Sample Talend Questions – MCQs





1. Which of the following in the design workspace indicates an error with a component in Talend

A) A red ‘X’                                                                      B) A red exclamation point

C)A green ‘ I’                                                                    D)A yellow  exclamation point

2. Which of the following components can be used to implement lookup in Talend

A)tJoin                                                                         B) tLookUp

C)tMap                                                                        D)tUnite

3.tMap offers following match modes for a lookup in Talend studio

A)Unique match                                             B)Only unique match

C)First match                                                   D)All matches

4.tMap offers following join model in Talend tool

A)Left Outer Join                                     B)Right Outer Join

C)Inner Join                                                D)Full Outer Join

5. Which of the following the components is used to execute a job infinite times in Talend

A)tInfiniteLoop                                          B) tFileWatcher

C) tForEach                                                  D)tRunJon

6.How to access parameters in the Global Map in Talend ETL tool

A)globalMap.put(“Key”, Object)

B)globalMap.get(“Key”, Object)

C)globalMap.put(“key”)

D)globalMap.get(“key”)

7. How do you reference the value of Context Variable FileName in configuration while Talend programming

A)Context.FileName                                B)context.FileName

C)FileName.value                                     D)$context.FileName

8. While Installing your Talend solutions, you have to set the following variable is mandatory?

A)JAVA_HOME                           B)TALEND_HOME

C)TIS_HOME                                 D)JRE_HOME

9. What is the use of a tReplicate component? Choose one best answer?

A)To duplicate the configuration of an existing component

B)To copy the input row to an output row without processing it

C)To duplicate a sub job

D)To send duplicates of an output row to multiple target components

10. How do you see the configuration of an error message for a component in Talend studio?

A)Right-click the component and then click show problem

B)From the errors view

C)Place the mouse pointer over error symbol in the design workspace

D)From the problems view

11. How do you create a row between two components in Talend

A) Drag the target component to source component

B)Right-click the source component click Row followed by the row type and then click the target component

C)Drag the source component onto target components

D)Right-click the source component and then click the target component

12. How do you ensure that a subjob completes before a second subjob runs in Talend?

A)Using RunIf trigger

B)Using the main connection

C)Using onComponentOk or OnComponentError trigger

D)Using onSubJobOk or onSubJobError trigger

13. Which of the following the components will be used to load JSON file to MySQL database in Talend?

A)tMySQLInput

B)tFileInputJSON

C)tMySQLOutput

D)tMap

14. How do you run a job in Talend Studio?

A)Click the Run button in the Run view

B)Click the Run button in the Job view

C)Click the Run button in the File Menu

D)Click the Start button in the Run view
15. What is the best practice for arranging components on the design workspace in Talend studio?

A)Bottom to Top

B)Right to Left

C)Top to Bottom

D)Matching the flow of data

16. From which tab in component view would you specify the component label in Talend

A)View

B)Advanced settings

C)Basic settings

D)Documentation

17. How to place your component in a job in Talend Studio?

A) Click it on Edit Menu

B) Click it in the Repository and then click in the design workspace

C) Click it from Repository to the design workspace

D)Click it in the Palette and then click in the design workspace

Talend Questions for Certification




Sample Questions for Talend Certification

1. Which of the Following transformations/operations are possible for using tMap?

A) Lookup                                                           B)Join

C) Sorting                                                            D) Filtering

2. What is Job in Talend?

A) visual set of components graphically connected using different connections
B) visual set of metadata graphically connected using different components

C) collection of components and metadata
D)a & c

3. Which of the following component is used to generate sample data
A) tFixedFlowInput                                                              B)tGenerateDate

C)tRowGenerator                                                                D) tSampleData

4.Which Layout exported from a component in Talend
A)Excel format                          B)Text file

