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.
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.
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.
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.
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.
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