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SQL Interview Success: Unlocking the Top 5 Frequently Asked Queries

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 Here are the five top commonly asked SQL queries in the interviews. These you can expect in Data Analyst, or, Data Engineer interviews. Top SQL Queries for Interviews 01. Joins The commonly asked question pertains to providing two tables, determining the number of rows that will return on various join types, and the resultant. Table1 -------- id ---- 1 1 2 3 Table2 -------- id ---- 1 3 1 NULL Output ------- Inner join --------------- 5 rows will return The result will be: =============== 1  1 1   1 1   1 1    1 3    3 02. Substring and Concat Here, we need to write an SQL query to make the upper case of the first letter and the small case of the remaining letter. Table1 ------ ename ===== raJu venKat kRIshna Solution: ========== SELECT CONCAT(UPPER(SUBSTRING(name, 1, 1)), LOWER(SUBSTRING(name, 2))) AS capitalized_name FROM Table1; 03. Case statement SQL Query ========= SELECT Code1, Code2,      CASE         WHEN Code1 = 'A' AND Code2 = 'AA' THEN "A" | "A
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AWS CLI PySpark a Beginner's Comprehensive Guide

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AWS (Amazon Web Services) and PySpark are separate technologies, but they can be used together for certain purposes. Let me provide you with a beginner's guide for both AWS and PySpark separately.

PySpark


AWS (Amazon Web Services):

Amazon Web Services (AWS) is a cloud computing platform that offers a wide range of services for computing power, storage, databases, machine learning, analytics, and more.

1. Create an AWS Account:

Go to the AWS homepage.

Click on "Create an AWS Account" and follow the instructions.

2. Set Up AWS CLI:

Install the AWS Command Line Interface (AWS CLI) on your local machine. Configure it with your AWS credentials using AWS configure.

3. Explore AWS Services:

AWS provides a variety of services. Familiarize yourself with core services like EC2 (Elastic Compute Cloud), S3 (Simple Storage Service), and IAM (Identity and Access Management).

PySpark:

PySpark is the Python API for Apache Spark, a fast and general-purpose cluster computing system. It allows you to write Spark applications using Python.

1. Install PySpark:

pip install pyspark

2. Create a SparkSession:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("example").getOrCreate()

3. Load Data:

# Read from a CSV file

df = spark.read.csv("s3://your-s3-bucket/your-file.csv", header=True, inferSchema=True)

4. Perform Operations:

# Show the first few rows of the DataFrame

df.show()

# Perform transformations

df_transformed = df.select("column1", "column2").filter(df["column3"] > 10)

# Perform actions

result = df_transformed.collect()

5. Write Data:

# Write to Parquet format

df_transformed.write.parquet("s3://your-s3-bucket/output/parquet_data")

Combining AWS and PySpark:

  • If you want to use PySpark on AWS, you can leverage services like Amazon EMR (Elastic MapReduce), a cloud-based big data platform. It allows you to easily deploy and scale Apache Spark and Hadoop clusters.
  • Create an EMR cluster using the AWS Management Console or AWS CLI. Submit PySpark jobs to the cluster. Remember to check the documentation for both AWS and PySpark for more detailed information and examples.

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