C)XML file                                       D)CSV file

5.Which of the following is a correct way to parse String column to Date

A)TalendDate.parseDate(“MM/dd/yyyy”, row2.date)
B) Date.parseDate(“MM/dd/yyyy”, row2.date)
C) TalendDate.getDate(“MM/dd/yyyy”, row2.date)
D)TalendDate.formatDate(“MM/dd/yyyy”, row2.date)

6. Which of the Following components are used to store log and statistical information about your job
A)tStatsCatcher
B)tLogCatcher
C)tFlowMeterCatcher
D)none of the above

7.In order to filter all files with a name having string “AMZ_AMZ001” using the tFileList component in Talend

A) Directory property should be set to “AMZ_AMZ001”
B)Set FileMask property to “AMZ_AMZ001”
C) Set FileMask property to “*AMZ_AMZ001*”
D)Set FileMask property to “*AMZ_AMZ001”

8. In which user interface element do you find Business Models, Job designs & Metadata?

A)The Job view                                                              B) The Repository

C)The design workspace                                          D)The Palette

9. What is indicated by an asterisk next to the job name in the design workspace

A)That this is an active job                              B)That the job contains an error

C)That the job contains unsaved changes  D)That the job is currently running

10. When you first start Talend Open Studio what are the advantages of creating a Talend account? Choose all that apply

A)You can visit MyTalend.com
B)You are required to create an account
C)You can post questions/answers to Talend forum
D)You can download components from Talend Exchange

11.From which View in Talend Open Studio would you clear the statistics from the design workspace? Choose one Answer
A)The component view
B)The context view
C)The problems view
D)The run view
E)The job view

Hadoop Admin Vs Hadoop Developer

Basically in Hadoop environment Hadoop Admin and Hadoop Developer major roles according to present IT market survey Admin has more responsibilities and salaries compared to Hadoop developers. But we can differentiate below-mentioned points:



Hadoop Developer:

  1. In Big Data environment Hadoop is a major role, especially in Hadoop developers. A developer primarily responsible for Coding in Hadoop developer also the same kind of thing here developing like:

A)Apache Spark – Scala, Python, Java, etc.

B) Map Reduce – Java

C)Apache Hive  – HiveQL (Query Language & SQL)

D) Apache Pig  – Pig Scripting language etc.

2. Familiarity with ETL backgrounds for data loading and ingestion tools like:

A)Flume

B)Sqoop

3. Bit of knowledge on Hadoop admin part also like Linux environment and some of the basic commands while developing and executing.

4. Nowadays most preferably Spark & Hive developers with high-level experience and huge packages.

2.Hadoop Administration:

1. Coming to Hadoop Administration is a good and respectable job in the IT industry. Whereas, admin is responsible for performing the operational tasks to keep the infrastructure and running jobs.

2. Strong knowledge of the Linux environment. Setting up Cluster and Security authentication like Kerberos and testing the HDFS environment.

3. To provide new user access to Hive, Spark, etc. And cluster maintenance like adding (commissioning) node and removing (decommissioning) nodes. Resolve errors like memory issues, user access issues, etc.

4.Must and should knowledge on BigData platforms like:




A) Cloudera Manager

B) Horontworks Data Platform

C) MapR

D) Pseudo-distributed and Single node cluster setup etc.

5. Review and Managing log files and setting up of XML files.

6. As of now trending and career growth job.

7. Compared to Hadoop developers, Hadoop Admins are getting high salary packages in present marketing.

Summary: In the Bigdata environment Hadoop has valuable and trending jobs. And provide huge packages for both Hadoop developers and Hadoop administration. Depends upon skill set will prefer what we need for future growth.

Big Data Spark Multiple Choice Questions

Spark Multiple Choice Questions and Answers:

1)Point out the incorrect  statement in the context of Cassandra:

A) Cassandra is a centralized key -value store

B) Cassandra is originally designed at Facebook

C) Cassandra is designed to handle a large amount of data across many commodity servers, providing high availability with no single point if failure.

