Jason Bell offers an overview of a self-learning knowledge system that uses Apache Kafka and Deeplearning4j to accept data, apply training to a neural network, and output predictions. Jason covers the system design and the rationale behind it and the implications of using a streaming data with deep learning and artificial intelligence.

Distributed deep learning can increase the productivity of AI practitioners and reduce time to market for training models. Hadoop can fulfill a crucial role as a unified feature store and resource management platform for distributed deep learning. Jim Dowling offers an introduction to writing distributed DL applications, covering TensorFlow and Apache Spark frameworks that make distribution easy.

GoDaddy ingests and analyzes over 100,000 data points per second. Felix Gorodishter discusses the company's big data journey from ingest to automation, how it is evolving its systems to scale to over 10 TB of new data per day, and how it uses tools like anomaly detection to produce valuable insights, such as the worth of a reminder email.

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.

Dean Wampler compares and contrasts data processing with Akka Streams and Kafka Streams, microservice streaming applications based on Kafka. Dean discusses the strengths and weaknesses of each tool for particular design needs and contrasts them with Spark Streaming and Flink, so you'll know when to choose them instead.

Real-time processing allows you to act faster and SQL allows you to construct flows quicker and reuse existing skills. Apache Kafka is a key component but how do you peek into the data, the topologies …

With the rise of large-scale real-time computation, there is a growing need to link legacy MySQL systems with real-time platforms. Henry Cai and Yi Yin offer an overview of WaterMill, Pinterest's continuous DB ingestion system for streaming SQL data into near-real-time computation pipelines to support dynamic personalized recommendations and search indices.

Holden Karau and Rachel Warren explore the state of the current big data ecosystem and explain how to best work with it in non-JVM languages. While much of the focus will be on Python + Spark, the talk will also include interesting anecdotes about how these lessons apply to other systems (including Kafka).

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

Join Dean Wampler and Boris Lublinsky to learn how to build two microservice streaming applications based on Kafka using Akka Streams and Kafka Streams for data processing. You'll explore the strengths and weaknesses of each tool for particular design needs and contrast them with Spark Streaming and Flink, so you'll know when to choose them instead.

LinkedIn runs more than 1,800+ Kafka brokers that deliver more than two trillion messages a day. Running Kafka at such a scale makes automated operations a necessity. Jiangjie Qin shares lessons learned from operating Kafka at scale with minimum human intervention.

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.

Dean Wampler and Boris Lublinsky walk you through building several streaming microservices applications based on Kafka using Akka Streams and Kafka Streams for data processing. You'll explore the strengths and weaknesses of each tool, helping you choose the best tools for your needs, and contrast them with Spark Streaming and Flink, so you can determine when to choose them instead.

Hyperledger Fabric has a very comprehensive design and is suitable for cross-enterprise business networks. But there are still challenges when it comes to deployment in a production environment, espec …