Monday, April 28, 2014

Walk-In for SAP ABAP On 3rd May 2014 @ Cognizant

Job Description


Cognizant hiring for SAP ABAP at CBE on 3rd May 2014
Experience: 3.5 - 11Years
Skills Required:
-SAP ABAP
-Experience in SAP ABAP programming
-ABAP Dictionary
-Reporting ALV Interface development by using anyone of the following methods like BDC,ALE IDOC, BAPI, LSMW.
For the above role the candidate should For the above role the candidate should possess a B.E. / B.Tech/ B.Sc/ BCA/ MCA/ M.E. / M.Tech/ M.Sc with consistent first class is mandatory
Work Location : Coimbatore
Walk in on: 3rd May 2014(Saturday)
Venue:
Cognizant Technology Solutions India Private Limited,
C3, CHIL Special Economic Zone,, Keeranatham Village,
Saravanampatti via, Coimbatore 641035
Time : 9.00 AM 12.00 Noon
If you would like to explore this opportunity please mail your updated resume to. We will revert back to you with more details on this requirement once we hear from you.
"Would be Highly Appreciated if you refer your Friends or Colleagues also"
Candidate Checklist :
-(Candidate must carry the following documents for the interview)
-Passport size photograph
-Last 3 months pay slip
-Offer letter and last increment letter from your current / last organization
-Photocopies of X, XII, graduation and post-graduation Certificates and Mark sheets (all semesters)
-Photocopies of Offer Letter or Experience Letters of ALL previous employments (mandatory)
-Photocopy of any one of the following ID Proofs is a MUST: Passport, Ration Card & Election ID card
-REGARDS
-TAG,CBE


For details check below link



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Thursday, April 24, 2014

Infosys BPO Hiring for SAP FICO Professional

Infosys BPO Hiring for SAP FICO Professional, Hyderabad, Dated 26th April 14, Saturday.

Job Description 1:
  • SAP FICO 2+ years of relevant experience.
  • The candidate should have Support/Implementation project experience
  • NO End-user experience,
  • Good communication,
  • Willing to work in 24/7 shifts (including night shift),
  • Willing to work under Infosys BPO payroll.
  • Experience: 2 to 4 years



Job Location : Hyderabad


Time : 9:30 AM to 5PM

Kindly Note:
  • Please Mention Source as "Direct Sourcing" on top of your Resume.
  • Selected candidates will be working under Infosys BPO.
  • Please carry updated profile and any address ID proof at the venue
Mandatory documents to be carried for the walk-in:

  1. Appointment & Relieving letter (Previous Employers if applicable)
  2. Current employer offer letter AND/OR Salary revision letter with annexure (if applicable) whichever pertains to your current salary.
  3.  Last 3 months salary slips.
  4. 10th Mark Sheet & Board Certificate
  5. 12th Mark sheet & Board Certificate
  6. All Graduation Mark Sheets (Each year separate mark Sheet & University Certificate)


Venue- Infosys Technologies Ltd, building 12
Survey No:210, Manikonda Village,Rajendranagar Mandal
Lingampally, Rangareddy District, Gachibowli
Hyderabad 500019, India