D) Cassandra uses a right based DHT*Distribution Hash Table) but without finger tables or routing

Ans : D

2. Which of the following are the simplest NoSQL databases in BigData environment?

A) Document                                    B) Key-Value Pair

C) Wide – Column                        D) All of the above mentioned 

Ans : ) All of the above mentioned

3) Which of the following is not a NoSQL database?

A) Cassandra                          B) MongoDB

C) SQL Server                           D) HBase

Ans: SQL Server

4) Which of the following is a distributed graph processing framework on top of Spark?

A) Spark Streaming                   B)MLlib

C)GraphX                                          D) All of the above

Ans: GraphX

5) Which of the following is leverage of Spark core fast scheduling capability to perform streaming analytics?

A) Spark Streaming                     B) MLlib

C)GraphX                                       D) RDDs

Ans: Spark Streaming

6) Which of the following Machine Learning API for Spark based on Which one:

A) RDD                                 B) Dataset

C)DataFrame          D) All of the above

Ans: DataFrame

7) Based on which functional programming language construct for Spark optimizer

A) Python                         B) R

C) Java                                   D)Scala

Ans: Scala is a functional programming language

8) Which of the following is a basic abstraction of Spark Streaming?

A)Shared variable                 B)RDD

C)Dstream                                  D)All of the above

Ans: Dstream

9) In a which cluster manager to do support of Spark?

A) MESOS                                B)YARN

C) Standalone Cluster manager   D) Pseudo Cluster manager

E) All of the above

Ans: All of the above

10) Which of the following is the reason for Spark being faster than MapReduce while execution time?

A) It supports different programming languages like Scala, Python, R, and Java.

B)RDDs

C)DAG execution engine and in-memory computation (RAM based)

D) All of the above

Ans: DAG execution engine and in-memory computation (RAM based)

BigData and Spark Multiple Choice Questions – I

1. In Spark, a —————– is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost.

A) Resilient Distributed Dataset (RDD)                  C)Driver

B)Spark Streaming                                                          D) Flat Map

Ans: Resilient Distributed Dataset (RDD)

2. Consider the following statement is the correct context of Apache Spark   :

Statement 1: Spark allows you to choose whether you want to persist Resilient Distributed Dataset (RDD) onto the disk or not.

Statement 2: Spark also gives you control over how you can partition your Resilient Distributed Datasets (RDDs).

A)Only statement 1 is true                 C)Both statements are true

B)Only statement 2 is true                  D)Both statements are false

Ans: Both statements are true

3) Given the following definition about the join transformation in Apache Spark:

def : join [W] (other: RDD[(K, W)]) : RDD [(K, (V, W))]

Where join operation is used for joining two datasets. When it is called on datasets of type (K, V) and (K, W), it returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key.

Output the result of joinrdd, when the following code is run.

val rdd1 = sc.parallelize (Seq ((“m”,55), (“m”,56), (“e”,57), (“e”,58), (“s”,59),(“s”,54)))
val rdd2 = sc.parallelize (Seq ((“m”,60),(“m”,65),(“s”,61),(“s”,62),(“h”,63),(“h”,64)))
val joinrdd = rdd1.join(rdd2)
joinrdd.collect
A) Array[(String, (Int, Int))] = Array((m,(55,60)), (m,(55,65)), (m,(56,60)), (m,(56,65)), (s,(59,61)), (s,(59,62)), (h,(63,64)), (s,(54,61)), (s,(54,62)))
B) Array[(String, (Int, Int))] = Array((m,(55,60)), (m,(55,65)), (m,(56,60)), (m,(56,65)), (s,(59,61)), (s,(59,62)), (e,(57,58)), (s,(54,61)), (s,(54,62)))
C) Array[(String, (Int, Int))] = Array((m,(55,60)), (m,(55,65)), (m,(56,60)), (m,(56,65)), (s,(59,61)), (s,(59,62)), (s,(54,61)), (s,(54,62)))
D)None of the mentioned.

