hadoop, Spark, and other Big Data tools.
    -The need for next-generation, composable infrastructure to deliver Big-Data-as-a-Service in an on-premises deployment.
    -How BlueData and Cisco UCS can help accelerate time-to-deployment and bring DevOps agility to your Big Data initiative.
  • Running Hadoop and Spark on Docker: Challenges and Lessons Learned Recorded: Aug 18 2016 62 mins
    Tom Phelan, Chief Architect, BlueData; Anant Chintamaneni, VP of Products, BlueData
    Join this webinar to learn how to run Hadoop and Spark on Docker in an enterprise deployment.

    Today, most applications can be “Dockerized”. However, there are unique challenges when deploying a Big Data framework such as Spark or Hadoop on Docker containers in a large-scale production environment.

    In this webinar, we’ll discuss:

    -Practical tips on how to deploy multi-node Hadoop and Spark workloads using Docker containers
    -Techniques for multi-host networking, secure isolation, QoS controls, and high availability with containers
    -Best practices to achieve optimal I/O performance for Hadoop and Spark using Docker
    -How a container-based deployment can deliver greater agility, cost savings, and ROI for your Big Data initiative

    Don’t miss this webinar on how to "Dockerize" your Big Data applications in a reliable, secure, and high-performance environment.
  • Big-Data-as-a-Service: On-Demand Elastic Infrastructure for Hadoop and Spark Recorded: Jun 22 2016 56 mins
    Kris Applegate, Big Data Solution Architect, Dell; Tom Phelan, Chief Architect, BlueData
    Watch this webinar to learn about Big-Data-as-a-Service from experts at Dell and BlueData.

    Enterprises have been using both Big Data and Cloud Computing technologies for years. Until recently, the two have not been combined.

    Now the agility and efficiency benefits of self-service elastic infrastructure are being extended to big data initiatives – whether on-premises or in the public cloud.

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    - The benefits of Big-Data-as-a-Service – including agility, cost-savings, and separation of compute from storage
    - Innovations that enable an on-demand cloud operating model for on-premises Hadoop and Spark deployments
    - The use of container technology to deliver equivalent performance to bare-metal for Big Data workloads
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  • Case Study in Big Data and Data Science: University of Georgia Recorded: May 11 2016 61 mins
    Shannon Quinn, Assistant Professor at University of Georgia; and Nanda Vijaydev, Director of Solutions Management at BlueData
    Join this webinar to learn how the University of Georgia (UGA) uses Apache Spark and other tools for Big Data analytics and data science research.

    UGA needs to give its students and faculty the ability to do hands-on data analysis, with instant access to their own Spark clusters and other Big Data applications.

    So how do they provide on-demand Big Data infrastructure and applications for a wide range of data science use cases? How do they give their users the flexibility to try different tools without excessive overhead or cost?

    In this webinar, you’ll learn how to:

    - Spin up new Spark and Hadoop clusters within minutes, and quickly upgrade to new versions

    - Make it easy for users to build and tinker with their own end-to-end data science environments

    - Deploy cost-effective, on-premises elastic infrastructure for Big Data analytics and research
  • Building Real-Time Data Pipelines with Spark Streaming, Kafka, and Cassandra Recorded: Mar 16 2016 62 mins
    Nik Rouda, Senior Analyst for Big Data at ESG; and Nanda Vijaydev, Director of Solutions Management at BlueData
    Join this webinar to learn best practices for building real-time data pipelines with Spark Streaming, Kafka, and Cassandra.

    Analysis of real-time data streams can bring tremendous value – delivering competitive business advantage, averting potential crises, or creating new revenue streams.

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    This webinar with Intel and BlueData describes an easier way to deploy Big Data.

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    Chris Harrold, Global CTO for Big Data, EMC; and Anant Chintamaneni, VP of Products, BlueData
    Join this webinar with EMC and BlueData for a discussion on cost-effective, high-performance Hadoop infrastructure for Big Data analytics.

    When Hadoop was first introduced to the market 10 years ago, it was designed to work on dedicated servers with direct-attached storage for optimal performance. This was sufficient at the time, but enterprises today need a modern architecture that is easier to manage as your deployment grows.

    Find out how you can use shared infrastructure for Hadoop – and separate compute and storage – without impacting performance for data-driven applications. This approach can accelerate your deployment and reduce costs, while laying the foundation for a broader data lake strategy.