Timing- 10 am to 5 pm


Contact Person- Shaik Shahameer Babu


For details Check the below link

Infosys BPO Hiring for SAP FICO Professional

Friday, April 18, 2014

Hadoop Interview Questions and Answers -3

What is the Hadoop-core configuration? Hadoop core is configured by two xml files: 1. hadoop-default.xml which was renamed to 2. hadoop-site.xml. These files are written in xml format. We have certain properties in these xml files, which consist of name and value. But these files do not exist now. Which are the three modes in which Hadoop can be run? The three modes in which Hadoop can be run are: 1. standalone (local) mode 2. Pseudo-distributed mode 3. Fully distributed mode
 Explain what are the features of Stand alone (local) mode? In stand-alone mode there are no daemons, everything runs on a single JVM. It has no DFS and utilizes the local file system. Stand-alone mode is suitable only for running MapReduce programs during development. It is one of the most least used environments.
 What are the features of Pseudo mode? Pseudo mode is used both for development and in the QA environment. In the Pseudo mode all the daemons run on the same machine.
Can we call VMs as pseudos? No, VMs are not pseudos because VM is something different and pesudo is very specific to Hadoop.
 What are the features of Fully Distributed mode? Fully Distributed mode is used in the production environment, where we have „n‟ number of machines forming a Hadoop cluster. Hadoop daemons run on a cluster of machines.
There is one host onto which Namenode is running and another host on which datanode is running and then there are machines on which task tracker is running. We have separate masters and separate slaves in this distribution.
 Does Hadoop follows the UNIX pattern? Yes, Hadoop closely follows the UNIX pattern. Hadoop also has the „conf„ directory as in the case of UNIX.
 In which directory Hadoop is installed? Cloudera and Apache has the same directory structure. Hadoop is installed in cd /usr/lib/hadoop-0.20/.
 What are the port numbers of Namenode, job tracker and task tracker? The port number for Namenode is ‟70′, for job tracker is ‟30′ and for task tracker is ‟60′.
What are the Hadoop configuration files at present? There are 3 configuration files in Hadoop: 1. core-site.xml 2. hdfs-site.xml 3. mapred-site.xml These files are located in the conf/ subdirectory.
How to exit the Vi editor? To exit the Vi Editor, press ESC and type :q and then press enter.
 What is a spill factor with respect to the RAM? Spill factor is the size after which your files move to the temp file. Hadoop-temp directory is used for this.
 Is fs.mapr.working.dir a single directory? Yes, fs.mapr.working.dir it is just one directory.
Which are the three main hdfs-site.xml properties? The three main hdfs-site.xml properties are: 1. dfs.name.dir which gives you the location on which metadata will be stored and where DFS is located – on disk or onto the remote. 2. dfs.data.dir which gives you the location where the data is going to be stored. 3. fs.checkpoint.dir which is for secondary Namenode.
How to come out of the insert mode? To come out of the insert mode, press ESC, type :q (if you have not written anything) OR type :wq (if you have written anything in the file) and then press ENTER.

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What is Cloudera and why it is used? Cloudera is the distribution of Hadoop. It is a user created on VM by default. Cloudera belongs to Apache and is used for data processing.
What happens if you get a ‘connection refused java exception’ when you type hadoop fsck /?
It could mean that the Namenode is not working on your VM.
 We are using Ubuntu operating system with Cloudera, but from where we can download Hadoop or does it come by default with Ubuntu? This is a default configuration of Hadoop that you have to download from Cloudera or from Edureka‟s dropbox and the run it on your systems. You can also proceed with your own configuration but you need a Linux box, be it Ubuntu or Red hat. There are installation steps present at the Cloudera location or in Edureka‟s Drop box. You can go either ways. What does ‘jps’ command do? This command checks whether your Namenode, datanode, task tracker, job tracker, etc are working or not.
How can I restart Namenode? 1. Click on stop-all.sh and then click on start-all.sh OR 2. Write sudo hdfs (press enter), su-hdfs (press enter), /etc/init.d/ha (press enter) and then /etc/init.d/hadoop-0.20-namenode start (press enter).
What is the full form of fsck? Full form of fsck is File System Check.
How can we check whether Namenode is working or not? To check whether Namenode is working or not, use the command /etc/init.d/hadoop- 0.20-namenode status or as simple as jps.
 What does the command mapred.job.tracker do? The command mapred.job.tracker lists out which of your nodes is acting as a job tracker.
 What does /etc /init.d do? /etc /init.d specifies where daemons (services) are placed or to see the status of these daemons. It is very LINUX specific, and nothing to do with Hadoop
. How can we look for the Namenode in the browser? If you have to look for Namenode in the browser, you don‟t have to give localhost:8021, the port number to look for Namenode in the brower is 50070.
 How to change from SU to Cloudera? To change from SU to Cloudera just type exit.
Which files are used by the startup and shutdown commands? Slaves and Masters are used by the startup and the shutdown commands
. What do slaves consist of? Slaves consist of a list of hosts, one per line, that host datanode and task tracker servers.
What do masters consist of? Masters contain a list of hosts, one per line, that are to host secondary namenode servers.
 What does hadoop-env.sh do?
hadoop-env.sh provides the environment for Hadoop to run. JAVA_HOME is set over here.
 Can we have multiple entries in the master files? Yes, we can have multiple entries in the Master files.
Where is hadoop-env.sh file present? hadoop-env.sh file is present in the conf location
. In Hadoop_PID_DIR, what does PID stands for? PID stands for „Process ID‟.
What does /var/hadoop/pids do? It stores the PID.
What does hadoop-metrics.properties file do? hadoop-metrics.properties is used for „Reporting„ purposes. It controls the reporting for Hadoop. The default status is „not to report„.
What are the network requirements for Hadoop? The Hadoop core uses Shell (SSH) to launch the server processes on the slave nodes. It requires password-less SSH connection between the master and all the slaves and the secondary machines.
 Why do we need a password-less SSH in Fully Distributed environment? We need a password-less SSH in a Fully-Distributed environment because when the cluster is LIVE and running in Fully Distributed environment, the communication is too frequent. The job tracker should be able to send a task to task tracker quickly.
 Does this lead to security issues? No, not at all. Hadoop cluster is an isolated cluster. And generally it has nothing to do with an internet. It has a different kind of a configuration. We needn‟t worry about that kind of a security breach, for instance, someone hacking through the internet, and so on. Hadoop has a very secured way to connect to other machines to fetch and to process data.
On which port does SSH work? SSH works on Port No. 22, though it can be configured. 22 is the default Port number.
Can you tell us more about SSH? SSH is nothing but a secure shell communication, it is a kind of a protocol that works on a Port No. 22, and when you do an SSH, what you really require is a password.
Why password is needed in SSH localhost? Password is required in SSH for security and in a situation where passwordless communication is not set.
Do we need to give a password, even if the key is added in SSH? Yes, password is still required even if the key is added in SSH.

