CCA175 Exam Dumps : CCA Spark and Hadoop Developer Exam

Solution :

Step 1 : Login to musql db

mysql --user=retail_dba -password=cloudera

show databases;

use retail db; show tables;

Step 2 : Create a table as given in problem statement.

CREATE table departments_new (department_id int(11), department_name varchar(45), createddate T1MESTAMP DEFAULT NOW());

show tables;

Step 3 : isert records from departments table to departments_new insert into departments_new select a.", null from departments a;

Step 4 : Import data from departments new table to hdfs.

sqoop import \

-connect jdbc:mysql://quickstart:330G/retail_db \

~username=retail_dba \

-password=cloudera \

-table departments_new\

--target-dir /user/cloudera/departments_new \

--split-by departments

Stpe 5 : Check the imported data.

hdfs dfs -cat /user/cloudera/departmentsnew/part"

Step 6 : Insert following 5 records in departmentsnew table.

Insert into departments_new values(110, "Civil" , null);

Insert into departments_new values(111, "Mechanical" , null);

Insert into departments_new values(112, "Automobile" , null);

Insert into departments_new values(113, "Pharma" , null);

Insert into departments_new values(114, "Social Engineering" , null);

commit;

Stpe 7 : Import incremetal data based on created_date column.

sqoop import \

-connect jdbc:mysql://quickstart:330G/retaiI_db \

-username=retail_dba \

-password=cloudera \

--table departments_new\

-target-dir /user/cloudera/departments_new \

-append \

-check-column created_date \

-incremental lastmodified \

-split-by departments \

-last-value "2016-01-30 12:07:37.0"

Step 8 : Check the imported value.

hdfs dfs -cat /user/cloudera/departmentsnew/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;

Solution:

Step 1 : Import Single table .

sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera -table=orders --target-dir=p89_orders - -m1

sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera -table=order_items ~target-dir=p89_ order items -m 1

Note : Please check you dont have space between before or after '=' sign. Sqoop uses the MapReduce framework to copy data from RDBMS to hdfs

Step 2 : Read the data from one of the partition, created using above command, hadoopfs -cat p89_orders/part-m-00000 hadoop fs -cat p89_order_items/part-m-00000

Step 3 : Load these above two directory as RDD using Spark and Python (Open pyspark terminal and do following). orders = sc.textFile("p89_orders") orderitems = sc.textFile("p89_order_items")

Step 4 : Convert RDD into key value as (orderjd as a key and rest of the values as a value)

#First value is orderjd

ordersKeyValue = orders.map(lambda line: (int(line.split(",")[0]), line))

#Second value as an Orderjd

orderltemsKeyValue = orderltems.map(lambda line: (int(line.split(",")[1]), line))

Step 5 : Join both the RDD using orderjd

joinedData = orderltemsKeyValue.join(ordersKeyValue)

#print the joined data

tor line in joinedData.collect():

print(line)

Format of joinedData as below.

[Orderld, 'All columns from orderltemsKeyValue', 'All columns from orders Key Value']

Step 6 : Now fetch selected values Orderld, Order date and amount collected on this order.

revenuePerOrderPerDay = joinedData.map(lambda row: (row[0]( row[1][1].split(",")[1]( f!oat(row[1][0].split('\M}[4]}}}

#printthe result

for line in revenuePerOrderPerDay.collect():

print(line)

Step 7 : Select distinct order ids for each date.

#distinct(date,order_id)

distinctOrdersDate = joinedData.map(lambda row: row[1][1].split('\")[1] + "," + str(row[0])).distinct()

for line in distinctOrdersDate.collect(): print(line)

Step 8 : Similar to word count, generate (date, 1) record for each row. newLineTuple = distinctOrdersDate.map(lambda line: (line.split(",")[0], 1))

Step 9 : Do the count for each key(date), to get total order per date. totalOrdersPerDate = newLineTuple.reduceByKey(lambda a, b: a + b}

#print results

for line in totalOrdersPerDate.collect():

print(line)

step 10 : Sort the results by date sortedData=totalOrdersPerDate.sortByKey().collect()

#print results

for line in sortedData:

print(line)