#The log files that we use for this assignment are in the (). # **Part 1: Apache Web Server Log file format** # *Part 3*: Analyzing Web Server Log File # *Part 2*: Sample Analyses on the Web Server Log File # *Part 1*: Apache Web Server Log file format This means using the various () operations along with () that are applied at each worker. For each problem, you should start by thinking about the algorithm that you will use to *efficiently* process the log in a parallel, distributed manner. #This assignment is broken up into sections with bite-sized examples for demonstrating Spark functionality for log processing. Log data comes from many sources, such as web, file, and compute servers, application logs, user-generated content, and can be used for monitoring servers, improving business and customer intelligence, building recommendation systems, fraud detection, and much more. This homework will show you how to use Apache Spark on real-world text-based production logs and fully harness the power of that data. Spark allows you to store your logs in files on disk cheaply, while still providing a quick and simple way to perform data analysis on them. It's a very large, common data source and contains a rich set of information.
#Server log analysis is an ideal use case for Spark. #This lab will demonstrate how easy it is to perform web server log analysis with Apache Spark.
# **Web Server Log Analysis with Apache Spark**
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