Data Engineer PySpark Data Bricks Session Day 9
This video will guide you through writing your first program in PySpark 🐍, accessing Big Data 📊 ranging from MBs to TBs, using the glom() function 🔍, converting data from non-partitioned to partitioned formats 📂, executing the parallelize() function in Spark 🚀, and using persist(), repartition(), and other DataFrame functions to write data in various file formats 📑.
📍 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.
In this video we will learn:
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🚀 *Course:* Master Azure Data Engineering
📅 *Last Date:* 15 Jan 2025
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2:00 Work on Text File in PySpark
6:30 Value column in Word or Text File in PySpark
8:41 User Defined Functions udf() in PySpark using single argument
21:33 User Defined Functions udf() in PySpark using single argument
26:33 User Defined Functions udf() in PySpark using single argument
30:44 cache() in PySpark
38:22 Convert data from dataframe to RDD in PySpark
43:33 SQL query in Spark
48:35 Write DataFrame in different file in PySpark
📍 📢 Day 7 Video Link: • Data Engineer PySpark Data Bricks Session ...
Introduction to Big Datasets
Mounting Google Drive to Access Big Data in Zip Format
Importing Spark Libraries
Identifying Partitions in Cache
Using the Glom Function in PySpark
Applying Repartition and Partition Functions to a Custom Dataset
Understanding Parallelism and Limitations of SQL with Big Data
Using the Persist Function in PySpark
Repartition Function in PySpark
LinkedIn Profile of author:
/ sachin-saxena-graphic-designer
Code Source Link:
https://lnkd.in/g67a4kY3
1:15 Description of Big Dataset
6:55 Mount Google Drive to access Big Data which in zip format
11:55 import Spark libraries
16:22 How many partitions are there in my Cache?
18:40 Glom function in Pyspark
21:00 Check repartition and partition function over custom dataset
24:00 Why Parallelism and why SQL got failed to operand Big Data
43:00 Persist function in PySpark
49:11 repartition function in PySpark
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.
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