Ans: Array[(String, (Int, Int))] = Array((m,(55,60)), (m,(55,65)), (m,(56,60)), (m,(56,65)), (s,(59,61)), (s,(59,62)), (s,(54,61)), (s,(54,62)))

4)Consider the following statements are correct:

Statement 1: Scale up means incrementally grow your cluster capacity by adding more COTS machines (Components Off the Shelf)

Statement 2: Scale out means grow your cluster capacity by replacing with more powerful machines

A) Only statement 1 is true               C) Both statements are true

B) Only statement 2 is true              D) Both statements are false

Ans: Both statements are true

Complete mapR Installation on Linux machine

After completion of Prerequisite set up will go through directly with MapR actual steps for Installation on Linux machine.

Actual steps for MapR installation:

Step 1:  fdisk -l




Powerful and popular command it is used for the list of disk partition tables.

Step 2: cat /etc/yum.repos.d/mapr_ecosystem.repo

Install/Update mapr eco system repo files

Step 3:  cat /etc/yum.repos.d/mapr_installer.repo

Install/Update mapr installer repo  files

Step 4:  cat /etc/yum

configuring yum repos

Step 5:cat /etc/yum.repos.d/mapr_core.repo

Install/Update mapr repo repo files

Step 6: yum clean all

Yum un necessary repos cleaned

Step 7: yum update

Yum update

Step 8: yum list | grep mapr

Check yum list files in mapr by using grep command

Step 9: rpm –import http://package.mapr.com/releases/pub/maprgpg.key

Import mapr public key

Step 10: yum install mapr-cldb mapr-fileserver mapr-webserver mapr-resourcemanager mapr-nodemanager mapr-nfs mapr-gateway mapr-historyserver

Install mapr CLDB file server, Web server, Resource manager, node manager, nfs ,gateway and History server by using above single command.

Step 11: yum install mapr-zookeeper

Install MapR Zookeeper for configuration

Step 12:  ls -l /opt/mapr/roles

Check mapr roles

Step  13: rpm -qa | grep mapr

Step 14: id mapr

ID creation of mapr user

Step 15: hostname -i

Check Fully Qualified Domain Name

Step 16: /opt/mapr/server/configure.sh -N training -C 192.0.0.0 -Z  192.0.0.0:5181

Configure server with your ip

Step 17: cat /root/maprdisk.txt

Check disk files
Step 18: /opt/mapr/server/disksetup -F /root/maprdisk.txt

Disk setup in mapr disk.
Step 19: service mapr-zookeeper start

Start the MapR Zookeeper service

Step 20: service mapr-zookeeper status

Status of the MapR Zookeeper service

Step 21: service mapr-warden start

Start the MapR Warden service

Step 22: service mapr-warden status

Status of the MapR Warden service

Step 23: maprcli node cldbmaster

Step 24: maprcli license showid

Show your mapr license id

Step 25: https://<ipaddress>:8443

Open a web browser with your < IP address : 8443 > then will check it working or not

Step 26: hadoop fs -ls /

Check hadoop file list




Summary: Above steps are worked for Linux single node cluster for complete MapR Installation with the explanation each and every command.

MapR Installation steps on AWS

MapR Installation on Amazon Web Service Machine with simple steps for Hadoop environment.




Step 1: Login with AWS credentials and then open the root machine.

[ec2-user@ip----~]$ sudo su -

Step 2: Put off the IP tables  services

[root@ip---- ~]# service iptables stop

Step 3: Check the configuration of iptables

[root@ip----- ~]# chkconfig iptables off

Step 4: Edit the SELinux configuration

[root@ip----~]# vim /etc/selinux/config

Step 5: EDIT replace enforcing with disabled (save and exit)

[root@ip----~]# SELINUX = disabled

Step 6: Open repos by using below command

[root@ip----~]# cd /etc/yum.repos.d/

Step 7: edit mar ecosystem repo file.

