Hi [[ session.user.profile.firstName ]]


  • Date
  • Rating
  • Views
  • 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 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.
  • Converging Your Data Landscape
    Converging Your Data Landscape Jack Norris, MapR Technologies & John Myers, Enterprise Management Associates (EMA) Recorded: Jan 11 2018 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
  • Machine Learning Workshop 2: Machine Learning Model Comparison & Evaluation
    Machine Learning Workshop 2: Machine Learning Model Comparison & Evaluation Ted Dunning PhD, Chief Application Architect - MapR Technologies Recorded: Nov 28 2017 61 mins
    In this addition of our machine learning webinar series we will be building on the ideas of the key requirements for effective management of machine learning logistics presented in the Overview webinar and in Part I Workshop, we will:

    Dive into what model evaluation really can and should be
    Talk about how the rendezvous architecture makes evaluation more effective and also easier

    Watch this webinar to hear Ted cover multi-model comparison, how rendezvous helps you handle metrics, and how it provides query-by-query comparison. A key issue for real world success that is often overlooked by data scientists is latency and system reliability. Conversely, accuracy is often difficult for SysOps team members to address. The rendezvous approach has a built-in way to include latency and accuracy as systematic parts of evaluation, thus addressing key concern of all parts of a DataOps team.

    Finally, hear Ted discuss how the containerization of models and system components in a rendezvous architecture makes security auditing easier.
  • 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 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
  • 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 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.
  • 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 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.
  • Machine Learning Workshop 1: A New Architecture for Machine Learning Logistics
    Machine Learning Workshop 1: A New Architecture for Machine Learning Logistics Ted Dunning PhD, Chief Application Architect - MapR Technologies Recorded: Oct 24 2017 69 mins
    How to use streaming, containers and a microservices design

    Having heard the high-level rationale for the rendezvous architecture in the introduction to this series, in this session we dug in deeper to talk about how and why the pieces fit together. In terms of components, we covered why streams work, why they need to be persistent, performant and pervasive in a microservices design and how they provide isolation between components. From there, we talked about some of the details of the implementation of a rendezvous architecture including discussion of when the architecture is applicable, key components of message content and how failures and upgrades are handled. We touched on the monitoring requirements for a rendezvous system but will save the analysis of the recorded data for later.

    At the end of this workshop, you should have a clear understanding of the fit and finish between the parts of the rendezvous model and should be able to evaluate its applicability in your situation.
  • Machine Learning Success: The Key to Easier Model Management
    Machine Learning Success: The Key to Easier Model Management Ellen Friedman PhD, Principal Technologist, MapR Recorded: Sep 19 2017 59 mins
    Listen to Ellen Friedman, co-author (with Ted Dunning) of a new short O’Reilly book Machine Learning Logistics: Model Management in the Real World, to look at what you can do to have effective model management, including the role of stream-first architecture, containers, a microservices approach and a DataOps style of work. Ellen will provide a basic explanation of a new architecture that not only leverages stream transport but also makes use of canary models and decoy models for accurate model evaluation and for efficient and rapid deployment of new models in production.
  • Data Warehouse Modernization: Accelerating Time-to-Action
    Data Warehouse Modernization: Accelerating Time-to-Action Clarke Patterson, StreamSets & Ankur Desai, MapR Recorded: Jun 6 2017 60 mins
    Data warehouses have been the standard tool for analyzing data created by business operations. In recent years, increasing data volumes, new types of data formats, and emerging analytics technologies such as machine learning have given rise to modern data lakes. Connecting application databases, data warehouses, and data lakes using real-time data pipelines can significantly improve the time to action for business decisions.

    Listen to our MapR and StreamSets experts to learn how to:

    Connect your databases, data warehouse, and data lake without code: StreamSets can help you connect your existing databases and data warehouse with the MapR Converged Data Platform to create a modern data lake. You can offload/move data without having to write a single line of code as well as continually monitor and visualize your dataflows.
    Utilize change data capture (CDC) for efficient real-time data movement: No more analyzing stale data or reliance on batch database updates. Leverage CDC to continually update the MapR data lake and ensure your insights and action are based on complete and current data.
    Effectively analyze frequently changing data: MapR Converged Data Platform is built on a read-write file system unlike other big data platforms. Land frequently changing data into MapR-DB and analyze it using modern distributed computing technologies.

Embed in website or blog