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 :

val z = x.intersection(y)

intersection : Returns the elements in the two RDDs which are the same.

Solution :

Step 1 : Create 2 files first using Hue in hdfs.

Step 2 : Load all file as an RDD

val technology = sc.textFile(Msparkl2/technology.txt").map(e => e.splitf',"))

val salary = sc.textFile("spark12/salary.txt").map(e => e.split("."))

Step 3 : Now create Key.value pair of data and join them.

val joined = technology.map(e=>((e(0),e(1)),e(2))).join(salary.map(e=>((e(0),e(1)),e(2))))

Step 4 : Save the results in a text file as below.

joined.repartition(1).saveAsTextFile("spark12/multiColumn Joined.txt")