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BlueData

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  • Big Data Customer Case Study: The Advisory Board Company
    Big Data Customer Case Study: The Advisory Board Company Ramesh Thyagarajan, Advisory Board; Roni Fontaine, Hortonworks; Anant Chintamaneni, BlueData Recorded: Sep 14 2017 64 mins
    Join this webinar and learn how a leading healthcare company is yielding big dividends from Big Data.

    Advisory Board, a healthcare firm serving 90% of U.S. hospitals, has multiple different business units and data science teams within their organization. In this webinar, they'll share how they use technologies like Hadoop and Spark to address the diverse use cases for these different teams – with a highly flexible and elastic platform leveraging Docker containers.

    In this webinar, Advisory Board will discuss how they:

    -Migrated their analytics from spreadsheets and RDBMS to a modern architecture using tools such as Hadoop, Spark, H2O, Jupyter, RStudio, and Zeppelin.
    -Provide the ability to spin up instant clusters for greater agility, with shared and secure access to a treasure trove of data in their HDFS data lake.
    -Shortened time-to-insights from days to minutes, slashed infrastructure costs by more than 80 percent, and freed up staff to innovate and build new capabilities.

    Don’t miss this case study webinar. Find out how you can improve agility, flexibility, and ROI for your Big Data journey.
  • Hadoop and Spark on Docker: Container Orchestration for Big Data
    Hadoop and Spark on Docker: Container Orchestration for Big Data Anant Chintamaneni, Vice President, Products, BlueData; Tom Phelan, Chief Architect, BlueData Recorded: Jul 27 2017 63 mins
    Join this webinar to learn the key considerations and options for container orchestration with Big Data workloads.

    Container orchestration tools such as Kubernetes, Marathon, and Swarm were designed for a microservice architecture with a single, stateless service running in each container. But this design is not well suited for Big Data clusters constructed from a collection of interdependent, stateful services. So what are your options?

    In this webinar, we’ll discuss:

    - Requirements for deploying Hadoop and Spark clusters using Docker containers
    - Container orchestration options and considerations for Big Data environments
    - Key issues such as management, security, networking, and petabyte-scale storage
    - Best practices for a scalable, secure, and multi-tenant Big Data architecture

    Don’t miss this webinar on container orchestration for Hadoop, Spark, and other Big Data workloads.
  • Nasdaq Runs Big Data Analytics on BlueData
    Nasdaq Runs Big Data Analytics on BlueData Nasdaq, Intel, BlueData Recorded: Jul 25 2017 5 mins
    Watch this video to find out how Nasdaq improves agility and reduces costs for their Big Data infrastructure, while ensuring performance and security. To learn more about the BlueData software platform, visit www.bluedata.com
  • BlueData EPIC on AWS – Demo
    BlueData EPIC on AWS – Demo BlueData Recorded: Jul 25 2017 4 mins
    The BlueData EPIC software platform makes deployment of Big Data infrastructure and applications easier, faster, and more cost-effective – whether on-premises or on the public cloud.

    With BlueData EPIC on AWS, you can quickly and easily deploy your preferred Big Data applications, distributions and tools; leverage enterprise-class security and cost controls for multi-tenant deployments on the Amazon cloud; and tap into both Amazon S3 and on-premises storage for your Big Data analytics.

    Sign up for a free two-week trial at www.bluedata.com/aws
  • Simplifying Big Data Deployment
    Simplifying Big Data Deployment BlueData Recorded: Jul 25 2017 4 mins
    The BlueData software platform is a game-changer for Big Data analytics. Watch this video to see how BlueData makes it easier, faster, and more cost-effective to deploy Big Data infrastructure and applications on-premises.

    With BlueData, you can spin up Hadoop or Spark clusters in minutes rather than months – at a fraction of the cost and with far fewer resources. Leveraging Docker containers and optimized to run on Intel architecture, BlueData’s software delivers agility and high performance for your Big Data analytics.

    Learn more at www.bluedata.com
  • Top 5 Worst Practices for Big Data Deployments and How to Avoid Them
    Top 5 Worst Practices for Big Data Deployments and How to Avoid Them Matt Maccaux, Global Big Data Lead, Dell EMC; Anant Chintamaneni, Vice President, Products, BlueData Recorded: Jun 28 2017 63 mins
    Join this webinar to learn how to deploy a scalable and elastic architecture for Big Data analytics.

