In this talk we present a business use case where Capital One needs to process customer activities real-time and react to events appropriately as needed. We then present our experience in building a real-time analytics application that serves the business using a set of open source software frameworks with Apache Flink at its core for real-time stream processing engine.
Kafka Streams represents a new design point in the stream processing space. Where most frameworks provide a service for running stream processing applications, Kafka Streams emphasizes low-overhead development that feels more like developing any other application.
Polymer is a new kind of library, built atop Web Components. This talk will cover the benefits of using Web Components to create your own encapsulated, custom HTML elements.
We recently replaced a proprietary API management solution with an in-house implementation built with nginx and Lua that is more robust, higher performance, and has greater visibility. Learn about our development process and the overall architecture that allowed us to write high-level code while enjoying native code performance, and how we leveraged other open source tools like Vagrant, Ansible, and OpenStack to build an automation-rich delivery pipeline. We will also take an in-depth look at our capacity management approach that differs from the rate limiting concept prevalent in the API community.
How would you like 2 extra hours of your time back every week? All mobile app developers face similar workflows as they work to upload an app to the App & Play Store. Many of these processes are currently done manually, but why not automate them? Fabric’s set of developer tools, collectively called fastlane, makes building, testing, and releasing your app faster, reproducible and less troublesome, leaving developers more time to focus on feature code and not deployment!
In today’s world of exploding big and fast data, developers who want both streaming analytics and ad hoc, OLAP-like analysis have often had to develop complex architectures such as Lambda—a path for fast streaming analytics using NoSQL stores such as Cassandra and HBase with a separate batch path involving HDFS and Parquet. While this approach works, it involves too many moving parts, too many technologies for ops, and too many engineering hours. Helena Edelson and Evan Chan highlight a much simpler approach to combine streaming and ad hoc/batch analysis using what they call the NoLambda stack (Apache Spark/Scala, Mesos, Akka, Cassandra, Kafka), plus FiloDB, a new entrant to the distributed-database world that combines streaming and ad hoc analytics.
In this talk, we will talk about the operational challenges of running a Cluster Scheduler to serve highly available services across multiple geographies and in a heterogeneous runtime environment. We will go into details of the needs from a cluster scheduler with respect to managing multiple runtime/virtualization platforms, provide observability, running maintenance on hardware and software, etc.
In this session we will explore the aspects of Clojure that encourage writing code that is loosely coupled and reusable. We will discuss the benefits of the Clojure approach, and we will see how it applies in practice with a live demo.
So you need to store data in your mobile application? Great, now you need to work with SQLite. Writing SQL is great fun if you enjoy thinking about mapping your objects to a relational store over and over and over. But what if there was another solution? One that allowed you to work with objects and store them as such with a powerful query system. No transformations back and forth to a relational store. Well, you’re in luck, one does exist: Realm. Realm is a mobile object (MVCC) database that can do all of these things and more. In this session learn how you can rid yourself of SQL, SQLite and its binding chains so you can harness the power and speed of working with native objects in Realm.