Data Engineer PySpark Data Bricks Session Day 3

📍 𝐒𝐭𝐚𝐫𝐭 𝐚 𝐒𝐩𝐚𝐫𝐤 𝐒𝐞𝐬𝐬𝐢𝐨𝐧 : Set up the PySpark environment.
🧣 𝐂𝐫𝐞𝐚𝐭𝐞 𝐚 𝐋𝐢𝐬𝐭 : Define the list with three elements.
📢 𝐏𝐚𝐫𝐚𝐥𝐥𝐞𝐥𝐢𝐳𝐞 𝐭𝐡𝐞 𝐋𝐢𝐬𝐭 : Distribute the list across the cluster nodes.
🔔 𝐂𝐨𝐧𝐯𝐞𝐫𝐭 𝐭𝐨 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞 : Convert the distributed RDD to a DataFrame.
🔋 𝐏𝐞𝐫𝐟𝐨𝐫𝐦 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 : Show the contents and perform any desired operations.

📍 This video will explain how to write first program in PySpark.

📢 Video Link: https://lnkd.in/gmE_dAcG

Code Source Link:
https://lnkd.in/g67a4kY3

𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐒𝐭𝐞𝐩𝐬 :

𝐄𝐱𝐩𝐥𝐚𝐧𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐭𝐡𝐞 𝐂𝐨𝐝𝐞 :

𝟏. 𝐒𝐩𝐚𝐫𝐤 𝐒𝐞𝐬𝐬𝐢𝐨𝐧 : The SparkSession is created to provide an entry point for Spark functionality.
𝟐. 𝐋𝐢𝐬𝐭 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧 : A list of three elements is defined.
𝟑. 𝐏𝐚𝐫𝐚𝐥𝐥𝐞𝐥𝐢𝐳𝐞 : The list is parallelized with numSlices=3, which ensures that each element is assigned to a different partition in the RDD. This is how we can distribute it across the three nodes.
𝟒. 𝐂𝐨𝐧𝐯𝐞𝐫𝐭 𝐭𝐨 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞 : The RDD is mapped to a tuple format to convert it into a DataFrame. The column is named "element".
𝟓. 𝐃𝐢𝐬𝐩𝐥𝐚𝐲 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞 : The contents of the DataFrame are printed using df.show(), which will display each element as a separate row.
𝟔. 𝐂𝐨𝐮𝐧𝐭 : The total number of elements is counted and printed.
𝟕. 𝐅𝐮𝐫𝐭𝐡𝐞𝐫 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 : An optional step is included to filter the DataFrame for elements containing "1" and display the result.
𝟖. 𝐒𝐭𝐨𝐩 𝐒𝐩𝐚𝐫𝐤 𝐒𝐞𝐬𝐬𝐢𝐨𝐧 Finally, the Spark session is stopped to release resources.

Accredian Boston University ABES Engineering College Duke-NUS Medical School MIT Professional Education freeCodeCamp Great Learning HEC Paris Indian Institute of Science (IISc) Jefferson Health KU Leuven Lyft Microsoft Databricks DataMites™ Datafoundry Data Analytics Data Makers Fest The Sparks Foundation Spark Spark Foundry
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