[root@ip----yum.repos.d]# vi mapr_ecosystem.repo

Put the following lines into the above file

[MapR_Ecosystem]
name = MapR Ecosystem Components
baseurl = http://package.mapr.com/releases/MEP/MEP-3.0.4/redhat
gpgcheck = 0
enabled = 1
protected = 1

Step 8: edit mapr installer repo files.

[root@ip----yum.repos.d]# vi mapr_installer.repo

Step 9: Edit mapr core repo files.

[root@ip----yum.repos.d]# vi mapr_core.repo

Put the following lines into the above file

[MapR_Core]
name = MapR Core Components
baseurl = http://archive.mapr.com/releases/v5.0.0/redhat/
gpgcheck = 1
enabled = 1
protected = 1

Step 10: create yum repolist

[root@ip----- yum.repos.d]# yum repolist

(here you will seen all packages)
Step 11: Search mapr package files.

[root@ip------ yum.repos.d]# yum list all | grep mapr

(this displays all packages related to mapr)

Step 12: import rpm package files

[root@ip----- yum.repos.d]# rpm --import

http://package.mapr.com/releases/pub/maprgpg.key

Step 13:  install mapr cldb file server,webserver,resource manager and node manager

[root@ip------ yum.repos.d]# yum install mapr-cldb mapr-fileserver mapr-

webserver mapr-resourcemanager mapr-nodemanager

Step 14: Install mapr Zookeeper

[root@ip------ yum.repos.d]# yum install mapr-zookeeper

Step 15: list of mapr files

[root@ip----- yum.repos.d]# ls -l /opt/mapr/roles/

Step 16: search for mapr rpm files by using files grep command.

[root@ip------ yum.repos.d]# rpm -qa | grep mapr

(displays installed packages related to mapr)

Step 17: Adding Group for mapr system

[root@ip------ yum.repos.d]# groupadd -g 5000 mapr

Step 18: Adding a user for mapr group system

[root@ip------ yum.repos.d]# useradd -g 5000 -u 5000 mapr

Step 19 : Set passwd for mapr user

[root@ip------ yum.repos.d]#passwd mapr

(here you will give password for mapr user)
(you can give any name)

Step 20: create id mapr

[root@ip------ yum.repos.d]# id mapr

Step 21: check Fully Qualified Doman Name using below command

[root@ip------ yum.repos.d]# hostname -f

Step 22: check disk availability

[root@ip------ yum.repos.d]# fdisk -l

(here you have seen available disks in that machine and select the second disk for mapr)

Step 23: Edit second disk information for maprdisk file system.

[root@ip----- yum.repos.d]# vi /root/maprdisk.txt

(here that second disk put here)(save and exit)

Step 24: Set the configuration server in different zones.

[root@ip----- yum.repos.d]# /opt/mapr/server/configure.sh -N training -C ip--------.ap-southeast-1.compute.internal -Z ip------.ap-southeast-1.compute.internal:5181

Step 25: Edit second disk files

[root@ip------ yum.repos.d]# cat /root/maprdisk.txt

Step 26: Download the rpm files

[root@ip------ ~]# wget http://download.fedoraproject.org/pub/epel/6/x86_64/epel-release-6-8.noarch.rpm

Step 27: Extra package for enterprise linux system

[root@ip------ ~]# rpm -Uvh epel-release-6*.rpm

Step 28: Start Zookeeper services

[root@ip------ ~]# service mapr-zookeeper start

Step 29 :Start warden services

[root@ip-1----- ~]# service mapr-warden start

Step 30: Start MapR CLI NODE CLDB MASTER service



[root@ip----- ~]# maprcli node cldbmaster

Here you will go with your machine ip in web server for mcs..shown below..
example: http://192.168.0.0:8443

Adding Hive Service in MapR





After successful installation of MapR distribution, we need to add services like Hive, Sqoop, Spark, Impala etc. Here we are adding Hive service with simple commands in MapR for Hadoop Environment.

Add Hive Service in MapR :

We must should follow below commands for Hive services:

Step 1: yum install for Hive Mapr.

