![]() ![]() Our starting position is a simple Kafka producer producing data every two seconds of a truck driving from Hamburg to Munich in Germany.Here is what I am trying to make the code do: For example, the Kafka topic is in JSON value format so that we do not need a schema registry. Prerequisites: docker, docker-compose, ( MongoDB Compass)įor configurations, we focus on simplicity so that our settings here can be used as a baseline for similar projects. ![]() ![]() On GitHub, you can find all code for the Mongo DB and rkafka pipelines. We also highlight the advantages and drawbacks of each approach (with MongoDB and without MongoDB). Using the other method, we consume the data directly using the rkafka package. ![]() In one method, we use MongoDB as a layer in between, and then we use the R package mongolite to request the data. In this tutorial, I’ll explain two ways to create data pipelines from Apache Kafka ® into RStudio. However, the statistical software R also provides deep statistical libraries, and it is my personal first choice when analyzing data. Python, with its Jupyter Notebooks, is commonly used for descriptive analytics. In a second article, we’ll talk about running your model on real-time data. In this blog post, we focus exactly on this crucial step: retrieving the data. A key point for every data scientist is not just the mathematical skills themselves, but also how to get the data into your analytics program. Then you transfer it and use it in your running system. You start with analyzing historical data to gain insights, find correlations, and finally develop and optimize your model. Overall, these can be seen as one process. In Data Science projects, we distinguish between descriptive analytics and statistical models running in production. ![]()
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