Your guide to Amazon Neptune sessions, workshops, and chalk talks at AWS re:Invent 2019

AWS re:Invent 2019 is less than two weeks away. This post includes a complete list of Amazon Neptune sessions, workshops, and chalk talks at AWS re:Invent 2019. Use the information on this page to help schedule your week in Las Vegas and accelerate your knowledge of Amazon Neptune.

Sessions, Workshops, and Chalk Talks

ADM203 – Reimagining advertising analytics & identity resolution at scale (Session)

Most companies in Advertising & Marketing run some kind of big data workload. But are they taking advantage of the latest cloud technology? In this session, learn how AWS customers can optimize data collection, analytics, and identity resolution using containers, serverless computing, and graph databases. Customers share detailed technical best practices for big data and advertising analytics at massive scale and low cost. Then we dive deep into an example of how to improve identity matching and audience targeting using Amazon Neptune and other AWS tools.

ADM301 – Best practices for identity resolution with Amazon Neptune (Chalk Talk)

In this chalk talk, learn how to build a cloud-centric, graph-based identity resolution system that connects customer data across devices, channels, and touchpoints and helps enable better media buying and personalization. Learn about best practices and the mistakes to avoid for identity resolution data collection, processing, and preparation. We deep dive into architectural details for achieving high availability and low latency at scale using AWS services such as Amazon EMR, Amazon Neptune, Amazon EC2, and Amazon S3. We also discuss recommendations for using Neptune as a fully managed graph database service for identity resolution.

DAT220 – Real-world customer use cases with Amazon Neptune (Session)

Why do you need a graph database? In this session, we deep dive into the use cases of Amazon Neptune. You hear from Uber and NBCUniversal about how they have deployed highly scalable graph solutions with Neptune. This session will also cover the customer’s design decisions, the lessons they learned, and their experiences using Neptune.

DAT329 – Building your first graph application with Amazon Neptune (Workshop)

In this session, get hands-on with Amazon Neptune and build a cloud-based graph application. Learn how to quickly load data and begin writing Gremlin traversals.

DAT341-R – [REPEAT] Best practices for graph data modeling and Amazon Neptune (Chalk Talk)

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. In this session, we review some best practices for graph data modeling and how to migrate to graph from other data models.

DAT347-R – [REPEAT] Neptune best practices: How to optimize your graph queries (Builders Session)

In this builders session, we cover the fundamentals of performance tuning for OLTP query workloads against Amazon Neptune. Using AWS CloudFormation, you can set up a Neptune and Jupyter notebook stack, enabling you to run small read-and-write query workloads against Neptune in you own AWS account. We will then tune the scripts to maximize the throughput of the sample workloads through client-side parameter tuning and server-side improvements, such as failover to larger instance types and provisioning additional read replicas. Throughout the session, we discuss how to use Amazon CloudWatch to understand system behavior and identify optimizations. Please bring your laptop.

DAT361 – Deep dive on Amazon Neptune (Session)

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. In this session, we review how Neptune is optimized for storing billions of relationships and querying the graph with milliseconds latency. You also get a deep dive into the capabilities of the service and a review of the latest available features. Finally, we walk you through the techniques that you can use to migrate to Neptune.

DVC06 – Use Neptune to discover where & when events can impact local businesses (Dev Chats)

This dev chat explains for beginners how knowledge graphs work and how to use Amazon Neptune. Using a real-world example based on New York City’s public datasets, we take you through how relationships are mapped, using both static and temporal data to show you impacts and outcomes for different businesses. Our focus is on how different events impact businesses differently, and our main character (or entity) is the NYC rat.

GAM303 – How Call of Duty uses ML to personalize player engagement (Session)

Join Activision Blizzard to learn how Call of Duty, one of the world’s most famous first-person-shooter game franchises, continues to engage millions of players each day by running real-time analytics and machine learning pipelines with the help of AWS Cloud services. In this technical deep dive, Senior Manager of Consumer Technology Mike Bleske and Associate Solutions Architect Diego Toledo show you how Activision Blizzard nurtures millions of players every day and personalizes their experiences to increase engagement and spending through reinforcement machine learning pipelines built with Amazon Neptune, AWS Lambda, Amazon Kinesis, and Amazon Simple Queue Service (Amazon SQS).

MOB318-R – [REPEAT] AWS AppSync does that: Support for alternative data sources (Chalk Talk)

AWS AppSync supports a number of data sources out of the box, but can also support a variety of alternative data sources, including Amazon ElastiCache and Amazon Neptune. During this chalk talk, we discuss how to GraphQL-ify subscriptions to alternative data sources, including AWS services such as AWS Secrets Manager and AWS Step Functions.

STP05 – Building the factory of the future today with robotics & ML

Bright Machines is revolutionizing manufacturing by using adaptive robotics, computer vision, machine learning, and sensor technology to automate final product assembly. The company has deployed 30+ software-defined microfactories to customer manufacturing sites across Asia, Europe, and North America. It has leveraged AWS since its founding, using Amazon SageMaker, AWS Lambda, and Amazon EKS for deep learning; Amazon EMR, Amazon Neptune, and Amazon DocumentDB for analytics; and more. In this session, Nick Ciubotariu, SVP of Engineering at Bright Machines, and Jim Puzar, Flex’s VP & liaison to Bright Machines, talks about the company’s journey, customer case studies, and how it uses AWS to scale.

About the Author

Karthik Bharathy leads product for Amazon Neptune.