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    • Data Fabric @ Scale: Breaking Through Legacy Data Architectures
      Data Fabric @ Scale: Breaking Through Legacy Data Architectures Jack Norris - Senior Vice President, Data and Applications, MapR Recorded: Oct 25 2017 1:00 pm UTC 49 mins
    • The use of an emerging data fabric, offers enterprises a number of benefits and advantages including the ability to break through the gravitational pull of legacy data architectures and capture the full potential of all your data.

      This webinar will detail how the deployment of a data fabric can enable enterprises to more quickly and easily scale across data volumes, data types and locations. The session will also provide an overview on how a data fabric reduces storage costs and increases application agility and reliability – with the underpinning to support the successful pursuit of:

      * IoT through a data fabric’s capability of handling data flows from the edge to the cloud, centralizing learning, and distributing intelligence back to the edge for real-time responsiveness.

      * Machine Learning/AI with the fabric able to handle the complex data flows and logistics to support the rapid deployment and coordination across machine learning models, algorithms and analytic tools

      * Microservices and containers with the underlying data fabric able to support intelligent streams and support the mobility and flexibility for elastic stateful applications and analytic processes relying on shared data.

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    • Sharing Sensitive Data Across Departments, Companies, Countries & Clouds
      Sharing Sensitive Data Across Departments, Companies, Countries & Clouds Pinakin Patel, MapR & Lawrence Stoker, MapR Recorded: Apr 16 2018 7:10 pm UTC 28 mins
    • The sharing of sensitive data across departments, within group companies, externally, and across national boundaries is increasingly subject to legislation.

      In addition, enforcing governance of data subject to such requirements is increasingly challenging, costly and time consuming; with the processes often manual and disjointed; increasing the risk of breach, fines and loss of reputation.

      Join us, to find out how MapR’s Global Data Fabric empowers you with a SELF-SERVICE data sharing solution across departments, companies, jurisdictional boundaries, and clouds, ensuring:

      - The rules governing data are automatically adhered to at access time
      - Control of data remains fully with the sovereign data owner
      - Data Access is fully audited across the organisation
      - Sharing sensitive data across departments, companies, countries & clouds using a Global Data Fabric

      The sharing of sensitive data across departments, within group companies, externally, and across national boundaries is increasingly subject to legislation.

      In addition, enforcing governance of data subject to such requirements is increasingly challenging, costly and time consuming; with the processes often manual and disjointed; increasing the risk of breach, fines and loss of reputation.

      Join us, to find out how MapR’s Global Data Fabric empowers you with a SELF-SERVICE data sharing solution across departments, companies, jurisdictional boundaries, and clouds, ensuring:

      The rules governing data are automatically adhered to at access time. Control of data remains fully with the sovereign data owner. Data Access is fully audited across the organisation.

      Read more >
    • Building a Big Data Fabric with a Next Generation Data Platform
      Building a Big Data Fabric with a Next Generation Data Platform Noel Yuhanna, Forrester, Jacque Istok, Pivotal Recorded: Dec 13 2017 7:00 pm UTC 57 mins
    • For more than 25 years IT organizations have spent many cycles building enterprise data warehouses, but both speed to market and high cost has left people continually searching for a better way. Over the last 10 years, many found an answer with Hadoop, but the inability to recruit skilled resources, combined with common enterprise necessities such as ANSI compliant SQL, security and the overall complexity has Hadoop relegated to an inexpensive, but scalable data repository.

      Join Noel Yuhanna from Forrester and Pivotal’s Jacque Istok for an interactive discussion about the most recent data architecture evolution; the Big Data Fabric. During this webinar you will learn:

      What a Big Data Fabric is
      - How does it leverage your existing investments in enterprise data warehouses, data marts, cloud analytics, and Hadoop clusters?
      How to leverage your team’s expertise to build a Big Data Fabric
      - What skills should you be investing in to continue evolving with the market?
      When is it appropriate for an organization to move to a Big Data Fabric
      - Can you afford to divert from your existing path? Can you afford not to?
      The skills and technologies that will ease the move to this new architecture
      - What bets can you place that will keep you moving forward?

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    • GDPR Best Practice: Using a Data Hub to Protect Personal Data
      GDPR Best Practice: Using a Data Hub to Protect Personal Data Remi Forest, MapR Technologies & Jean-Michel Franco, Talend Recorded: Jan 16 2018 1:55 pm UTC 52 mins
    • Is Your Data Ready for GDPR?

      As the deadline for GDPR approaches, it is time to get practical about protecting personal data.

      We break down the steps for turning a data lake into a data hub with appropriate data management and governance activities: from capturing and reconciling personal data to providing for consent management, data anomyzation, and the rights of the data subject.

      A smart approach to GDPR compliance lays a foundation for personalized and profitable customer and employee relations.

      Watch, as experts from MAPR and Talend show you how to:

      - Diagnose the maturity of your GDPR compliance;
      - Set up milestones and priorities to reach compliance;
      - Create a foundation to manage personal data through a data lake;
      - Master compliance operations - from data inventory to data transfers to individual rights management.