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    Mike Gualtieri, Principal Analyst, Forrester Research and Anant Chintamaneni, VP of Products, BlueData
    Apache Spark has arrived in the enterprise. Adoption of the lightning-fast cluster computing phenomenon for big data processing is accelerating rapidly.

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    - How to get started quickly and easily.
  • BlueData EPIC 2.0 Demo Recorded: Sep 9 2015 3 mins
    BlueData
    BlueData software makes it easier, faster, and more cost-effective to deploy Big Data infrastructure on-premises. You can deploy big data clusters in minutes, not months.
  • Big Data Infrastructure Made Easy Recorded: Aug 26 2015 3 mins
    BlueData
    Learn how you can deploy Hadoop or Spark infrastructure on-premises: easier, faster, and more cost-effectively. With the BlueData EPIC™ software platform, you can:

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    *Work with any Big Data analytical application, any Hadoop or Spark distribution, and any infrastructure

    *Provide the enterprise-grade governance and security required, in a multi-tenant environment
  • Tame the Complexity of Big Data Infrastructure Recorded: Aug 12 2015 58 mins
    Tony Baer, Big Data Analyst, Ovum; Anant Chintamaneni, VP of Products, BlueData
    Implementing Hadoop can be complex, costly, and time-consuming. It can take months to get up and running, and each new user group typically requires their own infrastructure.

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    Join us to learn:

    - The pitfalls to avoid when deploying Big Data infrastructure
    - Real-world examples of multi-tenant Hadoop implementations
    - How to achieve the simplicity and agility of Hadoop-as-a-Service – but on-premises

    Gain insights and best practices for your Big Data deployment. Find out why data locality is no longer required for Hadoop; discover the benefits of scaling compute and storage independently. And more.
  • Apache Spark and Big Data Analytics: Solving Real-World Problems Recorded: May 19 2015 64 mins
    Parviz Peiravi, Principal Architect for Big Data, Intel; Anant Chintamaneni, VP of Products, BlueData
    Big Data analysis is having an impact on every industry today. Industry leaders are capitalizing on these new business insights to drive competitive advantage. Apache Hadoop is the most common Big Data framework, but the technology is evolving rapidly – and one of the latest innovations is Apache Spark. 
     
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    - How to leverage the power of Spark for iterative algorithms such as machine learning
    - Deployment strategies for Spark, leveraging your on-premises data center infrastructure
  • How to Simplify and Accelerate Hadoop Deployment Recorded: Mar 19 2015 49 mins
    Nik Rouda, Senior Analyst for Big Data, ESG; Anant Chintamaneni, VP of Products, BlueData
    Big Data is a top IT priority, yet many organizations are still in the early stages of deploying Hadoop and new data processing frameworks such as Spark. One of the challenges that slows down adoption is getting all the infrastructure and systems up and running. It’s a complex process and can often take weeks or even months.

    Join this webinar with Nik Rouda, senior analyst for Big Data at Enterprise Strategy Group (ESG), and Anant Chintamaneni, vice president of products at BlueData, to learn:

    • How to evaluate infrastructure deployment options (e.g. on-premises, Hadoop-as-a-Service)
    • Big Data infrastructure best practices, use cases, and real-world examples
    • How to leverage new technologies to speed up deployment for faster time-to-value with Big Data
Big Data Infrastructure Made Easy
BlueData is transforming how enterprises deploy their Big Data applications and infrastructure. The BlueData infrastructure software platform leverages Docker container technology and patent-pending innovations to make it easier, faster, and more cost-effective for enterprises of all sizes to deploy Hadoop or Spark infrastructure on-premises. With BlueData, our customers can spin up virtual Big Data clusters within minutes, providing their data scientists with on-demand access to the analytical applications, data and infrastructure they need. Founded in 2012 by VMware veterans and headquartered in Mountain View, California, BlueData is backed by investors including Amplify Partners, Atlantic Bridge, Ignition Partners, and Intel Capital.

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  • Title: Case Study in Big Data and Data Science: University of Georgia
  • Live at: May 11 2016 5:00 pm
  • Presented by: Shannon Quinn, Assistant Professor at University of Georgia; and Nanda Vijaydev, Director of Solutions Management at BlueData
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