Thursday, April 17, 2014

Hadoop Interview Questions and Answers -2



When is the reducers are started in a MapReduce job?
In a MapReduce job reducers do not start executing the reduce method until the all Map jobs have completed. Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The programmer defined reduce method is called only after all the mappers have finished. If reducers do not start before all mappers finish then why does the progress on MapReduce job shows something like Map(50%) Reduce(10%)? Why reducers progress percentage is displayed when mapper is not finished yet? Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The progress calculation also takes in account the processing of data transfer which is done by reduce process, therefore the reduce progress starts showing up as soon as any intermediate key-value pair for a mapper is available to be transferred to reducer. Though the reducer progress is updated still the programmer defined reduce method is called only after all the mappers have finished.  
What is HDFS ? How it is different from traditional file systems? HDFS, the Hadoop Distributed File System, is responsible for storing huge data on the cluster. This is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. HDFS provides high throughput access to application data and is suitable for applications that have large data sets. HDFS is designed to support very large files. Applications that are compatible with HDFS are those that deal with large data sets. These applications write their data only once but they read it one or more times and require these reads to be satisfied at streaming speeds. HDFS supports write-once-read-many semantics on files.
 What is HDFS Block size? How is it different from traditional file system block size? In HDFS data is split into blocks and distributed across multiple nodes in the cluster. Each block is typically 64Mb or 128Mb in size. Each block is replicated multiple times. Default is to replicate each block three times. Replicas are stored on different nodes. HDFS utilizes the local file system to store each HDFS block as a separate file. HDFS Block size can not be compared with the traditional file system block size.
What is a NameNode? How many instances of NameNode run on a Hadoop Cluster? The NameNode is the centerpiece of an HDFS file system. It keeps the directory tree of all files in the file system, and tracks where across the cluster the file data is kept. It does not store the data of these files itself. There is only One NameNode process run on any hadoop cluster. NameNode runs on its own JVM process. In a typical production cluster its run on a separate machine. The NameNode is a Single Point of Failure for the HDFS Cluster. When the NameNode goes down, the file system goes offline. Client applications talk to the NameNode whenever they wish to locate a file, or when they want to add/copy/move/delete a file. The NameNode responds the successful requests by returning a list of relevant DataNode servers where the data lives. 