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    • Best Practices: Implementing DataOps with a Data Science Platform
      Best Practices: Implementing DataOps with a Data Science Platform Crystal Valentine, MapR Technologies & William Merchan, DataScience.com Recorded: Nov 7 2017 2:55 pm UTC 66 mins
    • With the growing number of data-driven organizations new approaches are needed to drive innovation in scaling and implementing data science. We will discuss how data and data science platforms take advantage of what we are calling DataOps. We will share background on this approach and how it supports putting data science models into production. We will provide best practices and a roadmap on how to implement these techniques to become a leader in machine learning and data science.

      Watch the recording of this complimentary webinar with experts from DataScience.com & MapR to:

      - Learn about the benefits of applying a DataOps approach to your data science workflow
      - Review best practices for how IT teams can support their data science teams
      - Hear how customers of MapR and DataScience.com have reaped the benefits of this new approach.

      Read more >
    • Enabling Real-Time Business with Change Data Capture
      Enabling Real-Time Business with Change Data Capture Audrey Egan, MapR Technologies & Rupal Shah, StreamSets Recorded: Nov 14 2017 2:25 pm UTC 55 mins
    • Machine learning (ML) and artificial intelligence (AI) enable intelligent processes that can autonomously make decisions in real-time. The real challenge for effective ML and AI is getting all relevant data to a converged data platform in real-time, where it can be processed using modern technologies and integrated into any downstream systems.

      Running a business in real-time means being able to react to important business events as they happen. Applications that support day-to-day operations, however, are often scattered across the organization making it difficult to enable real-time movement of data.

      In this session, MapR and StreamSets discussed how change data capture (CDC) can be used to enable real-time workloads to drive success with ML and AI. You’ll see demonstrations of technologies that enable CDC, and specifically learn how to:

      - Utilize change data capture (CDC) for efficient real-time data movement & processing
      - Connect your databases, data warehouses, and data lakes without code
      - Use MapR-DB as both source and destination for change data capture

      Read more >
    • Deploy the In-Memory Data Fabric
      Deploy the In-Memory Data Fabric Mac Moore Recorded: Jan 27 2015 7:00 pm UTC 60 mins
    • Businesses large and small are increasingly turning to comprehensive in-memory data processing solutions, such as the GridGain In-Memory Data Fabric, to address their Fast Data challenges and create a competitive advantage by operating as a real-time business. When deploying an In-Memory Data Fabric into a production environment, typical challenges that need to be addressed are around availability and resilience, security and manageability, among other things.

      Join GridGain Solution Architect Mac Moore, as he explains how to harden the deployment of the GridGain In-Memory Data Fabric by taking advantage of a number of enterprise-grade features in the commercial version of the product designed for always up, always on real-time data processing.

      This webinar is a must-see for technology leaders in the transition to high-speed, low-latency Fast Data systems.

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    • Converging Your Data Landscape
      Converging Your Data Landscape Jack Norris, MapR Technologies & John Myers, Enterprise Management Associates (EMA) Recorded: Jan 11 2018 12:00 pm UTC 59 mins
    • How Data-Driven Approaches are Changing Your Data Management Strategies

      Introducing data-driven strategies into your business model alters the way your organization manages and provides information to your customers, partners and employees. Gone are the days of “waterfall” implementation strategies from relational data to applications within a data center. Now, data-driven business models require agile implementation of applications based on information from all across an organization–on-premises, cloud, and mobile–and includes information from outside corporate walls from partners, third-party vendors, and customers. Data management strategies need to be ready to meet these challenges or your new and disruptive business models will fail at the most critical time: when your customers want to access it.

      In this webinar, John L. Myers of Enterprise Management Associates (EMA) and Jack Norris of MapR will discuss how the new business advancements require data-rich applications that enable global, real-time data integration, microservices support, and in-place and continuous machine learning/AI and SQL capabilities.

      Watch this video to learn:

      - Examples of disruptive business models
      - Drivers of changes to the management landscape
      - Best practices associated with meeting requirements for data-driven applications

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    • Data Fabric: A New Paradigm For Self-Service Data & Data Scientists
      Data Fabric: A New Paradigm For Self-Service Data & Data Scientists Kelly Stirman, VP Strategy, Dremio Recorded: Dec 12 2017 5:00 pm UTC 45 mins
    • Data Scientists are rare and highly valued individuals, and for good reason: making sense of data, and using the machine learning libraries requires an unusual blend of advanced skills. Why is it then that Data Scientists spend the majority of their time getting data ready for models, and a fraction actually doing the high value work?

      In this talk we introduce the concept of Data Fabric, a new way to provide a self-service model for data, where data scientists can easily discover, curate, share, and accelerate data analysis using Python, R, and visualization tools, no matter where the data is managed, no matter the structure, and no matter the size.

      We will talk through the role of Apache Arrow, the in-memory columnar data standard that is accelerating analytics for GPU-based processing, as well as the role of Pandas and Arrow in providing unprecedented speed in accessing datasets from Python.

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