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Pivotal Data & Analytics

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  • Using Caching in Microservices Architectures: Session I
    Using Caching in Microservices Architectures: Session I Jagdish Mirani is a Product Marketing Manager in charge of Pivotal’s in-memory products Recorded: Apr 26 2017 54 mins
    In this 60 minute webinar, we will cover the key areas of consideration for data layer decisions in a microservices architecture, and how a caching layer, satisfies these requirements. You’ll walk away from this webinar with a better understanding of the following concepts:

    - How microservices are easy to scale up and down, so both the service layer and the data layer need to support this elasticity.
    - Why microservices simplify and accelerate the software delivery lifecycle by splitting up effort into smaller isolated pieces that autonomous teams can work on independently. Event-driven systems promote autonomy.
    - Where microservices can be distributed across availability zones and data centers for addressing performance and availability requirements. Similarly, the data layer should support this distribution of workload.
    - How microservices can be part of an evolution that includes your legacy applications. Similarly, the data layer must accommodate this graceful on-ramp to microservices.
  • The Data Warehouse in the Age of Digital Transformation
    The Data Warehouse in the Age of Digital Transformation Neil Raden, Principal Analyst, Hired Brains Research Recorded: Feb 22 2017 50 mins
    In the past years of Big Data and digital transformation “euphoria”, Hadoop and Spark received most of the attention as platforms for large-scale data management and analytics. Data warehouses based on relational database technology, for a variety of reasons, came under scrutiny as perhaps no longer needed.

    However, if there is anything users have learned recently it’s that the mission of data warehouses is as vital as ever. Cost and operational deficiencies can be overcome with a combination of cloud computing and open source software, and by leveraging the same economics of traditional big data projects - scale-up and scale-out at commodity pricing.

    In this webinar, Neil Raden from Hired Brains Research makes the case that an evolved data warehouse implementation continues to play a vital role in the enterprise, providing unique business value that actually aids digital transformation. Attendees will learn:

    - How the role of the data warehouse has evolved over time
    - Why Hadoop and Spark are not replacements for the data warehouse
    - How the data warehouse supports digital transformation initiatives
    - Real-life examples of data warehousing in digital transformation scenarios
    - Advice and best practices for evolving your own data warehouse practice
  • Using Data Science for Cybersecurity
    Using Data Science for Cybersecurity Anirudh Kondaveeti and Jeff Kelly Recorded: Jan 17 2017 56 mins
    Enterprise networks are under constant threat. While perimeter security can help keep some bad actors out, we know from experience that there is no 100%, foolproof way to prevent unwanted intrusions. In many cases, bad actors come from within the enterprise, meaning perimeter security methods are ineffective.

    Enterprises, therefore, must enhance their cybersecurity efforts to include data science-driven methods for identifying anomalous and potentially nefarious user behavior taking place inside their networks and IT infrastructure.

    Join Pivotal’s Anirudh Kondaveeti and Jeff Kelly in this live webinar on data science for cybersecurity. You’ll learn how to perform data-science driven anomalous user behavior using a two-stage framework, including using principal components analysis to develop user specific behavioral models. Anirudh and Jeff will also share examples of successful real-world cybersecurity efforts and tips for getting started.

    About the Speakers:
    Anirudh Kondaveeti is a Principal Data Scientist at Pivotal with a focus on Cybersecurity and spatio-temporal data mining. He has developed statistical models and machine learning algorithms to detect insider and external threats and "needle-in-the-hay-stack"​ anomalies in machine generated network data for leading industries.

    Jeff Kelly is a Principal Product Marketing Manager at Pivotal.

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