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print(sortedPriceProducts)
Step 10 : Now sort data based on product_price as ascending and product_id in ascending order, using takeOrdered{) function.
#Dont forget to cast string
#Tuple as key ((price,id),name) sortedPriceProducts=nonemptyJines.map(lambda line:
((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple :
(tuple[0][0],tuple[0][1]))
Step 11 : Now sort data based on product_price as descending and product_id in ascending order, using takeOrdered() function.
# Dont forget to cast string
# Tuple as key ((price,id},name)
# Using minus(-) parameter can help you to make descending ordering , only for numeric value.
sortedPrlceProducts=nonemptylines.map(lambda line:
((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple :
(-tuple[0][0],tuple[0][1]}}
We monitor
print(sortedPriceProducts)
Step 10 : Now sort data based on product_price as ascending and product_id in ascending order, using takeOrdered{) function.
#Dont forget to cast string
#Tuple as key ((price,id),name) sortedPriceProducts=nonemptyJines.map(lambda line:
((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple :
(tuple[0][0],tuple[0][1]))
Step 11 : Now sort data based on product_price as descending and product_id in ascending order, using takeOrdered() function.
# Dont forget to cast string
# Tuple as key ((price,id},name)
# Using minus(-) parameter can help you to make descending ordering , only for numeric value.
sortedPrlceProducts=nonemptylines.map(lambda line:
((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple :
(-tuple[0][0],tuple[0][1]}}
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(tuple[0][0],tuple[0][1]))
Step 11 : Now sort data based on product_price as descending and product_id in ascending order, using takeOrdered() function.
# Dont forget to cast string
# Tuple as key ((price,id},name)
# Using minus(-) parameter can help you to make descending ordering , only for numeric value.
sortedPrlceProducts=nonemptylines.map(lambda line:
((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple :
(-tuple[0][0],tuple[0][1]}}
(tuple[0][0],tuple[0][1]))
Step 11 : Now sort data based on product_price as descending and product_id in ascending order, using takeOrdered() function.
# Dont forget to cast string
# Tuple as key ((price,id},name)
# Using minus(-) parameter can help you to make descending ordering , only for numeric value.
sortedPrlceProducts=nonemptylines.map(lambda line:
((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple :
(-tuple[0][0],tuple[0][1]}}
(tuple[0][0],tuple[0][1]))
Step 11 : Now sort data based on product_price as descending and product_id in ascending order, using takeOrdered() function.
# Dont forget to cast string
# Tuple as key ((price,id},name)
# Using minus(-) parameter can help you to make descending ordering , only for numeric value.
sortedPrlceProducts=nonemptylines.map(lambda line:
((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple :
(-tuple[0][0],tuple[0][1]}}
(tuple[0][0],tuple[0][1]))
Step 11 : Now sort data based on product_price as descending and product_id in ascending order, using takeOrdered() function.
# Dont forget to cast string
# Tuple as key ((price,id},name)
# Using minus(-) parameter can help you to make descending ordering , only for numeric value.
sortedPrlceProducts=nonemptylines.map(lambda line:
((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple :
(-tuple[0][0],tuple[0][1]}}
(tuple[0][0],tuple[0][1]))
Step 11 : Now sort data based on product_price as descending and product_id in ascending order, using takeOrdered() function.
# Dont forget to cast string
# Tuple as key ((price,id},name)
# Using minus(-) parameter can help you to make descending ordering , only for numeric value.
sortedPrlceProducts=nonemptylines.map(lambda line:
((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple :
(-tuple[0][0],tuple[0][1]}}
(tuple[0][0],tuple[0][1]))
Step 11 : Now sort data based on product_price as descending and product_id in ascending order, using takeOrdered() function.
