Must-Know [LATEST] FileNet Interview Questions and Answers

1. What daemons are required to run a Hadoop cluster?

Ans:

DataNode, NameNode, TaskTracker, and JobTracker area unit needed to run Hadoop clusters.

2.Which OS is supported by Hadoop deployment?

Ans:

The main OS used for Hadoop is the UNIX system. However, by mistreating some further code, it will be deployed on the Windows platform.

3. What area unit the common Input Formats in Hadoop?

Ans:

Three wide used input formats are:

Text Input: it’s the default input format in Hadoop.

Key Value: it’s used for plain text files.

Sequence: Use for reading files in sequence.

4. What modes will Hadoop code be run in?

Ans:

Hadoop will be deployed in:

• Standalone mode.
• Pseudo-distributed mode.
• Fully distributed mode.

5. What’s the biggest distinction between RDBMS and Hadoop?

Ans:

RDMS	Hadoop
RDBMS is a data management system that relies on a knowledge model.	It’s AN ASCII text file code framework used for storing information and running applications on a gaggle of artifact hardware.
In RDBMS tables are units used for data storage.	it’s massive storage capability and high process power.

6. What area unit the vital hardware needs for a Hadoop cluster?

Ans:

There aren’t any specific needs for information nodes. However, the namenodes would like a selected quantity of RAM to store filesystem pictures in memory. This relies on the actual style of the first and secondary namenode.

7.How would you deploy completely different elements of Hadoop in production?

Ans:

8. What does one ought to do as Hadoop admin when adding new datanodes?

Ans:

You need to start out the balancer for redistributing information equally between all nodes in order that the Hadoop cluster can realize new information nodes mechanically. To optimize the cluster performance, you ought to begin rebalancer to spread the information between datanodes.

9. What area unit the Hadoop shell commands that may use for copy operation?

Ans:

The copy operation command are:

fs – copyToLocal

fs – put

fs – copyFromLocal

10. What’s the Importance of the namenode?

Ans:

The role of namenode is incredibly crucial in Hadoop. It’s the brain of Hadoop. It’s mostly to blame for managing the distribution blocks on the system. It conjointly provides the precise addresses for the information supported once the shopper created a letter of invitation.

11.Explain however you may restart a NameNode?

Ans:

The easiest means of doing this is to run the command to prevent running shell scripts.

12. What happens once the NameNode is down?

Ans:

If the NameNode is down, the filing system goes offline.

13.Is it attainable to repeat files between completely different clusters? If affirmative, however are you able to come through?

Ans:

Yes, we are able to copy files between multiple Hadoop clusters. This will be done by mistreating distributed copy.

14. Is there any customary technique to deploy Hadoop?

Ans:

No, there are currently customary procedures to deploy information mistreatment Hadoop. There are a few general needs for all Hadoop distributions. However, the precise strategies can perpetually show a discrepancy for every Hadoop admin.

15. What’s distcp?

Ans:

Distcp may be a Hadoop copy utility. It’s principally used for playing MapReduce jobs to repeat information. The key challenges within the Hadoop surroundings is repetition information across varied clusters, and distcp will provide multiple datanodes for parallel repetition of the information.

16.What is rack awareness?

Ans:

It is a way that decides a way to place blocks that support the rack definitions. Hadoop can attempt to limit the network traffic between information nodes that are given within the same rack. So that, it’ll solely contact the remote.

17.What is the employment of the ‘jps’ command?

Ans:

The ‘jps’ command helps North American nations to search out whether or not the Hadoop daemons area unit is running or not. It conjointly displays all the Hadoop daemons like namenode, datanode, node manager, resource manager, etc. that area unit running on the machine.

18.Name a number of the essential Hadoop tools for effectively operating with massive Data?

Ans:

“Hive,” HBase, HDFS, ZooKeeper, NoSQL, Lucene/SolrSee, Avro, Oozie, Flume, Clouds, and SQL area unit a number of the Hadoop tools that enhance the performance of huge information.

19. What percentage times does one ought to reformat the namenode?

Ans:

The namenode solely must be formatted once within the starting. After that, it’ll ne’er be shaped. In fact, reformatting of the namenode will cause loss of the information on the whole namenode.

20. What’s speculative execution?

Ans:

If a node is capital punishment a task slower then the master node. Then there’s a necessity to redundantly execute another instance of identical task on another node. That the task that finishes 1st is accepted and also the alternative one doubtless to be killed. This method is understood as “speculative execution.”

21. What’s massive Data?

Ans:

Big information may be a term that describes the massive volume of information. massive information will be wont to create higher selections and strategic business moves.

22.What is Hadoop and its components?

