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NEW QUESTION: 1
An administrator has a drive working properly under Linux and moves it to a Windows server. Which of the following should be performed to ensure Windows can read the disk?
A. Format the drive with UFS.
B. Format the drive with ext4.
C. Format the drive with NTFS.
D. Format the drive with ext2.
Answer: C
NEW QUESTION: 2
CORRECT TEXT
Problem Scenario 80 : You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.products
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Columns of products table : (product_id | product_category_id | product_name | product_description | product_price | product_image )
Please accomplish following activities.
1. Copy "retaildb.products" table to hdfs in a directory p93_products
2. Now sort the products data sorted by product price per category, use productcategoryid colunm to group by category
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Import Single table .
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba - password=cloudera -table=products --target-dir=p93
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 : Step 2 : Read the data from one of the partition, created using above command, hadoop fs -cat p93_products/part-m-00000
Step 3 : Load this directory as RDD using Spark and Python (Open pyspark terminal and do following}. productsRDD = sc.textFile(Mp93_products")
Step 4 : Filter empty prices, if exists
#filter out empty prices lines
Nonempty_lines = productsRDD.filter(lambda x: len(x.split(",")[4]) > 0)
Step 5 : Create data set like (categroyld, (id,name,price)
mappedRDD = nonempty_lines.map(lambda line: (line.split(",")[1], (line.split(",")[0], line.split(",")[2], float(line.split(",")[4])))) tor line in mappedRDD.collect(): print(line)
Step 6 : Now groupBy the all records based on categoryld, which a key on mappedRDD it will produce output like (categoryld, iterable of all lines for a key/categoryld) groupByCategroyld = mappedRDD.groupByKey() for line in groupByCategroyld.collect():
print(line)
step 7 : Now sort the data in each category based on price in ascending order.
# sorted is a function to sort an iterable, we can also specify, what would be the Key on which we want to sort in this case we have price on which it needs to be sorted.
groupByCategroyld.map(lambda tuple: sorted(tuple[1], key=lambda tupleValue:
tupleValue[2])).take(5)
Step 8 : Now sort the data in each category based on price in descending order.
# sorted is a function to sort an iterable, we can also specify, what would be the Key on which we want to sort in this case we have price which it needs to be sorted.
on groupByCategroyld.map(lambda tuple: sorted(tuple[1], key=lambda tupleValue:
tupleValue[2] , reverse=True)).take(5)
NEW QUESTION: 3
Which two statements describe key differences between single- and dual-homed WAN connections? (Choose two )
A. Dual-homed WAN connections are more expensive than single-homed connections
B. Dual-homed WAN connections require more skill to administer than single-homed connections
C. Dual-homed WAN connections have a simpler topology than single-homed connections.
D. Dual-homed WAN connections have more management-application overhead than single-homed connections
E. Dual-homed WAN connections have higher WAN speed than single-homed connections
Answer: A,E