coding_answers

1.
select A.deparment,A.cnt 
from
   ( select department,count(*)as cnt 
    from 
    Employee 
    group by department )
    as A 
order by A.cnt desc;

2. 
select B.DepartmentName ,max(A.Unitprice) as MostExpensiveProductPrice,
from Product A
inner join department B 
on A.SepartmentID=B.ID 
group by B.DepartmentName ;

3. a)
from pyspark.sql import SparkSession
from pyspark.sql.types import *
from pyspark.sql.functions import *
#Create a SparkSession
spark = SparkSession.builder \
    .appName("Reading and calculating the Average transaction amount from the CSV file") \
    .getOrCreate()

#Read the CSV data into a DataFrame using the defined schema
df=spark.read.csv.path("path_of_file",header=True) 
df.show()  

b)
# Calculate the average transaction amount
average_transaction_amount = df.select(avg(df['TransactionAmount']).alias('AverageTransactionAmount')).collect()[0][0]
print("Average transaction amount:", average_transaction_amount)
#Stop the SparkSession
spark.stop()

4. #Approach1:
def square_function(lst): #Function to get the square of elements
    new_list = []
    new_list=[ele*ele for ele in lst] #list_comprehension
    return new_list

lst = [1, 2, 3, 4] #Given input
print(square_function(lst)) #calling function

#Approach2: 
def square_function(lst): #Function to get the square of elements
    return list(map(lambda ele: ele * ele, lst))

lst = [1, 2, 3, 4] #Given input
print(square_function(lst)) #calling function

5. 
def is_duplicate(nums): #Function to check if array has duplicates
    nums_set = set() #declaring a set
    for ele in nums: #iterating through given array
        if ele in nums_set:
            return True
        else:
            nums_set.add(ele) #if ele is not present in nums_set add the element to nums_set
    return False

nums=input("enter the array elements") #give the desired input
print(is_duplicate(nums))

6. 
Let's design the data pipeline from the start using AWS services in the following steps:

1. Data Extraction:
   >Source data is stored in a legacy database in CSV format.
   > We'll use AWS Glue for data extraction. Glue supports various data sources, including CSV files, and provides managed ETL capabilities. We can create Glue crawlers to discover the schema of the CSV files and create tables of metadata in the Data Catalog.

2. Data Transformation:
   >After extraction, the data may needs to be verified and cleaned.
   > We can utilize Glue for this data transformation. Glue will  supports Python and Scala for writing ETL scripts.
   > Transformation logic will be implemented in Glue ETL scripts this will included any filtration of unwanted records, combination of different payloads and applying necessary data transformations.

3. Data Loading:
   > The transformed data from the above step will be loaded into cloud database which is redshift for the analytics purpose
   > Glue or AWS Data Pipeline can be used to build the data loading process. This can help with automation and scheduling for ETL pipeline for loading data into the required database.

4. Batch Processing:
   > Amazon S3 can be used to stored both raw source data and transformed output data.
   > The data will be stored in partitions in S3 by export date for efficient storage and       easy retrieval.
   > Data extraction from the source system will occur overnight each night as part of a batch process.
   > We can use AWS Lambda with along CloudWatch service for scheduling the overnight batch process. Lambda functions triggered by CloudWatch Events will initiate the data extraction, transformation, and loading tasks at the specified time each night.

5. Monitoring and Logging:
   > We can use AWS CloudWatch for monitoring the health, performance, and logs of the pipeline components.

6.Security:
   > For more secure data extractions and transformation and to prevent loss of any confidential data we will encryption of data at rest and in transit, access control using AWS IAM roles and policies, and regular backups to ensure data integrity and security.

By following this design, we can implement a scalable, efficient, and reliable ETL/ELT data pipeline on AWS to meet the business requirements.