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Running Data Platforms Like Products

Applications need data, but the legacy approach of n-tiered application architecture doesn’t solve for today’s challenges. Developers aren’t empowered to build and iterate their code quickly without lengthy review processes from other teams. New data sources cannot be quickly adopted into application development cycles, and developers are not able to control their own requirements when it comes to data platforms.

Part of the challenge here is the existing relationship between two groups: developers and DBAs. Developers are trying to go faster, automating build/test/release cycles with CI/CD, and thrive on the autonomy provided by microservices architectures. DBAs are stewards of data protection, governance, and security. Both of these groups are critically important to running data platforms, but many organizations deal with high friction between these teams. As a result, applications get to market more slowly, and it takes longer for customers to see value.

What if we changed the orientation between developers and DBAs? What if developers consumed data products from data teams? In this session, Pivotal’s Dormain Drewitz and Solstice’s Mike Koleno will speak about:

- Product mindset and how balanced teams can reduce internal friction
- Creating data as a product to align with cloud-native application architectures, like microservices and serverless
- Getting started bringing lean principles into your data organization
- Balancing data usability with data protection, governance, and security
Recorded Jun 14 2018 58 mins
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Presented by
Dormain Drewitz, Pivotal & Mike Koleno, Solstice
Presentation preview: Running Data Platforms Like Products

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  • Simplify Access to Data from Pivotal GemFire Using the GraphQL (G2QL) Extension Recorded: Oct 17 2018 44 mins
    Sai Boorlagadda, Staff Software Engineer & Jagdish Mirani, Pivotal
    GemFire GraphQL (G2QL) is an extension that adds a new query language for your Apache Geode™ or Pivotal GemFire clusters allowing developers to build web and mobile applications using any standard GraphQL libraries. G2QL provides an out-of-the-box experience by defining GraphQL schema through introspection. It can be deployed to any GemFire cluster and serves a GraphQL endpoint from an embedded jetty server, just like GemFire’s REST endpoint.

    We will be demoing G2QL using a sample application that can read and write data to GemFire and share data between applications built using GemFire client APIs, showing you:

    - How to use GraphQL to query and mutate data in GemFire
    - How to use open-source GraphQL library to build web and mobile applications using GemFire
    - How to use GraphQL to deal with object graphs
    - How G2QL can simplify their overall architecture
  • 5 Tips for Getting Started with Pivotal GemFire Recorded: Aug 23 2018 41 mins
    Addison Huddy and Jagdish Mirani, Pivotal
    Pivotal GemFire is a powerful, distributed key-value store. It's the backbone of some of the most data-intensive workloads in the world. Whether you’re making a travel reservation, a stock trade, or buying a home, Pivotal GemFire is likely involved.

    During this webinar, we’ll dive into architecture best practices and data modeling techniques to get the most out of GemFire. We’ll look at common errors when working with In-memory Data Grids (IMDG) and run through five tips for getting started with Pivotal GemFire. Learn to model your data in a NoSQL key-value store, avoid serialization issues, and get the most out of your IMDG.
  • How to Meet Enhanced Data Security Requirements with Pivotal Greenplum Recorded: Aug 22 2018 52 mins
    Alastair Turner, Data Engineer & Greg Chase, Business Development, Pivotal
    As enterprises seek to become more analytically driven, they face a balancing act: capitalizing on the proliferation of data throughout the company while simultaneously protecting sensitive data from loss, misuse, or unauthorized disclosure. However, increased regulation of data privacy is complicating how companies make data available to users.

    Join Pivotal Data Engineer Alistair Turner for an interactive discussion about common vulnerabilities to data in motion and at rest. Alastair will discuss the controls available to Greenplum users—both natively and via Pivotal partner solutions—to protect sensitive data.

    We'll cover the following topics:

    - Security requirements and regulations like GDPR
    - Common data security threat vectors
    - Security strategy for Greenplum
    - Native security features of Greenplum

    Don’t miss this lively session on a timely issue—sign up today!
  • Mixing Analytic Workloads with Greenplum and Apache Spark Recorded: Aug 16 2018 35 mins
    Kong Yew Chan, Product Manager, Pivotal
    Apache Spark is a popular in-memory data analytics engine because of its speed, scalability, and ease of use. It also fits well with DevOps practices and cloud-native software platforms. It’s good for data exploration, interactive analytics, and streaming use cases.

    However, Spark, like other data-processing platforms, is not one size fits all. Different versions of Spark support different feature sets, and Spark’s machine-learning libraries can also vary in important ways between versions, or may lack the right algorithm.

