IoT Live Demo – 100.000 Connected Cars with Kubernetes, Kafka, MQTT, TensorFlow
Kai Waehner
Live Demo – 100.000 Connected Cars – Real Time Processing and Analytics with Kubernetes, Kafka, MQTT and TensorFlow leveraging Confluent and HiveMQ.
Browsing Tag
Real Time
11 posts
Deep Learning in Real Time with TensorFlow, H2O.ai and Kafka Streams (Slides from JavaOne 2017)
Apache Kafka + Kafka Streams to Produductionize Neural Networks (Deep Learning). Models built with TensorFlow, DeepLearning4J, H2O. Slides from JavaOne 2017.
Apache Kafka Streams + Machine Learning (Spark, TensorFlow, H2O.ai)
Apache Kafka Streams to build Real Time Streaming Microservices. Apply Machine Learning / Deep Learning using Spark, TensorFlow, H2O.ai, etc. to add AI. Embed Kafka Streams into Java App, Docker, Kubernetes, Mesos, anything else.
Comparison: Data Preparation vs. Inline Data Wrangling in Machine Learning and Deep Learning Projects
Data Preparation: Comparison of Programming Languages, Frameworks and Tools for Data Preprocessing and (Inline) Data Wrangling in Machine Learning / Deep Learning Projects.
Machine Learning Applied to Microservices
Build intelligent Microservices by applying Machine Learning and Advanced Analytics. Leverage Apache Hadoop / Spark with Visual Analytics and Stream Processing.
Characteristics of a Good Visual Analytics and Data Discovery Tool
Several tools are available on the market for Visual Analytics and Data Discovery. Three of the most well known options are Tableau, Qlik and TIBCO Spotfire. This post shows important characteristics to compare and evaluate these tools.
Streaming Analytics with Analytic Models (R, Spark MLlib, H20, PMML)
Closed Big Data Loop: 1) Finding Insights with R, H20, Apache Spark MLlib, PMML and TIBCO Spotfire. 2) Putting Analytic Models into Action via Event Processing and Streaming Analytics.
Difference between a Data Warehouse and a Live Datamart?
Data Warehouses have existed for many years in almost every company. While they are still as good and relevant for the same use cases as they were 20 years ago, they cannot solve new, existing challenges and those sure to come in a ever-changing digital world. The upcoming sections will clarify when to still use a Data Warehouse and when to use a modern Live Datamart instead.
Comparison of Stream Processing and Streaming Analytics Alternatives (Apache Storm, Spark, IBM InfoSphere Streams, TIBCO StreamBase, Software AG Apama)
The article discusses what stream processing is, how it fits into a big data architecture with Hadoop and a data warehouse (DWH), when stream processing makes sense, and what technologies and products you can choose from. Comparison of open source and proprietary stream processing / streaming analytics alternatives: Apache Storm, Spark, IBM InfoSphere Streams, TIBCO StreamBase, Software AG’s Apama, etc.
Fundamentals of Stream Processing (IBM InfoSphere Streams, TIBCO StreamBase, Apache Storm) – Book Review
Internet of things, cloud and mobile are the major drivers for stream processing. Use cases are network monitoring, intelligent surveillance, but also less technical things such as inventory management or fraud detection. The book helps a lot to get a basic understanding about history, concepts and patterns of the stream processing paradigm.
