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Mettre en place un Data Catalog

on February 26, 2025

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Spark Structured Streaming with Kafka: Understanding the startingOffset=“earliest” Issue

Spark Structured Streaming with Kafka: Understanding the startingOffset=“earliest” Issue Spark Structured Streaming is a powerful tool for processing large volumes of data in real-time. However, many data scientists have encountered an issue where the startingOffset="earliest" setting is not honored when using Kafka as a source. This blog post will delve into this issue, providing a comprehensive understanding and potential solutions. Understanding the Issue When using Spark Structured Streaming with Kafka, the startingOffset option allows you to specify where Spark should start reading data. The two options are "latest", which reads only new data, and "earliest", which reads from the beginning of the topic. However, some users have reported that Spark does not always honor the "earliest" setting. Instead of starting from the beginning, Spark starts reading from the latest offset, leading to data loss. Read more : https://saturncloud.io...

Dive Deeper into Data Engineering on Databricks

Bayes Rule Applied : a clear example

Bayes’ Rule Applied Using Bayesian Inference on a real-world problem The fundamental idea of Bayesian inference is to become “less wrong” with more data. The process is straightforward: we have an initial belief, known as a prior, which we update as we gain additional information. Although we don’t think about it as Bayesian Inference, we use this technique all the time. For example, we might initially think there is a 50% chance we will get a promotion at the end of the quarter. If we receive positive feedback from our manager, we adjust our estimate upwards, and conversely, we might decrease the probability if we make a mess with the coffee machine. As we continually gather information, we refine our estimate to get closer to the “true” answer. Read more: https://towardsdatascience.com/bayes-rule-applied-75965e4482ff

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