Cloud Foundry is an integral part of the movement creating interoperability among the open source, cloud-native ecosystem. Complementary, interlocking open source technologies like Cloud Foundry, Kube …

ML researchers spend too much time building infrastructure to support their work. Kubeflow aims to solve that by using Kubernetes to build an open, scalable, and extensible platform for ML. Since our …

Abhishek Kumar and Vijay Srinivas Agneeswaran offer an introduction to deep learning-based recommendation and learning-to-rank systems using TensorFlow. You'll learn how to build a recommender system based on intent prediction using deep learning that is based on a real-world implementation for an ecommerce client.

When Kafka stream processing pipelines fail, they can leave users panicked about data loss when restarting their application. Jordan Hambleton and Guru Medasani explain how offset management provides users the ability to restore the state of the stream throughout its lifecycle, deal with unexpected failure, and improve accuracy of results.

Data scientists, machine learning engineers, and researchers are under increasing pressure to provide explanations for how they are processing and managing user data. In particular, the EU's GDPR regu …

Sergey Ermolin and Suqiang Song demonstrate how to use Spark BigDL wide and deep and neural collaborative filtering (NCF) algorithms to predict a users probability of shopping at a particular offer merchant during a campaign period. Along the way, they compare the deep learning results with those obtained by MLlibs alternating least squares (ALS) approach.

How do you scale geospatial analytics on big data? And while you're at it, can you make it easy to use while achieving state-of-the-art performance on a single node? Ram Sriharsha offers an overview of Magellana geospatial optimization engine that seamlessly integrates with Sparkand explains how it provides scalability and performance without sacrificing simplicity.

As organizations look to scale their analytics capability, the need to grow beyond a traditional data warehouse becomes critical, and cloud-based solutions allow more flexibility while being more cost efficient. Billy Liu offers an overview of Kyligence Cloud, a managed Apache Kylin online service designed to speed up mission-critical analytics at web scale for big data.

Tim Berglund leads a basic architectural introduction to Kafka and walks you through using Kafka Streams and KSQL to process streaming data.

Kai Whner shares a highly scalable, mission-critical infrastructure using Apache Kafka and Apache Mesos: Kafka brokers are used as the distributed messaging backbone; Kafkas Streams API embeds stream processing into any external application without the need for a dedicated streaming cluster; and Mesos is used as a scalable infrastructure to leverage the benefits of a cloud-native platform.

Steven Wu explains how Netflixs SPaaS platform empowers users to focus on extracting insights from data streams and build stream processing applications and shares lessons learned building and operating the largest SPaaS use case: Netflixs Keystone data pipeline, a self-serve platform for creating near-real-time event pipelines that processes three trillion events and 12 PB of data every day.

Mario-Leander Reimer explores key JEE technologies that can be used to build JEE-powered data services and walks you through implementing the individual data processing tasks of a simplified showcase application. You'll then deploy and orchestrate the individual data services using OpenShift, illustrating the scalability of the overall processing pipeline.

A public API is a new type of service that extends the business model beyond traditional boundaries. Tom Hofte and Marco van der Linden walk you through designing a resource model for a public API. You'll then work in teams to design an API for a fictional case study.