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Data Analysis with Apache Spark - Pyspark Assignment Help


In this assignment, we will be using Apache Spark to perform data analysis. Apache Spark is a powerful framework for big data processing and analytics, capable of handling large datasets and performing distributed computations. By leveraging Spark's capabilities, we can efficiently analyze data and extract valuable insights. Throughout this assignment, we will use either Java, Scala, or Python to run Spark queries, and we will include screenshots of the queries and their results. We will also utilize either Azure HDInsight or a local installation of Bitnami Hadoop, Hortonworks, or Cloudera Hadoop distribution for this assignment.

Part 1: Word Count Analysis

  1. Connect to the Cluster: Start by connecting to the Apache Spark cluster, ensuring that you have the necessary environment set up to run Spark commands.

  2. Verify Spark and HDFS Commands: Open a command line interface and test if the 'spark' and 'hdfs' commands are functioning correctly. This ensures that the necessary tools are properly installed and configured.

  3. Perform Word Count Analysis: a. Prepare Sample Data: Put the sample data from Week 5 Paper 2, which involves counting the occurrences of the word "Sentence," into a text file. The sample data can include sentences such as:

  • "This is test sentence number one."

  • "This is test sentence number 2."

  • "This is test sentence number three."

  • "This is sentence no 4."

  • "sentence 5."

b. Upload File to HDFS: Use the HDFS commands to upload the text file containing the sample data into HDFS. This step ensures that the data is accessible for Spark analysis.

c. Run Spark Transformations and Actions: Use Spark CLI (spark-shell or pyspark) or a Zeppelin notebook to execute Spark transformations and actions. Implement operations such as filter, map, and reduce to count the number of times the word "Sentence" appears in the file. This analysis will provide insights into the frequency of the word "Sentence" within the dataset.

Part 2: Baseball Data Analysis

  1. Upload Baseball Data: Using the HDFS commands, upload the Baseball data files into an HDFS folder, such as /temp or /tmp. This step ensures that the data is available for Spark analysis.

  2. Answer Questions Using Spark CLI or Notebook: Use the Spark command-line interface (CLI) or a Jupyter or Zeppelin Notebook to answer the following questions about the Baseball data: a. Total Number of Baseball Players: Determine the total number of baseball players present in the dataset. This calculation will provide an overall count of the players.

b. Number of Players Born Before 1960: Find the count of players who were born before the year 1960. This analysis will help identify the number of older players in the dataset.

c. Number of Players Born in or After 1960: Determine the count of players who were born in or after the year 1960. This analysis will provide insights into the number of younger players in the dataset.

d. Number of Players Born Outside of the USA: Identify the count of players who were born outside of the USA. This analysis will help determine the representation of international players in the dataset.

e. Number of Players Born in the USA: Find the count of players who were born in the USA. This analysis will help identify the representation of US-born players in the dataset.

  1. Utilize Spark Actions: Explain the output of the following Spark actions utilized in the analysis: a. collect: This action collects all the elements of a distributed dataset (RDD) into an array or list on the driver program. It returns an array containing all the elements present in the RDD.

b. take(3): This action retrieves the first three elements from an RDD. It returns an array containing the specified number of elements from the RDD.

c. distinct: This action eliminates duplicate elements from an RDD. It returns a new RDD containing only the unique elements.

Capture Screenshots and Provide Write-Up Capture screenshots of the command execution and the corresponding results for each step in the assignment. Ensure that the screenshots are in .jpg, .gif, or .pdf format. Provide a write-up detailing the commands used and the results obtained in each part of the assignment. The write-up should explain the purpose and significance of each command or action taken. Remember to adhere to the file format requirements, using a .doc or .docx extension for the assignment document.

By utilizing Apache Spark for data analysis, we can extract valuable insights and perform complex computations on large datasets. In this assignment, we focused on two parts: Word Count Analysis and Baseball Data Analysis. Through Spark transformations and actions, we counted the occurrences of the word "Sentence" in a sample dataset and answered various questions about baseball player data. The screenshots and write-up provided in the assignment document demonstrate the execution of the commands and the obtained results.

If you require assistance or solutions for the projects mentioned above, please feel free to contact us at CodersArts. Our team of experts specializes in machine learning, data analysis, and Apache Spark, and we are here to help you optimize your data-driven business processes. Reach out to us via email or through our website, and let us revolutionize your data analysis endeavors.

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