CCA175 Exam Dumps : CCA Spark and Hadoop Developer Exam

Solution :

Step 1 : Go to hive interface and create database.

hive

create database hadoopexam;

Step 2. Use the database created in above step and then create table in it. use hadoopexam; show tables;

Step 3 : Create table in it.

create table departments (department_id int, department_name string);

show tables;

desc departments;

desc formatted departments;

Step 4 : Please check following directory must not exist else it will give error, hdfs dfs -Is /user/cloudera/departments

If directory already exists, make sure it is not useful and than delete the same.

This is the staging directory where Sqoop store the intermediate data before pushing in hive table.

hadoop fs -rm -R departments

Step 5 : Now import data in existing table

sqoop import \

-connect jdbc:mysql://quickstart:3306/retail_db \

~username=retail_dba \

-password=cloudera \

--table departments \

-hive-home /user/hive/warehouse \

-hive-import \

-hive-overwrite \

-hive-table hadoopexam.departments

Step 6 : Check whether data has been loaded or not.

hive;

use hadoopexam;

show tables;

select" from departments;

desc formatted departments;

Step 7 : Import data in non-existing tables in hive and create table while importing.

sqoop import \

-connect jdbc:mysql://quickstart:3306/retail_db \

--username=retail_dba \

~password=cloudera \

-table departments \

-hive-home /user/hive/warehouse \

-hive-import \

-hive-overwrite \

-hive-table hadoopexam.departments_new \

-create-hive-table

Step 8 : Check-whether data has been loaded or not.

hive;

use hadoopexam;

show tables;

select" from departments_new;

desc formatted departments_new;

Solution :

Step 1 : Select Maximum, minimum, average , Standard Deviation, and total quantity.

val results = sqlContext.sql('.....SELECT MAX(price) AS MAX , MIN(price) AS MIN , AVG(price) AS Average, STD(price) AS STD, SUM(quantity) AS total_products FROM products......)

results. showQ

Step 2 : Select minimum and maximum price for each product code.

val results = sqlContext.sql(......SELECT code, MAX(price) AS Highest Price', MIN(price) AS Lowest Price'

FROM products GROUP BY code......)

results. showQ

Step 3 : Select Maximum, minimum, average , Standard Deviation, and total quantity for each product code, hwoever make sure Average and Standard deviation will have maximum two decimal values.

val results = sqlContext.sql(......SELECT code, MAX(price), MIN(price),

CAST(AVG(price} AS DECIMAL(7,2)) AS Average', CAST(STD(price) AS DECIMAL(7,2)) AS 'Std Dev\ SUM(quantity) FROM products

GROUP BY code......)

results. showQ

Step 4 : Select all the product code and average price only where product count is more than or equal to 3.

val results = sqlContext.sql(......SELECT code AS Product Code',

COUNTf) AS Count',

CAST(AVG(price) AS DECIMAL(7,2)) AS Average' FROM products GROUP BY code

HAVING Count >=3"M") results. showQ

Step 5 : Select maximum, minimum , average and total of all the products for each code. Also produce the same across all the products.

val results = sqlContext.sql( """SELECT

code,

MAX(price),

MIN(pnce),

CAST(AVG(price) AS DECIMAL(7,2)) AS Average',

SUM(quantity)-

FROM products

GROUP BY code

WITH ROLLUP""" )

results. show()

Solution :

Step 1 : Create an RDD out of this list

val rdd = sc.parallelize(List( ("Deeapak" , "male", 4000}, ("Deepak" , "male", 2000), ("Deepika" , "female", 2000),("Deepak" , "female", 2000), ("Deepak" , "male", 1000} , ("Neeta" , "female", 2000}}}

Step 2 : Convert this RDD in pair RDD

val byKey = rdd.map({case (name,sex,cost) => (name,sex)->cost})

Step 3 : Now group by Key

val byKeyGrouped = byKey.groupByKey

Step 4 : Nowsum the cost for each group

val result = byKeyGrouped.map{case ((id1,id2),values) => (id1,id2,values.sum)}

Step 5 : Save the results result.repartition(1).saveAsTextFile("spark12/result.txt")