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Unit 10 Assignment: Further Exploration of the Hadoop Environment
Outcomes addre
Unit 10 Assignment: Further Exploration of the Hadoop Environment
Outcomes addressed in this activity:
Unit Outcomes:
Migrate an unstructured data file from a local file system to the Apache Hadoop Distributed File System (HDFS).
Transform data using Apache Hive’s flexible SerDes (serializers/deserializers) to parse the log data into individual fields using a regular expression.
Perform data analysis using Apache Hive.
Course Outcome:
IT350-6: Explore non-relational database alternatives.
Purpose
In the modern world of big data, unstructured data is the most abundant. It is so prolific because unstructured data could be anything: media, imaging, audio, sensor data, text data, and much more. Unstructured simply means that datasets (typical large collections of files) are not stored in a structured database format. Unstructured data has an internal structure, but it is not predefined through data models. It might be human-generated or machine-generated in a textual or a non-textual format.
You will migrate a log file containing unstructured web clickstream data to the Apache Hadoop Distributed File System (HDFS). You will then transform the unstructured data to individual fields through the use of Apache Hive’s flexible SerDes (serializers/deserializers) functionality. You will complete the lab by performing basic data analysis by querying the migrated and transformed data in Apache Hive. Apache Hive is a data warehouse software project built on top of Apache Hadoop, providing data query and analysis capabilities.Assignment Instructions
Navigate to the Academic Tools area of this course and select Library, then Required Readings to review the Unit 10 videos covering facets associated with Hadoop. It is very important that you watch the Unit 10 videos before completing the assignment.
The assignment work will be performed within Codio’s cloud-based learning environment. Navigate to this course’s main menu and select Codio to access this platform.
Your course instructor will provide you with the Codio connection details for accessing the specific online lab environment. The lab environment consists of a Linux virtual machine that has MySQL, Apache Hadoop, and Apache Hive. The work will be performed using a command line interface (CLI) within a Linux Terminal window.
Complete Lab Exercise 2 (starts on page 12) contained in the following lab document:
IT350 Codio Big Data Labs
In a Microsoft Word document, describe your experience of completing this lab exercise in 250–300 words.
In addition to the Word document, you are required to provide the screen.log file and a comma separated value (CSV) file as part of the assignment submission. Details on the screen.log and CSV files are contained in the lab document. The submitted screen.log and CSV files provide verification of the completed lab work.
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