PlusOne

MQ, ETL and ESB middleware are often used as integration backbone between legacy applications, modern microservices and cloud services. This introduces several challenges and complexities like point-to-point integration or non-scalable architectures. This session discusses how to build a completely event-driven streaming platform leveraging Apache Kafka’s open source messaging, integration and streaming components to leverage distributed processing, fault-tolerance, rolling upgrades and the ability to reprocess events. nLearn the differences between an event-driven streaming platform leveraging Apache Kafka and middleware like MQ, ETL and ESBs – including best practices and anti-patterns, but also how these concepts and tools complement each other in an enterprise architecture.

When you start developing serverless applications in the real world, sooner rather than later you will need to talk to external (legacy) systems. In this talk you will discover how to leverage Apache Camel K to connect your Knative serverless applications with everything Camel can connect.nApache Camel K allows running Camel routes as serverless applications directly on top of any Kubernetes cluster, leveraging Knative serverless capabilities such as auto-scaling, scaling to zero, event-based communication in order to connect serverless functions and microservice with external systems.

In this session, you will learn all that’s new with Apache Unomi, the open source Customer Data platform (that graduated this year) based on the Apache Karaf runtime, and all that’s happened since the last ApacheCon. You will discover how to easily integrate it with an existing website or SPA/PWA using its built-in web tracker, how to build customer segments and how to use the API to personalize the experience for your users. You’ll also learn how you can extend it to do almost anything, using either the built-in rules engine or your own plugins. You will also discover the new Docker compatibility and the upcoming GraphQL API. Finally, you’ll learn what’s next and how you can help the project.

The Netflix media pipeline processes thousands of new shows and movies every day so that you can watch them on any device anywhere. We use a forward chaining rule engine to coordinate all of this work in multiple workflows. Hosting these workflows in a reliable, scalable and cost effective manner is a huge challenge at our scale. In this talk, we will introduce the design of Netflix’s next generation rule engine framework. The goal is to boost modularity, increase developer productivity and decrease operational overhead. The new system is a platform as a service that lets workflow developers focus on workflow data model, execution conditions, and remote function invocations without worrying about how to deploy, scale, and monitor it. The system uses the OSGI framework to build separation among workflows and leverages Apache Karaf as the runtime container. Other interesting topics such as workflow bundle management and a novel rule domain specific language will be covered in this talk. Keywords:nRule engine, OSGI, Apache Karaf, Serverless

Apache Karaf has been seen for a while as an applications server. If it’s actually the case for the standard distribution, it’s very easy to create a Karaf powered standard/immutable distribution embedding your applications. In combination with other Karaf subprojects (Decanter, Cave, …), it provides a perfect applications runtime for the cloud.nThis talk will show how to start and focus on business code and then generate different distribution packages, up to a docker image ready to run on cloud.

Going back well before the ASF was formed, community has played a vital role in the development of open source software. With the formation of the ASF, we see a great example of open source community being formalized and actively facilitated. Although it can seem counterintuitive, especially to the uninitiated, placing ‘Community over code’ helps ensure the ongoing relevance of open source as a movement, inviting individuals and organizations alike to invest in ‘free’ software. The community participation statistics across the ASF are impressive and highly convincing.nThe results of this community participation include personal growth, an ever evolving and strengthening Apache project, as well as a strong foundation for commercial business development.n- Brief historyn- ASF stats overviewn- What ‘Community over code’ means to various Apache community members At the individual project level, Apache OFBiz has benefitted from hundreds of thousands of volunteer hours (over 75,000 from one application development company alone) over the past 13 years, and relies on numerous other Apache projects. The presenter will share examples of one service provider’s approach to making community contributions, including those of individuals as well as company-wide initiatives that have worked well over the years.

The fashion industry works to meet the demand for apparel and dictates thentrends for what should be worn.nThis presentation will describe why Apache OFBiz is a great platform fornbuilding an ERP for the fashion industry and how it can be enhanced tonsupport its specific requirements. The various implementation steps of anconcrete project will be reviewed:n- Development of a custom eCommerce/B2B Responsive and Seo compliantnapplication using the Color-Size matrix feature.n- Integration of third-party applications such as retail, legacy Erp,nsocial network, WMS, Business Intelligence, mailing system.n- ‘Soft migration’. Progressive replacement of the external proprietarynsystem with OfBiz. The result is an OfBiz-centric, holonic Erp system.

Cloud computing greatly simplifies provisioning, scaling, reliability and maintenance: these aspects are particularly important for Enterprise applications like Apache OFBiz. This talk will provide effective tips, based on experience and lessons learned in the field, to enable OFBiz for the cloud. Topics covered include OFBiz specific solutions for:n- deployments in AWSn- images – AMI, Dockern- continuous deploymentsn- configurations for load balancersn- session management – sticky sessions, distributed sessions (with Redis)n- database options for the cloud – RDS (AWS), Google Cloud Spanner

Apache Marvin is an open source project that focuses on empowering data science teams to deliver industrial-grade applications supported by a high-scale, low-latency, language agnostic, and standardized architecture platform created to simplify the process of exploration and modeling in Machine Learning projects. Marvin aims at abstracting the complexities in the creation process of scalable, highly available, interoperable and maintainable predictive software. In this presentation, the speaker is going to talk about the architecture behind the platform and show the cooler features of Apache Marvin.

‘Data Scientists focuses on developing ML models with applications such as TensorFlow / MXNet / Caffe / XGBoost and do not deep dive into the complexities of computing and storage to run ML/DL jobs. Today’s most of ETL processed data are stored in HDFS and cloud, and leveraging this data to design strong ML models is a big challenge for a data scientist. In Big Data ecosystem, most of the ETL and batch jobs are running on Spark and Hive which process/ingest data to the same data stores. Data scientists find this challenging in order to effectively utilize these big data workloads to develop an effective ML model. Hadoop Submarine (https://hadoop.apache.org/submarine/) helps to bring these two worlds together and provides seamless integration across. Ecosystem around Hadoop Submarine helps to design and run ML workloads from a notebook. Integrations with notebook such as Zeppelin and workflow scheduler like Azkaban, helps user to consume data from spark or hive and effectively run ML jobs with ease on any compute cluster. In this deep dive session, we will demo the simplicity of Submarine by running distributed deep learning/machine learning applications on YARN and Kubernetes as simple as running it locally. We will also showcase the community effort in developing Submarine’s ecosystem which eases integrations with Zeppelin and Azkaban. Submarine project could easily launch in the same cluster to run DL/ML jobs without any additional upgrades or complexities of having different machines.n’