Predicting and Visualizing streaming Data through Python. Predicting pedestrian traffic and visualizing on a map.
Introduction to the ObjectiveThe skill to train a model on the batch/static data is quite important to have but so is the ability to apply that model on streaming data. In today’s fast world people/organizations want the responses/predictions to their queries in real-time as everyone is quite busy in their own worlds and frankly we would all agree there is a lot to do. So this post is dedicated to the group who is trying to imbibe the skill of real-time prediction. This post is in continuation to my last post where we trained the model using Apache Spark.As already briefed in the first paragraph, by now you would have gotten some idea as to what we would be discussing in this post and yes you are correct we would be working on deploying the earlier created model in real-time and then see how we can visualize the result on the map as the sensors are deployed on certain ‘locations’. For the sake of convenience, I would again share the structure of the data in this post. so that you do no have to go to the last post again.Its always good to follow a proper way, so again we would address some basic questions about the problem to solve like last time.Which tools would be used to handle the real-time streaming?As per this blog the tools used would be Apache Kafka for real-time data simulation and Apache Spark. The programming language used would be Python.
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🔥Intellipaat Kafka training: https://intellipaat.com/kafka-training-online/ 👉In this kafka spark streaming tutorial you will learn what is apache kafka, arch...
Spark Structured Streaming – Stateful Streaming. Welcome back folks to this blog series of Spark Structured Streaming. This blog is the continuation of the earlier blog "Internals of Structured Streaming".
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After the previous post wherein we explored Apache Kafka, let us now take a look at Apache Spark. This blog post covers working within Spark’s interactive shell environment, launching applications (including onto a standalone cluster), streaming data and lastly, structured streaming using Kafka. To get started right away, all of the examples will run inside Docker containers.