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Stream processing allows us to process data in real time as they arrive and quickly detect conditions within small time.

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In the point of performance, the latency of batch processing will be in minutes to hours while the latency of stream processing will be in seconds or milliseconds. It is responsible for creating the batch view from the master data set stored in the Hadoop distributed file system HDFS. It might take a large amount of time for that file to be processed, for this reason, we also have the real-time processing layer.

Once the computed view from batch and speed layers are stored in the Cassandra database, we have created a Spring Boot application which response to ad-hoc queries by returning pre-computed views in a dashboard that is automatically updated using Web socket to push the most updated report to the UI.

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Using the Spark streaming API, we processed and analysed IoT data events and transformed them into vehicle count for different types of vehicles on different routes. We performed a series of stateless and stateful transformation using Spark streaming API on streams and persisted them to Cassandra database tables.

In order to get accurate views, we also process a batch processing creating a batch view into Cassandra. We developed responsive web traffic monitoring dashboard using Spring Boot, SockJs and Bootstrap which merge two views from the Cassandra database before pushing to the UI using web socket. Github project. If you liked this article, please clap and follow me to read my future content.

Sign in. Get started. Lambda architecture— how to build a Big data pipeline part 1. Check out this open source project to get a start on your lambda architecture, learn about the tools you need to build your Big data pipeline, and more.


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Alexsandro Souza Follow. Internet of Things IoT is an emerging disruptive technology and becoming an increasing topic of interest.

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