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With Viktor Klang, Deputy CTO Lightbend, Inc. As software grows more and more interconnected, and with several generations of software having to interoperate, a new take on the integration of systems is needed—ad hoc, unversioned, and unreplicated scripts just won’t suffice, and the traditional Enterprise Service Bus (ESB) concept has experienced stability, reliability, performance, and scalability problems. In this webinar, Viktor explores a new take on Enterprise Integration Patterns: First, he will explore the Reactive Streams standard, an orchestration layer where transformations are standalone, composable, reusable, and—most importantly—using asynchronous flow-control—back pressure—to maintain predictable, stable, behavior over time. Furthermore, he will go through how one-off workloads relate to continuous, and batch, workloads, and how they can be addressed by that very same orchestration layer. Finally, he will review how this type of design achieves resilience, scalability, and ultimately—responsiveness.

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From banks to airlines to credit rating agencies, security continues to be a major focus for organizations across various industries. As the newspapers show, it’s heavily damaging to enterprises when security vulnerabilities in their code, infrastructure, or open source frameworks/libraries get exploited. The good news is that your Scala development team now has a powerful ally for securing their applications. Co-developed by the Fortify team along with Lightbend, the upcoming Fortify for Scala Plugin is the only Static Application Security Testing (SAST) solution to use the official Scala compiler. This plugin automatically identifies code-level security vulnerabilities early in the SDLC, so you can confidently and reliably secure your mission-critical Scala-based applications. In this webinar by Seth Tisue, Scala Committer and Senior Scala Engineer at Lightbend, and Poonam Yadav, Product Manager for Fortify at Micro Focus, you will learn about: * Some of the more than 200 vulnerabilities that the Fortify plugin for Scala can catch and help you resolve, * How the plugin works to analyze, identify and provide actionable recommendations, * How to integrate it into your modern DevOps environment, * Why this plugin was co-developed by Lightbend and the Fortify team, and how it benefits your organization’s security professionals / CISO office.

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Microservices. Streaming data. Event Sourcing and CQRS. Concurrency, routing, self-healing, persistence, clustering… You get the picture. The Akka toolkit makes all of this simple for Java and Scala developers at Amazon, LinkedIn, Starbucks, Verizon and others. So how does Akka provide all these features out of the box? Join Hugh McKee, Akka expert and Developer Advocate at Lightbend, on an illustrated journey that goes deep into how Akka works–from individual Akka actors to fully distributed clusters across multiple datacenters.

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Application development has come a long way. From client-server, to desktop, to web based applications served by monolithic application servers, the need to serve billions of users and hundreds of devices have become crucial to today's business. Typesafe Reactive Platform helps you to modernize your applications by transforming the most critical parts into microservice-style architectures which support extremely high workloads and allow you to serve millions of end-users.

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In recent times, Reactive Programming has gained a lot of popularity. It is not a “silver bullet” nor it is a solution for every problem. Yet, it is a paradigm to build applications which are non-blocking, event-driven and asynchronous and require a small number of threads to scale. Spring Framework 5 embraces Reactive Streams and Reactor for its own reactive use, as well as in many of its core APIs. It also adds an ability to code in a declarative way, as opposed to imperatively, resulting in more responsive and resilient applications. On top of that, you are given an amazing toolbox of functions to combine, create and filter any data stream. It becomes easy to support input and output streaming scenarios for microservices, scatter/gather, data ingestion, and so on. This presentation is about support and building blocks for reactive programming, that come with the latest versions of Spring Framework 5 and Spring Boot 2.

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At Stitch Fix, we maintain a distributed Kafka Connect cluster running several hundred connectors. Over the years, we've learned invaluable lessons for keeping our connectors going 24/7. As many conference goers probably know, event driven applications require a new way of thinking. With this new paradigm comes unique operational considerations, which I will delve into. Specifically, this talk will be an overview of: 1) Our deployment model and use case (we have a large distributed Kafka Connect cluster that powers a self-service data integration platform tailored to the needs of our Data Scientists). 2) Our favorite operational tools that we have built for making things run smoothly (the jobs, alerts and dashboards we find most useful. A quick run down of the admin service we wrote that sits on top of Kafka Connect). 3) Our approach to end-to-end integrity monitoring (our tracer bullet system that we built to constantly monitor all our sources and sinks). 4) Lessons learned from production issues and painful migrations (why, oh why did we not use schemas from the beginning?? Pausing connectors doesn't do what you think it does... rebalancing is tricky... jar hell problems are a thing of the past, upgrade and use plugin.path!). 5) Future areas of improvement. The target audience member is an engineer who is curious about Kafka Connect or currently maintains a small to medium sized Kafka Connect cluster. They should walk away from the talk with increased confidence in using and maintaining a large Kafka Connect cluster, and should be armed with the hard won experiences of our team. For the most part, we've been very happy with our Kafka Connect powered data integration platform, and we'd love to share our lessons learned with the community in order to drive adoption.

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At Capital One, we use Spark to detect Fraud. Recently we have started implementing real-time fraud detection using machine learnt models. One of Capital One’s fraud detection micro services was an early adopter of Structured Streaming. As part of this implementation, the micro service ran into several roadblocks. In this talk, we describe those roadblocks and how we got around them. Caching of lookup data Dilemma: Store state in Spark vs store state in database Retrieve state from database efficiently Non-homogenous data sources Aggregations in the stream Checkpointing fumbles Checkpointing performance and instabilities

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KSQL is the streaming SQL engine for Apache Kafka. It provides an easy and completely interactive SQL interface for stream processing on Kafka. Users can express their processing logic in SQL like statements and KSQL will compile and execute them as Kafka Streams applications. Although KSQL provides a rich set of features and built in functions, many use cases require more domain specific processing logic that cannot be expressed in pure SQL. To enable users to use KSQL in such scenarios, KSQL provides a framework to define complex processing logic as User Defined Functions (UDFs) and User Defined Aggregate Functions (UDAFs). In this talk, we provide a deep dive into the UDF/UDAF framework in KSQL. We explain how users can define their custom UDFs/UDAFs and use them in their queries. We also describe how KSQL utilizes the provided UDFs/UDAFs under the hood to process streams and tables. This deep dive will include an insight into how UDFs process data and how UDAFs keep track of their state. Armed with such knowledge, KSQL users will be able to define and utilize complex data processing logic in their KSQL queries. They will also be able to diagnose and fix issues in defining and deploying their UDFs/UDAFs more efficiently.

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Slick is Typesafe's open source database access library for Scala. It features a collection-style API, compact syntax, type-safe, compositional queries and explicit execution control. Community feedback helped us to identify common problems developers are facing when writing Slick applications. This talk suggests particular solutions to these problems. We will be looking at reducing boiler-plate, re-using code between queries, efficiently modeling object references and more.

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