Ans:

When “Big Data” emerged as a drag, Hadoop evolved as an answer for it. it’s a framework that provides varied services or tools to store and method massive information. It conjointly helps to research massive information and to create business selections that area unit troublesome mistreatment of the normal technique.

23. What square measures the essential options of Hadoop?

Ans:

Hadoop framework has the competency of finding several queries for large information analysis. It’s designed on Google MapReduce that is predicated on Google’s massive record systems.

24.Discuss the distinction between RDBMS and Hadoop?

Ans:

Name	RDBMS	Hadoop
Data volume RDBMS	Throughput RDBMS fails to realize a high outturn Hadoop achieves high Throughput.	cannot store and method an oversized quantity {of information|of knowledge|of information} Hadoop works higher for big amounts of data.It will simply store and method an oversized quantity of information compared to RDBMS.
Data variety Schema of {the information|the info|The information}	is taught in RDBMS and it continuously depends on the structured data.	It stores any reasonable information. whether or not it might be structured, unstructured, or semi-structured.
Read/Write	Speed Reads square measure quick in RDBMS as a result of the schema of the info is already illustrious.	Writes square measure quickly in Hadoop as a result of no schema validation happening throughout HDFS write.
Cost RDBMS	could be a accredited software.	Hadoop could be a free and ASCII text file framework.

25.Explain massive information and its characteristics?

Ans:

Big information refers to an oversized quantity of information that exceeds the process capability of typical info systems and needs a special data processing mechanism. This information will be either structured or unstructured information.

Characteristics of huge Data:

Volume – It represents the number of information that’s increasing at an associated exponential rate i.e. in gigabytes, Petabytes, Exabytes, etc.

Velocity – speed refers to the speed at which information is generated, modified, and processed. At present, Social media could be a major contributor to the rate of growing information.

Variety – It refers to information coming back from a spread of sources like audios, videos, CSV, etc. It will be either structured, unstructured, or semi-structured.

Veracity – truthfulness refers to general or unsure information.

Value – this can be the foremost necessary component of huge information. It includes information on the way to access and deliver quality analytics to the organization. It provides a good value on the used technology.

26.What is Hadoop and list its components?

Ans:

Hadoop is an ASCII text file framework used for storing massive information sets and runs applications across clusters of goods hardware. It offers in depth storage for any style of information and may handle endless parallel tasks.

Core parts of Hadoop:

Storage unit– HDFS (DataNode, NameNode)

Processing framework– YARN (NodeManager, ResourceManager)

27.What is YARN and justify its components?

Ans:

Yet Another Resource negociant (YARN) is during all|one amongst|one in every of} the core parts of Hadoop and is accountable for managing resources for the assorted applications operational in a Hadoop cluster, and additionally schedules tasks on completely different cluster nodes:

Resource Manager – It runs on a master daemon and controls the resource allocation within the cluster.

Node Manager – It runs on a slave daemon and is accountable for the execution of tasks for every single information Node.

Application Master – It maintains the user job lifecycle and resource necessities of individual applications. It operates in conjunction with the Node Manager and controls the execution of tasks.

Container – it’s a mixture of resources like Network, HDD, RAM, CPU, etc., on one node.

28.What is the distinction between an everyday classification system and HDFS?

Ans:

Regular File Systems	HDFS
A small block size of information (like 512 bytes).	Large block size (orders of 64MB).
Multiple disks obtain massive files.	Reads information consecutive once single obtain.

29.What square measures the Hadoop daemons and justify their roles during a Hadoop cluster?

Ans:

Generally, the daemon is nothing but a method that runs within the background. Hadoop has 5 such daemons. They are:

NameNode – is that the Master node accountable to store the meta-data for all the directories and files.

DataNode – it’s the Slave node accountable to store the particular information.

Secondary NameNode – it’s accountable for the backup of NameNode and stores the complete information {of information|of knowledge|of information} nodes like data node properties, addresses, and block reports of every information node.

JobTracker – it’s used for making and running jobs. It runs on information nodes and allocates the duty to TaskTracker.

30.What is Avro publishing in Hadoop?

Ans:

The process of translating objects or information structures into binary or matter type is named Avro publishing. it’s outlined as a language-independent schema (written in JSON).

31. However, are you able to skip the dangerous records in Hadoop?

Ans:

Hadoop provides a feature referred to as SkipBadRecords category for skipping dangerous records whereas process mapping inputs.

32.Explain HDFS and its components?

Ans:

HDFS (Hadoop Distributed File System) is the primary information storage unit of Hadoop.It stores varied forms of information as blocks during a distributed atmosphere and follows master and slave topology.

