Python Tips
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How to wrangle log data with Python and Apache Spark
One of the most popular and effective enterprise use-cases which leverage analytics today is log analytics. Nearly every organization today has multiple systems and infrastructure running day in and day out. To effectively keep their business running, these organizations need to know if their infrastructure is performing to its maximum potential. Finding out involves analyzing system and application logs and maybe even applying predictive analytics on log data. The amount of log data involved is typically massive, depending on the type of organizational infrastructure involved and applications running on it.
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How to analyze log data with Python and Apache Spark
In part one of this series, we began by using Python and Apache Spark to process and wrangle our example web logs into a format fit for analysis, a vital technique considering the massive amount of log data generated by most organizations today. We set up environment variables, dependencies, loaded the necessary libraries for working with both DataFrames and regular expressions, and of course loaded the example log data. Then we wrangled our log data into a clean, structure, and meaningful format. Here in part two, we focus on analyzing that data.
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