Cloudera CCA175 Exam With Confidence Using Practice Dumps

Solution :

Step 1 : Create directory mkdir /tmp/spooldir/bb mkdir /tmp/spooldir/dr

Step 2 : Create flume configuration file, with below configuration for

agent1.sources = source1 source2

agent1 .sinks = sink1

agent1.channels = channel1

agent1 .sources.source1.channels = channel1

agentl .sources.source2.channels = channell agent1 .sinks.sinkl.channel = channell

agent1 .sources.source1.type = spooldir

agent1 .sources.sourcel.spoolDir = /tmp/spooldir/bb

agent1 .sources.source2.type = spooldir

agent1 .sources.source2.spoolDir = /tmp/spooldir/dr

agent1 .sinks.sink1.type = hdfs

agent1 .sinks.sink1.hdfs.path = /tmp/flume/finance

agent1-sinks.sink1.hdfs.filePrefix = events

agent1.sinks.sink1.hdfs.fileSuffix = .log

agent1 .sinks.sink1.hdfs.inUsePrefix = _

agent1 .sinks.sink1.hdfs.fileType = Data Stream

agent1.channels.channel1.type = file

Step 4 : Run below command which will use this configuration file and append data in hdfs.

Start flume service:

flume-ng agent -conf /home/cloudera/flumeconf -conf-file /home/cloudera/fIumeconf/fIume7.conf --name agent1

Step 5 : Open another terminal and create a file in /tmp/spooldir/

echo "IBM,100,20160104" » /tmp/spooldir/bb/.bb.txt

echo "IBM,103,20160105" » /tmp/spooldir/bb/.bb.txt mv /tmp/spooldir/bb/.bb.txt /tmp/spooldir/bb/bb.txt

After few mins

echo "IBM,100.2,20160104" » /tmp/spooldir/dr/.dr.txt

echo "IBM,103.1,20160105" »/tmp/spooldir/dr/.dr.txt mv /tmp/spooldir/dr/.dr.txt /tmp/spooldir/dr/dr.txt

Solution :

Step 1 : create employee.json tile locally.

vi employee.json (press insert) past the content.

Step 2 : Upload this tile to hdfs, default location hadoop fs -put employee.json

Step 3 : Write spark script

#lmport SQLContext

from pyspark import SQLContext

#Create instance of SQLContext sqIContext = SQLContext(sc)

#Load json file

employee = sqlContext.jsonFile("employee.json")

#Register RDD as a temp table employee.registerTempTablef'EmployeeTab"}

#Select data from Employee table

employeelnfo = sqlContext.sql("select * from EmployeeTab"}

#lterate data and print

for row in employeelnfo.collect():

print(row)

Step 4 : Write dataas a Text file employeelnfo.toJSON().saveAsTextFile("employeeJson1")

Step 5: Check whether data has been created or not hadoop fs -cat employeeJsonl/part"

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;