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What is a DataNode? How many instances of DataNode run on a Hadoop Cluster? A DataNode stores data in the Hadoop File System HDFS. There is only One DataNode process run on any hadoop slave node. DataNode runs on its own JVM process. On startup, a DataNode connects to the NameNode. DataNode instances can talk to each other, this is mostly during replicating data.
How the Client communicates with HDFS?
The Client communication to HDFS happens using Hadoop HDFS API. Client applications talk to the NameNode whenever they wish to locate a file, or when they want to add/copy/move/delete a file on HDFS. The NameNode responds the successful requests by returning a list of relevant DataNode servers where the data lives. Client applications can talk directly to a DataNode, once the NameNode has provided the location of the data.

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 How the HDFS Blocks are replicated? HDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and have strictly one writer at any time. The NameNode makes all decisions regarding replication of blocks. HDFS uses rack-aware replica placement policy. In default configuration there are total 3 copies of a datablock on HDFS, 2 copies are stored on datanodes on same rack and 3rd copy on a different rack.

Monday, April 7, 2014

Hadoop Interview Questions and Answers -1




 What is a JobTracker in Hadoop? How many instances of JobTracker run on a Hadoop Cluster? JobTracker is the daemon service for submitting and tracking MapReduce jobs in Hadoop. There is only One Job Tracker process run on any hadoop cluster. Job Tracker runs on its own JVM process. In a typical production cluster its run on a separate machine. Each slave node is configured with job tracker node location. The JobTracker is single point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted. JobTracker in Hadoop performs following actions(from Hadoop Wiki:)
 Client applications submit jobs to the Job tracker. The JobTracker talks to the NameNode to determine the location of the data The JobTracker locates TaskTracker nodes with available slots at or near the data The JobTracker submits the work to the chosen TaskTracker nodes. The TaskTracker nodes are monitored. If they do not submit heartbeat signals often enough, they are deemed to have failed and the work is scheduled on a different TaskTracker. A TaskTracker will notify the JobTracker when a task fails. The JobTracker decides what to do then: it may resubmit the job elsewhere, it may mark that specific record as something to avoid, and it may may even blacklist the TaskTracker as unreliable. When the work is completed, the JobTracker updates its status. Client applications can poll the JobTracker for information.

 How JobTracker schedules a task? The TaskTrackers send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These message also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated. When the JobTracker tries to find somewhere to schedule a task within the MapReduce operations, it first looks for an empty slot on the same server that hosts the DataNode containing the data, and if not, it looks for an empty slot on a machine in the same rack.

 What is a Task Tracker in Hadoop? How many instances of TaskTracker run on a Hadoop Cluster A TaskTracker is a slave node daemon in the cluster that accepts tasks (Map, Reduce and Shuffle operations) from a JobTracker. There is only One Task Tracker process run on any hadoop slave node. Task Tracker runs on its own JVM process. Every TaskTracker is configured with a set of slots, these indicate the number of tasks that it can accept. The TaskTracker starts a separate JVM processes to do the actual work (called as Task Instance) this is to ensure that process failure does not take down the task tracker. The TaskTracker monitors these task instances, capturing the output and exit codes. When the Task instances finish, successfully or not, the task tracker notifies the JobTracker. The TaskTrackers also send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These message also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated.

 What is a Task instance in Hadoop? Where does it run? Task instances are the actual MapReduce jobs which are run on each slave node. The TaskTracker

starts a separate JVM processes to do the actual work (called as Task Instance) this is to ensure that process failure does not take down the task tracker. Each Task Instance runs on its own JVM process. There can be multiple processes of task instance running on a slave node. This is based on the number of slots configured on task tracker. By default a new task instance JVM process is spawned for a task. How many Daemon processes run on a Hadoop system? Hadoop is comprised of five separate daemons. Each of these daemon run in its own JVM. Following 3 Daemons run on Master nodes NameNode - This daemon stores and maintains the metadata for HDFS. Secondary NameNode - Performs housekeeping functions for the NameNode. JobTracker - Manages MapReduce jobs, distributes individual tasks to machines running the Task Tracker. Following 2 Daemons run on each Slave nodes DataNode – Stores actual HDFS data blocks. TaskTracker - Responsible for instantiating and monitoring individual Map and Reduce tasks.