HDFS components:

NameNode – it’s the master node and is accountable for maintaining the information for the blocks of information kept in HDFS. It manages all the DataNodes.

DataNode – it’s the slave node and accountable for storing information within the HDFS.

33.What square measures the options of HDFS?

Ans:

• Supports storage of terribly massive datasets.
• Write once scan several access model.
• Streaming information access.
• Replication mistreatment goods hardware.
• HDFS is extremely Fault Tolerant.
• Distributed Storage.

34.Fault replication factor?

Ans:

Replication issue suggests that the minimum range of times the file can replicate(copy) across the cluster. The default replication issue is three.

35.List the assorted HDFS Commands?

Ans:

The Various HDFS Commands are listed below:

• version
• mkdir
• ls
• put
• copy from native
• get
• copyToLocal
• cat
• mv
• cp

36.Compare HDFS (Hadoop Distributed File System) and NAS (Network connected Storage)?

Ans:

HDFS	NAS
It is a distributed filing system used for storing knowledge by trade goods hardware.	It’s a file-level laptop knowledge storage server connected to a network, providing network access to a heterogeneous cluster of purchasers.
It includes trade goods hardware that may be able to} be efficient. NAS is a high-end memory device that has a high value.	it’s designed to figure for the MapReduce paradigm. it’s not appropriate for MapReduce.

37.What are the restrictions of Hadoop one.0?

Ans:

NameNode: No Horizontal measurability and No High convenience.

Job Tracker: burdened.

MRv1: It will solely perceive Map and scale back tasks.

38. A way to commission (adding) the nodes within the Hadoop cluster?

Ans:

Update the network addresses within the dfs.include and mapred.include:

Update the NameNode: Hadoop dfsadmin -refreshNodes.

Update the Jobtracker: Hadoop mradmin-refreshNodes.

Update the slave file: Start the DataNode and NodeManager on the supplementary Node.

39.A way to call in (removing) the nodes within the Hadoop cluster?

Ans:

Update the network addresses within the dfs.exclude and mapred.exclude:

Update the Namenode: $ Hadoop dfsadmin -refreshNodes.

Update the JobTracker: Hadoop mr admin -refreshNodes.

Cross-check the online UI it’ll show “Decommissioning in Progress”:

Remove the Nodes from the embody file and so run: Hadoop dfsadmin-refreshNodes, Hadoop mradmin -refreshNodes.Remove the Nodes from the slave file.

40.Compare Hadoop one.x and Hadoop two.x

Ans:

Name	Hadoop 1.x	Hadoop 2.x
1. NameNode	In Hadoop one.x, NameNode is that the single purpose of failure.	In Hadoop two.x, we’ve got each Active and passive NameNodes.
2. Processing	MRV1 (Job huntsman & Task Tracker).	MRV2/YARN (ResourceManager & NodeManager)

41.What is a checkpoint?

Ans:

Checkpointing could be a technique that takes a FsImage. It edits logs and compacts them into a replacement FsImage. Therefore, rather than replaying AN edit log, the NameNode may be loaded within the final in-memory state directly from the FsImage. This is often an additional economical operation that reduces NameNode startup time.

42. What’s the distinction between active and passive NameNodes?

Ans:

Active NameNode works and runs within the cluster.Passive NameNode has similar knowledge as active NameNode and replaces it once it fails.

43. However, can you resolve the NameNode failure issue?

Ans:

The following steps got to be dead to resolve the NameNode issue and create the Hadoop cluster up and running:

• Use the FsImage (file system information replica) to start out a replacement NameNode.
• Now, assemble DataNodes and purchasers, in order that they’ll acknowledge the new NameNode, that’s started.
• The new NameNode can begin serving the shopper once it’s completed loading the last stop FsImage and enough block reports from the DataNodes.

44. List the various varieties of Hadoop schedulers?

Ans:

• Hadoop inventory accounting computer hardware.
• Hadoop honest computer hardware.
• Hadoop capability computer hardware.

45.How to keep AN HDFS cluster balanced?

Ans:

However, it’s out of the question to limit a cluster from changing into unbalanced. so as to provide a balance to a definite threshold among knowledge nodes, use the Balancer tool. This tool tries to even out the block knowledge distribution across the cluster.

46. What’s HDFS Federation?

Ans:

HDFS Federation enhances this HDFS design through a transparent separation of namespace and storage by enabling a generic block storage layer. It provides multiple namespaces within the cluster to boost measurability and isolation.

47. What’s HDFS High Availability?

Ans:

HDFS High convenience is introduced in Hadoop two.0. It suggests providing support for multiple NameNodes to the Hadoop design.

