FLaNK Stack with Flink For Streaming Use Cases
Channel: The ASF
Date: 2021-08-22

Today, data is being generated from devices and containers living at the edge of networks, clouds and data centers. We need to run business logic, analytics and deep learning at the edge before we start our real-time streaming flows. Fortunately using all the Apache FLaNK and FLiP stacks we can do this with ease! Streaming AI Powered Analytics From the Edge to the Data Center is now a simple use case. With MiNiFi we can ingest the data, do data checks, cleansing, run machine learning and deep learning models and route our data in real-time to Apache NiFi and Apache Pulsar for further transformations and processing. Apache Flink will provide our advanced streaming capabilities fed real-time via Apache Pulsar topics. Apache MXNet models will run both at the edge and in our data centers via Apache NiFi and MiNiFi.

Tools: Apache Flink, Apache Pulsar, Apache NiFi, MiNiFi, Apache MXNet

Speakers:
Timothy Spann: Tim Spann is a Developer Advocate @ StreamNative where he works with Apache Pulsar, MiniFi, Apache NiFi, Apache Flink, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal Field Engineer @ Cloudera, Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.

https://www.datainmotion.dev/p/about-me.html

https://dzone.com/users/297029/bunkertor.html

https://dev.to/tspannhw