What is configuration of a typical slave node on Hadoop cluster? How many JVMs run on a slave node? Single instance of a Task Tracker is run on each Slave node. Task tracker is run as a separate JVM process. Single instance of a DataNode daemon is run on each Slave node. DataNode daemon is run as a separate JVM process. One or Multiple instances of Task Instance is run on each slave node. Each task instance is run as a separate JVM process. The number of Task instances can be controlled by configuration. Typically a high end machine is configured to run more task instances.

What is the difference between HDFS and NAS ? The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. Following are differences between HDFS and NAS In HDFS Data Blocks are distributed across local drives of all machines in a cluster. Whereas in NAS data is stored on dedicated hardware. HDFS is designed to work with MapReduce System, since computation are moved to data. NAS is not suitable for MapReduce since data is stored seperately from the computations. HDFS runs on a cluster of machines and provides redundancy usinga replication protocal. Whereas NAS is provided by a single machine therefore does not provide data redundancy.

How NameNode Handles data node failures? NameNode periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode. When NameNode notices that it has not recieved a hearbeat message from a data node after a certain amount of time, the data node is marked as dead. Since blocks will be under replicated the system begins replicating the blocks that were stored on the dead datanode. The NameNode Orchestrates the replication of data blocks from one datanode to another. The replication data transfer happens directly between datanodes and the data never passes through the namenode.

 Does MapReduce programming model provide a way for reducers to communicate with each other? In a MapReduce job can a reducer communicate with another reducer? Nope, MapReduce programming model does not allow reducers to communicate with each other. Reducers run in isolation.

Can I set the number of reducers to zero? Yes, Setting the number of reducers to zero is a valid configuration in Hadoop. When you set the reducers to zero no reducers will be executed, and the output of each mapper will be stored to a separate file on HDFS. [This is different from the condition when reducers are set to a number greater than zero and the Mappers output (intermediate data) is written to the Local file system(NOT HDFS) of each mappter slave node.] Where is the Mapper Output (intermediate kay-value data) stored ? The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.

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 What are combiners? When should I use a combiner in my MapReduce Job? Combiners are used to increase the efficiency of a MapReduce program. They are used to aggregate intermediate map output locally on individual mapper outputs. Combiners can help you reduce the amount of data that needs to be transferred across to the reducers. You can use your reducer code as a combiner if the operation performed is commutative and associative. The execution of combiner is not guaranteed, Hadoop may or may not execute a combiner. Also, if required it may execute it more then 1 times. Therefore your MapReduce jobs should not depend on the combiners execution. What is Writable & WritableComparable interface? org.apache.hadoop.io.Writable is a Java interface. Any key or value type in the Hadoop Map-Reduce framework implements this interface. Implementations typically implement a static read(DataInput) method which constructs a new instance, calls readFields(DataInput) and returns the instance. org.apache.hadoop.io.WritableComparable is a Java interface. Any type which is to be used as a key in the Hadoop Map-Reduce framework should implement this interface. WritableComparable objects can be compared to each other using Comparators.



What is the Hadoop MapReduce API contract for a key and value Class? The Key must implement the org.apache.hadoop.io.WritableComparable interface. The value must implement the org.apache.hadoop.io.Writable interface

. What is a IdentityMapper and IdentityReducer in MapReduce ? org.apache.hadoop.mapred.lib.IdentityMapper Implements the identity function, mapping inputs directly to outputs. If MapReduce programmer do not set the Mapper Class using JobConf.setMapperClass then IdentityMapper.class is used as a default value. org.apache.hadoop.mapred.lib.IdentityReducer Performs no reduction, writing all input values directly to the output. If MapReduce programmer do not set the Reducer Class using JobConf.setReducerClass then IdentityReducer.class is used as a default value.

 What is the meaning of speculative execution in Hadoop? Why is it important? Speculative execution is a way of coping with individual Machine performance. In large clusters where hundreds or thousands of machines are involved there may be machines which are not performing as fast as others. This may result in delays in a full job due to only one machine not performaing well. To avoid this, speculative execution in hadoop can run multiple copies of same map or reduce task on different slave nodes. The results from first node to finish are used.