# Dont forget to cast string
# Tuple as key ((price,id},name)
# Using minus(-) parameter can help you to make descending ordering , only for numeric value.
sortedPrlceProducts=nonemptylines.map(lambda line:
((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple :
(-tuple[0][0],tuple[0][1]}}
(tuple[0][0],tuple[0][1]))
Step 11 : Now sort data based on product_price as descending and product_id in ascending order, using takeOrdered() function.
# Dont forget to cast string
# Tuple as key ((price,id},name)
# Using minus(-) parameter can help you to make descending ordering , only for numeric value.
sortedPrlceProducts=nonemptylines.map(lambda line:
((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple :
(-tuple[0][0],tuple[0][1]}}
(tuple[0][0],tuple[0][1]))
Step 11 : Now sort data based on product_price as descending and product_id in ascending order, using takeOrdered() function.
# Dont forget to cast string
# Tuple as key ((price,id},name)
# Using minus(-) parameter can help you to make descending ordering , only for numeric value.
sortedPrlceProducts=nonemptylines.map(lambda line:
((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple :
(-tuple[0][0],tuple[0][1]}}
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NEW QUESTION: 1
Architects seek to design in a way that brings value to an organisation. To reach these goals, data architects define and maintain specifications that:
A. Align data architecture with enterprise strategy and business architecture
B. Provide a standard business vocabulary for data and components
C. Define the current state of data in the organization.
D. Outline high-level integrated designs to meet these requirements.
E. Integrate with overall enterprise architecture roadmap
F. Express strategic data requirements
Answer: A,B,C,D,E,F
NEW QUESTION: 2
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、指定された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
Storage1という名前のAzure Storage v2アカウントがあります。
Storage1にデータをアーカイブする予定です。
アーカイブされたデータを5年間削除できないようにする必要があります。ソリューションは、管理者がデータを削除できないようにする必要があります。
解決策：ファイル共有とスナップショットを作成します。
これは目標を達成していますか？
A. いいえ
B. はい
Answer: A
Explanation:
Explanation
Instead you could create an Azure Blob storage container, and you configure a legal hold access policy.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blob-immutable-storage
NEW QUESTION: 3
CORRECT TEXT
Problem Scenario 79 : 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 products table : (product_id | product categoryid | product_name | product_description | product_prtce | product_image )
Please accomplish following activities.
1 . Copy "retaildb.products" table to hdfs in a directory p93_products
2 . Filter out all the empty prices
3 . Sort all the products based on price in both ascending as well as descending order.
4 . Sort all the products based on price as well as product_id in descending order.
5 . Use the below functions to do data ordering or ranking and fetch top 10 elements top() takeOrdered() sortByKey()
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_products -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 : 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("p93_products")
Step 4 : Filter empty prices, if exists
#filter out empty prices lines
nonemptyjines = productsRDD.filter(lambda x: len(x.split(",")[4]) > 0)
Step 5 : Now sort data based on product_price in order.
sortedPriceProducts=nonempty_lines.map(lambdaline:(float(line.split(",")[4]),line.split(",")[2]
)).sortByKey()
for line in sortedPriceProducts.collect(): print(line)
Step 6 : Now sort data based on product_price in descending order.
sortedPriceProducts=nonempty_lines.map(lambda line:
(float(line.split(",")[4]),line.split(",")[2])).sortByKey(False)
for line in sortedPriceProducts.collect(): print(line)
Step 7 : Get highest price products name.
sortedPriceProducts=nonemptyJines.map(lambda line : (float(line.split(",")[4]),line- split(,,,,,)[2]))-sortByKey(False).take(1) print(sortedPriceProducts)
Step 8 : Now sort data based on product_price as well as product_id in descending order.
#Dont forget to cast string #Tuple as key ((price,id),name)
sortedPriceProducts=nonemptyJines.map(lambda line : ((float(line
print(sortedPriceProducts)
Step 9 : Now sort data based on product_price as well as product_id in descending order, using top() function.
#Dont forget to cast string
#Tuple as key ((price,id),name)
sortedPriceProducts=nonemptyJines.map(lambda line: ((float(line.s
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