[root@master1 ~]# yum install mapr-hive mapr-hiveserver2 mapr-hivemetastore mapr-hivewebhcat

Here Loaded plugins like  fastest mirrors, refresh-package kit, security yu
Setting up Install Process is done in this step

Installing below packages of MapR Hiver Services:
mapr – hive noarch
mapr -hivemetastore
mapr-hiveserver2
mapr-hivewebhcat

Step 2:  To install MySQL server for external Database for multiple users.

[root@master1 ~]# yum install MySQL - server

Download below rpm files for MySQL servers:

mysql-5.1.73-8.el6_8.x86_64.rpm
mysql-server-5.1.73-8.el6_8.x86_64.rpm
perl-DBD-MySQL-4.013-3.el6.x86_64.rpm
perl-DBI-1.609-4.el6.x86_64.rpm

Step 3:  Checking of MySQL Status

[root@master1 ~]# service mysqld status

Step 4: Start MySQL service by using below command:

[root@master1 ~]# service mysqld start

After start MySQL services set the password for mysql service

#mysql -u root -p

Step 5: Grant all privileges.

mysql>grant all privileges on *.* to 'your name '@'localhost' identified by 'your name ';

Step 6: Flush all privileges.

mysql>flush privileges;

Step 7: Exit from MySQL cli

mysql>exit

Step 8: Set the hive site .xml file for fully configurations

[root@master1 ~] # vi /opt/mapr/hive/hive-2.1/conf/hive-site.xml
<configuration>

<property>
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:mysql://localhost:3306/hive?createDatabaseIfNotExist=true</value>
<description>JDBC connect string for a JDBC metastore</description>
</property>

<property>
<name>javax.jdo.option.ConnectionDriverName</name>
<value>com.mysql.jdbc.Driver</value>
<description>Driver class name for a JDBC metastore</description>
</property>

<property>
<name>javax.jdo.option.ConnectionUserName</name>
<value>siva</value>
<description>username to use against metastore database</description>
</property>

<property>
<name>javax.jdo.option.ConnectionPassword</name>
<value> your name</value>
<description>password to use against metastore database</description>
</property>

<property>
<name>hive.metastore.uris</name>
<value>thrift://localhost:9089</value>
</property>

</configuration>

Step 9: export the metastotr with port number.

[root @ master1 ~]# export METASTORE_PORT=9089

Step 10: For MySQL DB schema

[root @ master1 ~]# /opt/mapr/hive/hive-2.1/bin/schematool -dbType mysql -initSchema

Step 11: Login with MySQL CLI with your credentials

[root @ master 1 ~]# mysql -u name -p
Enter password:

Step 12: To check databases

mysql> show databases;
+--------------------+
| Database |
+--------------------+
| information_schema |
| hive |
| mysql | 
| test |
+--------------------+

Step 13: Exit from MySQL CLI



mysql> exit
Bye

Step 14: Install MySQL connector java file for connection

[root@master1 ~]# yum -y install mysql-connector-java

Step 15: Start Meta store services

[root@master1 ~]# /opt/mapr/hive/hive-2.1/bin/hive --service metastore --start

Step 16: Start Hive services:

[root@master1 ~]# hive
Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.

Prerequisites for MapR Installation on CentOS

In Hadoop Eco-System we preferable mostly three Big data distributions:

1.Cloudera Distribution Hadoop

2.Horton Works Data Platform

3.MapR Distributions Platform




In Cloudera, Distribution Platform is a free version, express, and enterprise edition up to 60 days trial version.

Coming to Hortonworks Data Platform completely open source platform for production, developing and testing environment.

Then finally MapR distribution platform is a complete enterprise edition but in MapR 3 is free version is available with fewer features to compare to MapR 5 and MapR 7.

