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TOP PYSPARK INTERVIEW QUESTION 2023

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What is Apache Spark and how does it differ from Hadoop? What are the benefits of using Spark over MapReduce? What is a Spark RDD and what operations can be performed on it? How does Spark handle fault-tolerance and data consistency? Explain the difference between Spark transformations and actions. What is a Spark DataFrame and how is it different from an RDD? What is Spark SQL and how does it work? How can you optimize a Spark job to improve its performance? How does Spark handle memory management and garbage collection? Explain the role of Spark Driver and Executors. What is PySpark and how does it differ from Apache Spark? How do you create a SparkContext in PySpark? What is the purpose of SparkContext? What is RDD (Resilient Distributed Dataset)? How is it different from DataFrame and Dataset? What are the different ways to create RDD in PySpark? What is the use of persist() method in PySpark? How does it differ from cache() method? What is the use of broadcast variables in PySpark
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Top 40 SQL Query Interview Questions and Answers for Practice

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  Top 40 SQL Query Interview Questions and Answers for Practice Hello friends! in this post, we will see some of the most common SQL queries asked in interviews. Whether you are a DBA, developer, tester, or data analyst, these SQL query interview questions and answers are going to help you. In fact, I have been asked most of these questions during interviews in the different phases of my career. If you want to skip the basic questions and start with some tricky SQL queries then you can directly move to our SQL queries interview questions for the experienced section.  Consider the below two tables for reference while trying to solve the SQL queries for practice . Table – EmployeeDetails EmpId FullName ManagerId DateOfJoining City 121 John Snow 321 01/31/2014 Toronto 321 Walter White 986 01/30/2015 California Table – EmployeeSalary EmpId Project Salary Variable 121 P1 8000 500 321 P2 10000 1000 Content SQL Query Interview Questions for Freshers SQL Query Interview Questions f
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TOP 50 AWS Glue Interview Questions

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  What is AWS Glue? AWS Glue helps in preparing data for Analysis by automated extract, transforming, and loading ETL processes. It supports MySQL, Microsoft SQL Server, PostgreSQL Databases which runs on Amazon EC2(Elastic Compute Cloud) Instances in an Amazon VPC(Virtual Private Cloud). AWS Glue is an extracted, loaded, transformed service which helps in automating time-consuming steps of Data Preparation for the analytics. What are the Benefits of AWS Glue? Benefits of AWS Glue are as follows: Fault Tolerance - AWS Glue is retrievable and the logs can be debugged. Filtering - AWS Glue uses filtering for bad data. Maintenance and Development - AWS Glue uses maintenance and deployment as the service is managed by AWS. What are the components used by AWS Glue? AWS Glue consists of: Data Catalog is a Central Metadata Repository. ETL Engine helps in generating Python and Scala Code. Flexible Scheduler helps in handling Dependency Resolution, Jo
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Spark SQL “case when” and “when otherwise”

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  Like SQL “case when”  statement  and Swith statement from popular programming languages, Spark SQL Dataframe also supports similar syntax using “when otherwise” or we can also use “case when” statement. So let’s see an example on how to check for multiple conditions and replicate SQL CASE statement. Using “when otherwise” on DataFrame. Using “case when” on DataFrame. Using && and || operator First Let’s do the imports that are needed and create spark context and DataFrame. import org . apache . spark . sql . functions . { when , _ } val spark : SparkSession = SparkSession . builder ( ) . master ( "local[1]" ) . appName ( "SparkByExamples.com" ) . getOrCreate ( ) import spark . sqlContext . implicits . _ val data = List ( ( "James" , "" , "Smith" , "36636" , "M" , 60000 ) , ( "Michael" , "Rose" , "" , "40288" , &q
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Top Hive Commands with Examples

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      In this blog post, let’s discuss top Hive commands with examples: If you are familiar with SQL, it’s a cakewalk. Many users can simultaneously query the data using Hive-QL. What is HQL? Hive defines a simple SQL-like query language to querying and managing large datasets called Hive-QL ( HQL ). It’s easy to use if you’re familiar with SQL Language. Hive allows programmers who are familiar with the language to write the custom MapReduce framework to perform more sophisticated analysis. Components of Hive: Metastore : Hive stores the schema of the Hive tables in a Hive Metastore. Metastore is used to hold all the information about the tables and partitions that are in the warehouse. By default, the metastore is run in the same process as the Hive service and the default Metastore is DerBy Database. SerDe : Serializer, Deserializer gives instructions to hive on how to process a record. Hive Commands : Data Definition Language (DDL ) DDL statements are used to
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SPARK : Ways to Rename column on Spark DataFrame

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We often need to rename one or multiple columns on Spark DataFrame, Especially when a column is nested it becomes complicated. Let’s discuss all possible ways to rename column with Scala examples. Though we have covered most of the examples in Scala here, the same concept can be used in PySpark to rename a DataFrame column (Python Spark). Using withColumnRenamed – To rename Spark DataFrame column name Using withColumnRenamed – To rename multiple columns Using StructType – To rename nested column on Spark DataFrame Using Select – To rename nested columns Using withColumn – To rename nested columns Using col() function – To Dynamically rename all or multiple columns Using toDF() – To rename all or multiple columns First, let’s create our data for our examples, we are using Row class as we convert this data to Spark DataFrame. val data = Seq ( Row ( Row ( "James " , "" , "Smith" ) , "36636" , "M" , 3000 ) , Row ( R
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