Track: Sponsored Solutions Track II

Multi-host, Multi-network Persistent Containers

Containers are great vessels for your application’s ephemeral data, but what about the data that drives your business? It must survive containers coming and going, maintain its availability and reliability, and grow when you need it. In this talk, we will discuss strategies for working with persistent containers, where you can store your data, and how to scale your persistent container layer. We will include code samples and interactive demos showing the power of Docker Machine, Engine, Swarm, and Compose, combined with multi-host networking, to build a reliable, scalable, and production-ready tier for the data needs of your organization.

Open Source Tools to Build ML Into Your Apps

Machine learning (ML) is becoming a significant part of an organization's strategic direction and a critical tool to deliver insights and automation. In this talk, we’ll explore the growing number of open source tools available to leverage ML in languages like Python, Go and others. We’ll dive into the usage of popular packages like Keras and Tensorflow and then walk through integrating and deploying them into a simple application to demonstrate how robust and accessible these tools have become. Learn how both developers and data-scientists can utilize the wide array of high quality, mature open source tools to incorporate and deploy machine-learning algorithms in your next applications.

Building A GraphQL API Backed By A Graph Database

Despite what the name may imply, GraphQL is not a query language for graph databases. Instead it is a query language for your API. By making the observation that your application data is a graph, GraphQL allows you to translate/map your application data from whatever the underlying model is to a graph. GraphQL then allows you to extract data from your graph by describing your data, asking for only exactly what you want, and get predictable results in a well defined structure. GraphQL can be used with any database, however when used with a graph database like Neo4j the impedance mismatch of translating from relational (or document, etc) to graph is removed, increasing developer productivity and performance. This talk will start with a brief overview of GraphQL and graph databases then dive into why they are awesome when used together! We'll talk about how we can use GraphQL with Neo4j and walk through some code examples.

How Comcast Automates Deployments

Continuous Delivery doesn’t end with automated deployments. Once your code is released to production, any minor imperfection or glitch can affect millions of users. It just doesn’t make sense to sift through logs and rely on end user reports at scale.

To solve this, the X1 Platform for XFINITY TV has introduced an automated error resolution strategy. In this talk, John McCann, Executive Director of Product Engineering at Comcast, will share automated workflows that his teams are using to resolve errors the minute they appear and drive down the mean time to resolution for over 10 million video customers.

99.99% Uptime at 175 TB of Data Per Day

In this talk we will cover how we design, build, and scale our platform to handle 5 million requests per second, 200 billion financial transactions a day and 175+ TBs of daily data processing distributed globally while handling unexpected unbounded growth. We will explain the rapid growth challenges we tackle every day in the areas of planning, releasing, monitoring and scaling our distributed systems with four nines of uptime. We will cover real-time updates of business logic with zero downtime, control systems and feedback loops, monitoring and app/event introspection.

Cloud Native Go

This session will provide patterns, practices, and sample code for building microservices in Go. We’re often tempted to add framework after framework to solve all of our problems, but this talk emphasizes the elegance of Go’s simplicity and illustrates how few dependencies you need to create services that can scale elastically, discover other services, remain stateless, get external configuration, and support the other “12 factors”. I’ll provide sample code that deals with routing, middleware, security, configuration, testing, and show how this all can be hooked to a Continuous Integration pipeline to build docker images that auto-deploy to your platform of choice after every commit. Come see why Go is such a great choice for building cloud native services!