How to install MapR free version on Pseduo Cluster:

Before the install of MapR, we configured prerequisites as  below:

——-Prerequisites——–

1.Configure hostname like FQDN by using the setup command (mapr.hadoop.com) after that check your hostname using hostname -f

2. vi/etc/hosts

3.hostname < your Fully Qualified Domain>

4. vim/etc/selinux/config ===> SELinux = disabled

——-Disable Firewalls and IPTables——-

If you enable firewalls and iptables doesn’t allow some ports so we must and should disable it.

1.service iptables save

2.service iptables stop

3.chkconfig iptables off

4.service ip6table save

5.service ip6tables stop

6.chkconfig ip6tables off

—– Enable NTP service for machines —–

NTP is a Network Time Protocol is a networking protocol for time synchronization between computers and packet switched data.

1.yum -y install ntp ntpupdate ntp-doc

2.chkconfig ntpd on

3.vi /etc/ntp.conf

4.server 0.rhel.pool.ntp.org

5.server 1.rhel.pool.ntp.org

6.server 2.rhel.pool.ntp.org

7.ntpq -p

8.date ( All machines have the same date otherwise it will showing error)

—— Install some additional packages in Linux OS —-

Here will install JAVA 1.8 and Python

1.yum -y install java-1.8.0 -openjdk-devel

2.yum -y install python perl expect expectk

—- setup passwordless SSH On all nodes form master node ——

For passwordless authentication in between master and slave nodes

1.ssh-keygen -t rsa

2.cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

3.ssh-copy-id root@<FQDN1, FQDN2>

—–Additional Linux configuration or Transparent Huge Pages(THP)—-

1. echo never > /sys/kernel/mm/redhat_transparent_hugepage/enabled

2.echo never > /sys/kernel/mm/redhat_transparent_hugepage/defrag

3.sysctl vm.swapiness=10

set up EPEL repository for installing additional packages on the system

Here  EPEL repository for installing the additional packages in centos machine




1.Install -uvh the EPEL repository

2.wget http://http://download.fedoraproject.org/pub/epel/6/x86_64/epel-release -6.8.norach.rpm

HBase Table(Single&Multiple) data migration from one cluster to another cluster

HBase single table migration from one cluster to another cluster:

Here will be shown about Hbase single data table migration existing cluster to a new cluster simple steps:




Step 1: First export the hbase table data into the local hdfs path (Hadoop Distributed File System)

Step 2: After that copy the HBase table data from the source cluster to destination cluster by using the distcp command. (mostly distcp is a copy command for one cluster data to another cluster)

Step 3: Then create an Hbase table in the destination cluster (target cluster)

Step 4: After that import the Hbase table data from local to HBase table in the destination cluster.

Source Cluster:

1.  hbase.org.apache.hadoop.hbase.mapreduce.Driver export <hbase _table _name >  < source _hdfs _path >

2. hbase distcp hdfs :// <source_cluster_ipaddress:8020> to </source _hdfs _path>

3.hdfs: // < destination_cluster_ipaddress: 8020 > to <destination _hdfs _path>

Destination Cluster:

1.hbase org.hadoop.hbase.mapreduce.import < hbase _ table_ name > to < hbase _table _hdfs _path >

HBase multiple table migration from one cluster to another cluster:

We know how to Hbase single table migration then coming to multiple table migration from one cluster to another cluster in simple manner by below steps.

We have script files then simply multiple Hbase data migrations happening to go through below steps:

Step 1: First step place the hbase-export.sh and hbase-table.txt in the source cluster

Step 2: After that place the hbase -import.sh and hbase-table.txt in the destination cluster.

Step 3: Mention all the table list in the hbase-table.txt file

Step 4: Create all the HBase table on the destination cluster

Step 5: Execute the hbase-export-generic.sh in the source cluster

Step 6: Execute the hbase-import.sh in the destination cluster.
Summary: I tried in Cloudera Distribute Hadoop environment for Hbase data migration from one cluster to another cluster. For Hbase single table data and multiple table data migration in very simple for Hadoop administrator as well as Hadoop developers. It is the same as Hortonword Distribution also.