    In this webinar, you’ll learn:

    - How to integrate data warehouse workloads with Spark
    - Which workloads are better for Greenplum and for Spark
    - How to use the Greenplum-Spark connector

    We look forward to you joining the webinar.
  • Using Data Science to Build an End-to-End Recommendation System Recorded: Jun 21 2018 62 mins
    Ambarish Joshi and Jeff Kelly, Pivotal
    We get recommendations everyday: Facebook recommends people we should connect with; Amazon recommends products we should buy; and Google Maps recommends routes to take. What all these recommendation systems have in common are data science and modern software development.

    Recommendation systems are also valuable for companies in industries as diverse as retail, telecommunications, and energy. In a recent engagement, for example, Pivotal data scientists and developers worked with a large energy company to build a machine learning-based product recommendation system to deliver intelligent and targeted product recommendations to customers to increase revenue.

    In this webinar, Pivotal data scientist Ambarish Joshi will take you step-by-step through the engagement, explaining how he and his Pivotal colleagues worked with the customer to collect and analyze data, develop predictive models, and operationalize the resulting insights and surface them via APIs to customer-facing applications. In addition, you will learn how to:

    - Apply agile practices to data science and analytics.
    - Use test-driven development for feature engineering, model scoring, and validating scripts.
    - Automate data science pipelines using pyspark scripts to generate recommendations.
    - Apply a microservices-based architecture to integrate product recommendations into mobile applications and call center systems.
  • Running Data Platforms Like Products Recorded: Jun 14 2018 58 mins
    Dormain Drewitz, Pivotal & Mike Koleno, Solstice
    Applications need data, but the legacy approach of n-tiered application architecture doesn’t solve for today’s challenges. Developers aren’t empowered to build and iterate their code quickly without lengthy review processes from other teams. New data sources cannot be quickly adopted into application development cycles, and developers are not able to control their own requirements when it comes to data platforms.

    Part of the challenge here is the existing relationship between two groups: developers and DBAs. Developers are trying to go faster, automating build/test/release cycles with CI/CD, and thrive on the autonomy provided by microservices architectures. DBAs are stewards of data protection, governance, and security. Both of these groups are critically important to running data platforms, but many organizations deal with high friction between these teams. As a result, applications get to market more slowly, and it takes longer for customers to see value.

    What if we changed the orientation between developers and DBAs? What if developers consumed data products from data teams? In this session, Pivotal’s Dormain Drewitz and Solstice’s Mike Koleno will speak about:

    - Product mindset and how balanced teams can reduce internal friction
    - Creating data as a product to align with cloud-native application architectures, like microservices and serverless
    - Getting started bringing lean principles into your data organization
    - Balancing data usability with data protection, governance, and security
  • Simplified Machine Learning, Text, and Graph Analytics with Pivotal Greenplum Recorded: May 24 2018 55 mins
    Bob Glithero, PMM, Pivotal and James Curtis Senior Analyst, 451 Research
    Data is at the center of digital transformation; using data to drive action is how transformation happens. But data is messy, and it’s everywhere. It’s in the cloud and on-premises. It’s in different types and formats. By the time all this data is moved, consolidated, and cleansed, it can take weeks to build a predictive model.

    Even with data lakes, efficiently integrating multi-structured data from different data sources and streams is a major challenge. Enterprises struggle with a stew of data integration tools, application integration middleware, and various data quality and master data management software. How can we simplify this complexity to accelerate and de-risk analytic projects?

    The data warehouse—once seen as only for traditional business intelligence applications — has learned new tricks. Join James Curtis from 451 Research and Pivotal’s Bob Glithero for an interactive discussion about the modern analytic data warehouse. In this webinar, we’ll share insights such as:

    - Why after much experimentation with other architectures such as data lakes, the data warehouse has reemerged as the platform for integrated operational analytics

    - How consolidating structured and unstructured data in one environment—including text, graph, and geospatial data—makes in-database, highly parallel, analytics practical

    - How bringing open-source machine learning, graph, and statistical methods to data accelerates analytical projects

    - How open-source contributions from a vibrant community of Postgres developers reduces adoption risk and accelerates innovation

    We thank you in advance for joining us.
  • Cloud-Native Data: What data questions to ask when building cloud-native apps Recorded: Mar 15 2018 64 mins
    Prasad Radhakrishnan, Platform Architecture for Data at Pivotal and Dave Nielsen, Head of Ecosystem Programs at Redis Labs
    While a number of patterns and architectural guidelines exist for cloud-native applications, a discussion about data often leads to more questions than answers. For example, what are some of the typical data problems encountered, why are they different, and how can they be overcome?

