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NEW QUESTION: 1
CORRECT TEXT
Problem Scenario 74 : You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.orders
table=retail_db.order_items
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Columns of order table : (orderjd , order_date , ordercustomerid, order status}
Columns of orderjtems table : (order_item_td , order_item_order_id ,
order_item_product_id,
order_item_quantity,order_item_subtotal,order_item_product_price)
Please accomplish following activities.
1. Copy "retaildb.orders" and "retaildb.orderjtems" table to hdfs in respective directory p89_orders and p89_order_items .
2. Join these data using orderjd in Spark and Python
3. Now fetch selected columns from joined data Orderld, Order date and amount collected on this order.
4. Calculate total order placed for each date, and produced the output sorted by date.
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=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)

NEW QUESTION: 2
Your client wants to ensure that a standard rejection correspondence is triggered when a candidate is put into the Rejection status for any step. How should you configure the system to ensure that this requirement is met? (Choose the best answer.)
A. Set the Rejection status for all steps in the Candidate Selection Workflow to be triggered on the Reject step.
B. Set the Candidate Selection Workflow context in the rejection correspondence so that any workflow with the status of Reject triggers the correspondence.
C. Set the correspondence trigger on the Reject status in the Candidate Selection Workflow.
D. Set the Candidate Selection Workflow context in the rejection correspondence so that any workflow with the Reject step triggers the correspondence.
Answer: C

NEW QUESTION: 3
You are using SAP BPC, version for SAP BW/4HANA Standard option. When you create a planning model based on an SAP BPC model, what can you do in SAP Analytics Cloud? Note: There are 2 correct Answers to this question.
A. Create data manager packages.
B. Create data actions.
C. Create public dimensions.
D. Create planning sequences.
Answer: B,D

NEW QUESTION: 4
Which of the following best describe implementation challenges that are associated with variable-length numbering plans?
A. the degree in which the dial plan varies.
B. the number of trunks that need to be assigned
C. the mapping between IP addresses and extension numbers
D. the variable number of extensions that need to be implemented
E. the identification of the number of digits that need to be dialed before the call is routed
Answer: A
Explanation:
Explanation- An open numbering plan, as found in countries that have not yet
standardized on numbering plans, features variance in the length of the area code or the
local number, or both.
Link-http://www.ciscopress.com/articles/article.asp?p=1715059