    Hadoop and related technologies for Big Data analytics can deliver tremendous business value, and at a lower cost than traditional data management approaches. But early adopters have encountered challenges and learned lessons over the past few years.

    In this webinar, we’ll discuss:

    -The five worst practices in early Hadoop deployments and how to avoid them
    -Best practices for the right architecture to meet the needs of the business
    -The case study and Big Data journey for a large global financial services organization
    -How to ensure highly scalable and elastic Big Data infrastructure

    Discover the most common mistakes for Hadoop deployments – and learn how to deliver an elastic Big Data solution.
  • Scalable Data Science with Spark, R, RStudio, & sparklyr
    Scalable Data Science with Spark, R, RStudio, & sparklyr Nanda Vijaydev, Director of Solutions Management, BlueData; and Anant Chintamaneni, Vice President, Products, BlueData Recorded: May 25 2017 62 mins
    Join this webinar to learn how to get started with large-scale distributed data science.

    Do your data science teams want to use R with Spark to analyze large data sets? How do you provide the flexibility, scalability, and elasticity that they need – from prototyping to production?

    In this webinar, we’ll discuss how to:

    *Evaluate compute choices for running R with Spark (e.g., SparkR or RStudio Server with sparklyr)
    *Provide access to data from different sources (e.g., Amazon S3, HDFS) to run with R and Spark
    *Create on-demand environments using Docker containers, either on-premises or in the cloud
    *Improve agility and flexibility while ensuring enterprise-grade security, monitoring, and scalability

    Find out how to deliver a scalable and elastic platform for data science with Spark and R.
  • Hybrid Architecture for Big Data: On-Premises and Public Cloud
    Hybrid Architecture for Big Data: On-Premises and Public Cloud Anant Chintamaneni, Vice President, Products, BlueData; Jason Schroedl, Vice President, Marketing, BlueData Recorded: Apr 13 2017 62 mins
    Join this webinar to learn how to deploy Hadoop, Spark, and other Big Data tools in a hybrid cloud architecture.

    More and more organizations are using AWS and other public clouds for Big Data analytics and data science. But most enterprises have a mix of Big Data workloads and use cases: some on-premises, some in the public cloud, or a combination of the two. How do you support the needs of your data science and analyst teams to meet this new reality?

    In this webinar, we’ll discuss how to:

    -Spin up instant Spark, Hadoop, Kafka, and Cassandra clusters – with Jupyter, RStudio, or Zeppelin notebooks
    -Create environments once and run them on any infrastructure, using Docker containers
    -Manage workloads in the cloud or on-prem from a common self-service user interface and admin console
    -Ensure enterprise-grade authentication, security, access controls, and multi-tenancy

    Don’t miss this webinar on how to provide on-demand, elastic, and secure environments for Big Data analytics – in a hybrid architecture.
  • Data Science Operations and Engineering: Roles, Tools, Tips, & Best Practices
    Data Science Operations and Engineering: Roles, Tools, Tips, & Best Practices Nanda Vijaydev, Director of Solutions Management, BlueData and Anant Chintamaneni Vice President, Products, BlueData Recorded: Feb 2 2017 64 mins
    Join this webinar to learn how to bring DevOps agility to data science and big data analytics.

    It’s no longer just about building a prototype, or provisioning Hadoop and Spark clusters. How do you operationalize the data science lifecycle? How can you address the needs of all your data science users, with various skillsets? How do you ensure security, sharing, flexibility, and repeatability?

    In this webinar, we’ll discuss best practices to:

    -Increase productivity and accelerate time-to-value for data science operations and engineering teams.

    -Quickly deploy environments with data science tools (e.g. Spark, Kafka, Zeppelin, JupyterHub, H2O, RStudio).

    -Create environments once and run them everywhere – on-premises or on AWS – with Docker containers.

    -Provide enterprise-grade security, monitoring, and auditing for your data pipelines.

    Don’t miss this webinar. Join us to learn about data science operations – including key roles, tools, and tips for success.
  • Big Data Analytics on AWS: Getting Started with Big-Data-as-a-Service
    Big Data Analytics on AWS: Getting Started with Big-Data-as-a-Service Anant Chintamaneni, Vice President, Products, BlueData; Tom Phelan, Chief Architect, BlueData Recorded: Dec 14 2016 64 mins
    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.

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