    Join Prasad Radhakrishnan from Pivotal and Dave Nielsen from Redis Labs as they discuss:

    - Expectations and requirements of cloud-native data
    - Common faux pas and strategies on how you can avoid them
  • Replatform your Teradata to a Next-Gen Cloud Data Platform in Weeks, Not Years Recorded: Mar 14 2018 54 mins
    Mike Waas, Founder & CEO Datometry, Inc., Derek Comingore, Data Engineering & Analytics Champion, Pivotal Software, Inc.
    Listen to key experts from Pivotal and Datometry on how your enterprise can migrate from a Teradata Data Warehouse to a next generation analytical platform in a matter of weeks, not years. Do this by using Greenplum, an open source, multi-cloud database solution along with Datometry’s category-defining data warehouse virtualization technology.

    Join us and learn:

    - How to gain significant economic and innovation benefits by moving to Pivotal Greenplum, a modern, multi-cloud data platform built for advanced analytics

    - When to eliminate the re-writing of Teradata applications using Datometry data warehouse virtualization technology and reducing migration costs by up to 90%

    - How to protect and expand your original data warehouse investment with new machine learning, geospatial, text, graph, and other innovative use cases

    Speakers:
    Mike Waas, Founder & CEO Datometry, Inc.
    Mike is one of the world’s top domain experts on database research. He has held key engineering positions at Microsoft, Amazon, Greenplum, EMC, and Pivotal where he worked on some of the commercially most successful database systems. Mike has authored or co-authored more than 35 publications and holds 24 patents on data management.

    Derek Comingore, Data Engineering & Analytics Champion, Pivotal Software, Inc.
    Derek is a passionate internationally recognized champion of data engineering and analytics. Derek serves as a regional anchor and pre-sales lead for Pivotal Data. Prior to Pivotal, Derek founded and sold an MPP systems integrator firm that catered to the Fortune 500.

    Thank you in advance for joining us.
  • Visualize and Analyze Apache Geode Real-time and Historical Metrics with Grafana Recorded: Feb 1 2018 59 mins
    Christian Tzolov, Pivotal
    Interested in a single dashboard providing a combined picture of both, real-time metrics and analysis of historical statistics for Apache Geode (Pivotal GemFire)? During this webinar we will show you how to create a dashboard providing the proper context for interpreting real-time metrics using Grafana - an open platform for analytics and monitoring.

    Accomplishing this requires the consolidation of two monitoring and metrics feeds in GemFire: the real-time metrics accessed via a JMX API; and the “post-mortem” historical statistics accessed via archive files.

    Join us as we describe and demonstrate how these two monitoring and metrics feeds can be combined, providing a unified monitoring and metrics dashboard for GemFire. We will also share common use cases and explore how the Geode Grafana Dashboard Repository, a pre-built collection of Geode-Grafana dashboards, helps create customized, monitoring dashboards.
  • Building a Big Data Fabric with a Next Generation Data Platform Recorded: Dec 13 2017 57 mins
    Noel Yuhanna, Forrester, Jacque Istok, Pivotal
    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?
  • Operationalizing Data Science: The Right Architecture and Tools Recorded: Nov 7 2017 51 mins
    Megha Agarwal, Data Scientist Pivotal
    In part one of this two-part series, you learned some of the common reasons enterprises struggle to turn insights into actions as well as a strategy for overcoming these challenges to successfully operationalize data science. In part two, it’s time to fill in the architectural and technological details of that strategy.

    Pivotal Data Scientist Megha Agarwal will share the key ingredients to successfully put data science models in production and use them to drive actions in real-time. In this webinar, you will learn:

    - Adopting extreme programming practices for data science
    - Importance of working in a balanced team
    - How to put and maintain machine learning models in production
    - End-to-end pipeline design

    We thank you in advance for joining us.
    The Pivotal Team
  • Analytical Innovation: How to Build the Next Generation Data Platform Recorded: Sep 14 2017 63 mins
    James Curtis, Senior Analyst, Data Platforms & Analytics, 451 Research & Jacque Istok, Head of Data, Pivotal
    There was a time when the Enterprise Data Warehouse (EDW) was the only way to provide a 360-degree analytical view of the business. In recent years many organizations have deployed disparate analytics alternatives to the EDW, including: cloud data warehouses, machine learning frameworks, graph databases, geospatial tools, and other technologies. Often these new deployments have resulted in the creation of analytical silos that are too complex to integrate, seriously limiting global insights and innovation.

    Join guest speaker, 451 Research’s Jim Curtis and Pivotal’s Jacque Istok for an interactive discussion about some of the overarching trends affecting the data warehousing market, as well as how to build a next generation data platform to accelerate business innovation. During this webinar you will learn:

    - The significance of a multi-cloud, infrastructure-agnostic analytics
    - What is working and what isn’t, when it comes to analytics integration
    - The importance of seamlessly integrating all your analytics in one platform
    - How to innovate faster, taking advantage of open source and agile software

    We look forward to you joining us.
    The Pivotal Team
  • Five Pitfalls When Operationalizing Data Science and a Strategy for Success Recorded: Aug 2 2017 64 mins
    Guest Speaker Mike Gualtieri, Forrester, Dormain Drewitz and Jeff Kelly, Pivotal
    Enterprise executives and IT teams alike know that data science is not optional, but struggle to benefit from it because the process takes too long and operationalizing models in applications can be hairy.

