Cloudera CCA175 Test Dumps

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
b.mapValuesf'x" + _ + "x").collect
mapValues [Pair] : Takes the values of a RDD that consists of two-component tuples, and
applies the provided function to transform each value. Tlien,.it.forms newtwo-componend
tuples using the key and the transformed value and stores them in a new RDD.

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
val z = x.intersection(y)
intersection : Returns the elements in the two RDDs which are the same.

Answer: 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=p92_orders –m 1
sqoop import -connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -
password=cloudera -table=order_items -target-dir=p92_order_orderitems -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, hadoop fs
-cat p92_orders/part-m-00000 hadoop fs -cat p92 orderitems/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(Mp92_orders") orderitems =
sc.textFile("p92_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
orders Key Value = 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
for line in joinedData.collect():
print(line)
#Format of joinedData as below.
#[Orderld, 'All columns from orderltemsKeyValue', 'All columns from ordersKeyValue']
ordersPerDatePerCustomer = joinedData.map(lambda line: ((line[1][1].split(",")[1],
line[1][1].split(",M)[2]), float(line[1][0].split(",")[4]))) amountCollectedPerDayPerCustomer =
ordersPerDatePerCustomer.reduceByKey(lambda runningSum, amount: runningSum +
amount}
#(Out record format will be ((date,customer_id), totalAmount} for line in
amountCollectedPerDayPerCustomer.collect(): print(line)
#now change the format of record as (date,(customer_id,total_amount))
revenuePerDatePerCustomerRDD = amountCollectedPerDayPerCustomer.map(lambda
threeElementTuple: (threeElementTuple[0][0],
(threeElementTuple[0][1],threeElementTuple[1])))
for line in revenuePerDatePerCustomerRDD.collect():
print(line)
#Calculate maximum amount collected by a customer for each day
perDateMaxAmountCollectedByCustomer =
revenuePerDatePerCustomerRDD.reduceByKey(lambda runningAmountTuple,
newAmountTuple: (runningAmountTuple if runningAmountTuple[1] >=
newAmountTuple[1] else newAmountTuple})for line in perDateMaxAmountCollectedByCustomer\sortByKey().collect(): print(line)

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create file in hdfs (We will do using Hue). However, you can first create in local
filesystem and then upload it to hdfs.
Step 2 : Load data.csv file from hdfs and create PairRDDs
val name = sc.textFile("spark8/data.csv")
val namePairRDD = name.map(x=> (x.split(",")(0),x.split(",")(1)))
Step 3 : Now swap namePairRDD RDD.
val swapped = namePairRDD.map(item => item.swap)
Step 4 : Now combine the rdd by key.
val combinedOutput = namePairRDD.combineByKey(List(_), (x:List[String], y:String) => y ::
x, (x:List[String], y:List[String]) => x ::: y)
Step 5 : Save the output as a Text file and output must be written in a single file.
:ombinedOutput.repartition(1).saveAsTextFile("spark8/result.txt")

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create directory mkdir /tmp/nrtcontent
Step 2 : Create flume configuration file, with below configuration for source, sink and
channel and save it in flume6.conf.
agent1 .sources = source1
agent1 .sinks = sink1
agent1.channels = channel1
agent1 .sources.source1.channels = channel1
agent1 .sinks.sink1.channel = channel1
agent1 .sources.source1.type = spooldir
agent1 .sources.source1.spoolDir = /tmp/nrtcontent
agent1 .sinks.sink1 .type = hdfs
agent1 .sinks.sink1.hdfs.path = /tmp/flume
agent1.sinks.sink1.hdfs.filePrefix = events
agent1.sinks.sink1.hdfs.fileSuffix = .log
agent1 .sinks.sink1.hdfs.inUsePrefix = _
agent1 .sinks.sink1.hdfs.fileType = Data Stream
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/fIume6.conf -name agent1
Step 5 : Open another terminal and create a file in /tmp/nrtcontent
echo "I am preparing for CCA175 from ABCTechm.com" > /tmp/nrtcontent/.he1.txt
mv /tmp/nrtcontent/.he1.txt /tmp/nrtcontent/he1.txt
After few mins
echo "I am preparing for CCA175 from TopTech.com" > /tmp/nrtcontent/.qt1.txt
mv /tmp/nrtcontent/.qt1.txt /tmp/nrtcontent/qt1.txt