Name: APJ Greenplum Summit 2020: An introduction to Greenplum
Start: 2020-11-17T03:00:00Z
End: 2020-11-17T04:40:55.000Z
Location: BrightTALK

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.

As the popularity of microservices continues to rise, so does the need for an efficient means of intercommunication. Features such as service discovery, client-side load balancing, and circuit breakers become invaluable tools as the complexity of your landscape grows. Thus, the rising popularity of the service mesh as means of delivering those capabilities.

While this technology space is still young, Istio and Envoy have already become the tools that many use to solve these problems. The Pivotal Application Service (PAS) integration with these solutions introduced weighted routing and guaranteed service identity—and now we’re bringing these features to Pivotal Container Service (PKS) via the new Pivotal Ingress Router. 

In this webinar, we’ll explore why a service mesh matters and how Pivotal Ingress Router works, and we’ll give you a sneak peak into its future.

Making Microservices Smarter with Istio, Envoy and Pivotal Ingress Router

This webinar is for IT professionals who have devoted considerable time and effort growing their careers in and around the Netezza platform. 

We’ll explore the architectural similarities and technical specifics of what makes the open source Greenplum Database a logical next step for those IT professionals wishing to leverage their MPP experience with a PostgreSQL-based database.  

As the Netezza DBMS faces a significant end-of-support milestone, leveraging an open source, infrastructure-agnostic replacement that has a similar architecture will help avoid a costly migration to either a different architecture or another proprietary alternative.

Pivotal Privacy Statement: 
https://pivotal.io/privacy-policy

This webinar:
https://content.pivotal.io/webinars/feb-13-ten-reasons-why-netezza-professionals-should-consider-greenplum-webinar

Ten Reasons Why Netezza Professionals Should Consider Greenplum

Containers have been widely adopted to make development and testing faster, and are now used at enterprise scale for stateless applications in production. Database infrastructure has not seen quite the same gains in terms of velocity over that same period, however. 

Can containers be as transformative for databases as they have been for application development? If container technology can be leveraged for running database workloads, what impact does this have on architects and operations teams that are responsible for running databases? 

We’ll discuss the trends—from virtualization to cloud to containerization—and the intersection of these platform trends with the data-driven world.

How to Use Containers to Simplify Speedy Deployment of Database Workloads

IoT and edge analytics/intelligence are broad terms that cover a wide range of applications and architectures. The one constant is that the data that streams in from sensors and other edge devices is valuable, offering a wealth of opportunities to process and exploit, in order to improve the products and services that enterprises offer to their customers.

But what is the nature of these intelligent analytical operations that one could do with sensor data, and where should those operations be performed? For example, where geographically should machine-learning models be trained: near the edge, in the data center, or perhaps at an intermediate point in between?

In this webinar, Neil Raden from Hired Brains Research and Frank McQuillan from Pivotal will discuss the notion of edge analytics/intelligence, including where to perform computations, what context is needed to do so effectively, and what the platforms look like that enable advanced analytics and machine learning on IoT data at scale. We will also offer examples from recent experience that demonstrate the range of possibilities.

Adding Edge Data to Your AI and Analytics Strategy

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

Simplify Access to Data from Pivotal GemFire Using the GraphQL (G2QL) Extension

Are you interested in learning how to schedule batch jobs in container runtimes?
Maybe you’re wondering how to apply continuous delivery in practice for data-intensive applications? Perhaps you’re looking for an orchestration tool for data pipelines?
Questions like these are common, so rest assured that you’re not alone.

In this webinar, we’ll cover the recent feature improvements in Spring Cloud Data Flow. More specifically, we’ll discuss data processing use cases and how they simplify the overall orchestration experience in cloud runtimes like Cloud Foundry and Kubernetes.

Please join us and be part of the community discussion!

Cloud-Native Patterns for Data-Intensive Applications

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.

5 Tips for  Getting Started with Pivotal GemFire

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!

How to Meet Enhanced Data Security Requirements with Pivotal Greenplum

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.

Mixing Analytic Workloads with Greenplum and Apache Spark

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.

Using Data Science to Build an End-to-End Recommendation System

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

Running Data Platforms Like Products

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.

Simplified Machine Learning, Text, and Graph Analytics with Pivotal Greenplum

Enterprise architectures never sleep because cloud-first strategies must also become multi-cloud-first strategies. Public cloud providers such as Microsoft Azure are providing compelling services and pricing. And, most enterprises now consider their own datacenter a private cloud. 

This is not a one-cloud playing field and enterprise architects must develop strategies, standards, and policies about how their data is being used, moved, and created across multiple cloud infrastructures.  

Join Pivotal’s Jag Mirani and Mike Stolz along with guest, Forrester Vice President and Principal Analyst, Mike Gualtieri, as they examine the trends driving multi-cloud adoption and more importantly how to architect technical solutions to make data free to roam among them safely.

Speakers:
Mike Gualtieri, VP, PRINCIPAL ANALYST, Forrester 
Jag Mirani, Product Marketing, Data Services, Pivotal 
Mike Stolz, Product Lead, GemFire, Pivotal

Overcoming Data Gravity In Multi-Cloud Enterprise Architectures

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

Cloud-Native Data: What data questions to ask when building cloud-native apps

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.

Replatform your Teradata to a Next-Gen Cloud Data Platform in Weeks, Not Years

When taking existing monoliths and decomposing their components into new microservices, the most critical concerns have much less to do with the application code and more to do with handling data. 

In this webinar, Kenny Bastani from Pivotal and Jason Mimick from MongoDB will focus on various methods of strangling a monolith’s ownership of domain data by transitioning the system of record over time. The new system of record, MongoDB, will fuel rapidly built and deployed microservices which companies can leverage for new revenue streams. 

They will use practices from Martin Fowler’s Strangler Application to slowly strangle domain data away from a legacy system into cloud-native MongoDB clusters using microservices built with Spring Boot and Spring Cloud. 


Speakers:
Kenny Bastani is a Spring developer advocate at Pivotal. As a passionate blogger and open source contributor, Kenny engages a community of passionate developers on topics ranging from graph databases to microservices. Kenny is a co-author of Cloud Native Java: Designing Resilient Systems with Spring Boot, Spring Cloud, and Cloud Foundry from O’Reilly.

Jason Mimick is the Technical Director for Partners at MongoDB developing new product and technical innovations with a number of companies. He's been at MongoDB nearly 4 years and previously spent the last 20-odd years in various engineering positions at Intersystems, Microsoft, and other companies.

How to Overcome Data Challenges When Refactoring Monoliths to Microservices

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.

Visualize and Analyze Apache Geode Real-time and Historical Metrics with Grafana

While we can't get together in person to discuss, share and shape the future of advanced data technologies, we can still learn from each other online! Join us at the APJ Greenplum Summit 2020, for a series of live sessions in your local language to discuss the latest developments in enterprise analytics and data science.

This session covers:
- An introduction to Greenplum
- Greenplum Everywhere
- Federated Analytics: Wrangling Business Insight from Disparate System
- Greenplum Command Center
- GP Text - How to integrate advanced Text processing into your Analytics.

APJ Greenplum Summit 2020: An introduction to Greenplum

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APJ Greenplum Summit 2020: An introduction to Greenplum

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