    Join guest speaker, Forrester Research’s Mike Gualtieri and Pivotal’s Jeff Kelly and Dormain Drewitz for an interactive discussion about operationalizing data science in your business. In this webinar, the first of a two-part series, you will learn:

    - The essential value of data science and the concept of perishable insights.
    - Five common pitfalls of data science teams.
    - How to dramatically increase the productivity of data scientists.
    - The smooth hand-off steps required to operationalize data science models in enterprise applications.
  • How to Build Modern Data Architectures Both On Premises and in the Cloud Recorded: Jul 20 2017 43 mins
    Jacque Istok, Head of Data Technical Field for Pivotal
    Enterprises are beginning to consider the deployment of data science and data warehouse platforms on hybrid (public cloud, private cloud, and on premises) infrastructure. This delivers the flexibility and freedom of choice to deploy your analytics anywhere you need it and to create an adaptable and agile analytics platform.

    But the market is conspiring against customer desire for innovation...

    Leading public cloud vendors are interested in pushing their new, but proprietary, analytic stacks, locking customers into subpar Analytics as a Service (AaaS) for years to come.

    In tandem, Legacy Data Warehouse vendors are trying to extend the lifecycle of their costly and aging appliances with new features of marginal value, simply imitating the same limiting models of public cloud vendors.

    New vendors are coming up with interesting ideas, but these ideas are often lacking critical features that don’t provide support for hybrid solutions, limiting the immediate value to users.

    It is 2017—you can, in fact, have your analytics cake and eat it too! Solve your short term costs and capabilities challenges, and establish a long term hybrid data strategy by running the same open source analytics platform on your infrastructure as it exists today.

    In this webinar you will learn how Pivotal can help you build a modern analytical architecture able to run on your public, private cloud, or on-premises platform of your choice, while fully leveraging proven open source technologies and supporting the needs of diverse analytical users.

    Let’s have a productive discussion about how to deploy a solid cloud analytics strategy.
  • Microservices Approaches for Continuous Data Integration Recorded: Jun 8 2017 64 mins
    Jurgen Leschner, Pivotal and Matt Aslett, Research Director, 451 Research
    How can businesses modernize their existing data integration flows? How can they connect a rapidly evolving number of data services? How can they capture, process, and generate new event streams? How can they leverage advances in Machine Learning to enhance real time interactions with their customers?

    Join Matt Aslett, Research Director at 451 Research, and Jürgen Leschner from Pivotal for an interactive discussion about continuous data integration applications, trends, and architectures.

    In this webinar you will learn:
    - How traditional data integration approaches like batch ETL can be improved
    - Why microservices support continuous data integration in a scalable way
    - How to incorporate DevOps practices in your data integration teams
    - What benefits microservices and DevOps practices bring to data integration
  • How to Turn Tweets Into Revenue Using Data Science Recorded: May 31 2017 53 mins
    Scott Hajek, Data Scientist, Pivotal
    Determining individual customer desire or intent from social media data provides companies, particularly consumer-facing companies, the opportunity to deliver targeted, more effective messaging and convert more sales. In some cases, the job is fairly easy.

    If someone tweets that she really enjoyed riding in a friend’s Tesla, for example, it’s pretty clear this person is more likely to respond to a targeted offer for an electric car than someone who tweets that they hate Teslas. But in many cases, discerning and capitalizing on customer desire and intent is more challenging. It requires confirming relevance, intent and sentiment, and matching customers with available inventory.

    Join this webinar and learn how to leverage Twitter data and images to create customized offers, using machine learning APIs. Scott Hajek, Pivotal Data Scientist, will describe the data science techniques, tools, and infrastructure to help you to capitalize on this potentially lucrative opportunity.
  • Using Caching in Microservices Architectures: Session I Recorded: Apr 26 2017 54 mins
    Jagdish Mirani is a Product Marketing Manager in charge of Pivotal’s in-memory products
    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 Recorded: Feb 22 2017 50 mins
    Neil Raden, Principal Analyst, Hired Brains Research
    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 Recorded: Jan 17 2017 56 mins
    Anirudh Kondaveeti and Jeff Kelly
    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.
Digital Transformation Happens from Data-Driven Action
Showing customers how to manage data and deploy advanced analytics with diverse data locality and data types. We demonstrate new features, functionality, and product updates.

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  • Live at: Jun 14 2018 5:00 pm
  • Presented by: Dormain Drewitz, Pivotal & Mike Koleno, Solstice
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