48. What’s a rack-aware duplicate placement policy?

Ans:

• Rack Awareness is the algorithmic rule used for raising the network traffic whereas reading/writing HDFS files to the Hadoop cluster by NameNode.
• NameNode chooses the Datanode that is nearer to constant rack or near rack for reading/Write request. The thought of selecting nearer knowledge nodes supported racks info is named Rack Awareness.
• Considering the replication issue is three for knowledge blocks on HDFS it suggests that for each block of information 2 copies are kept on a constant rack, whereas the third copy is kept on a distinct rack. This rule is named duplicate Placement Policy.

49. What’s the purpose of the Hadoop command?

Ans:

Hadoop command is employed for checking the HDFS filing system. There are totally {different|completely different} arguments which will be passed with this command to emit different results.

Hadoop / -files: Displays all the files in HDFS whereas checking.

Hadoop / -files -blocks: Displays all the blocks of the files whereas checking.

Hadoop / -files -blocks -locations: Displays all the files block locations whereas checking.

Hadoop -move: Moves the corrupted files to a specific directory.

50. What’s the aim of a DataNode block scanner?

Ans:

The purpose of the DataNode block scanner is to work and sporadically check all the blocks that are kept on the DataNode. If unhealthy blocks are detected they’re going to be fastened before any shopper reads.

51. What’s the aim of the admin tool?

Ans:

dfsadmin tool is employed for examining the HDFS cluster standing:

dfsadmin – report command produces helpful info regarding basic statistics of the cluster like DataNodes and NameNode standing, disk capability configuration, etc.It performs all the executive tasks on the HDFS.

52. What’s the command used for printing the topology?

Ans:

dfsadmin -point topology is employed for printing the topology. It displays the tree of racks and DataNodes connected to the tracks.

53. What’s RAID?

Ans:

RAID (redundant array of freelance disks) could be a knowledge storage virtualization technology used for rising performance and knowledge redundancy by combining multiple disk drives into one entity.

54. Will Hadoop need RAID?

Ans:

In DataNodes, RAID isn’t necessary as storage is achieved by replication between the Nodes. In NameNode’s disk RAID is suggested.

55. List the varied site-specific configuration files offered in Hadoop?

Ans:

• conf/Hadoop-env.sh
• conf/yarn-site.xml
• conf/yarn-env.sh
• conf/mapred-site.xml
• conf/hdfs-site.xml
• conf/core-site.xml

56. What’s the most practicality of NameNode?

Ans:

It is chiefly accountable for: Namespace – Manages data of HDFS.

57. That command is employed to format the NameNode?

Ans:

$ namenode -format.

58. However a shopper application interacts with the NameNode?

Ans:

• Client applications associate the Hadoop HDFS API with the NameNode once it’s to copy/move/add/locate/delete a file.
• The NameNode returns to the victorious requests by delivering a listing of relevant DataNode servers wherever the information is residing.
• The shopper will speak on to a DataNode when the NameNode has given the situation of the information.

59. What’s MapReduce and list its features?

Ans:

MapReduce could be a programming model used for processing and generating giant datasets on the clusters with parallel and distributed algorithms.

60. What are the options of MapReduce?

Ans:

The key and value types (K1 and V1) for the map’s input are generally distinct from those for the map’s output ( K2 and V2 ). Although the reduce output types may differ once more, the reduce input must nevertheless have the same kinds as the map output ( K3 and V3 ).

61. What will the MapReduce framework consist of?

Ans:

MapReduce framework is employed to write down applications for processing massive information in parallel on massive clusters of goods hardware. It consists of:

ResourceManager (RM):

• Global resource hardware.
• One master RM.

NodeManager (NM):

• One slave NM per cluster node.
• Container.
• RM creates Containers upon request by AM.
• The application runs in one or additional containers.

ApplicationMaster (AM):

• One AM per application.
• Runs in instrumentation.

62. What are the 2 main parts of ResourceManager?

Ans:

Scheduler- It allocates the resources (containers) to numerous running applications supported resource accessibility and organized shared policy.

ApplicationManager- it’s primarily liable for managing a group of submitted applications

63. What’s a Hadoop counter?

Ans:

Hadoop Counters measures the progress or tracks the amount of operations that occur among a MapReduce job. Counters are helpful for collection statistics regarding MapReduce jobs for application-level or internal control.

64. What are the most configuration parameters for a MapReduce application?

Ans:

The job configuration needs the following:

• Job’s input and output locations within the distributed filing system.
• The input format of information.
• The output format of information.
• Class containing the map operate and reduced operate.
• JAR file containing the reducer, driver, and plotter categories.

65. What are the steps concerned to submit a Hadoop job?

Ans:

Steps concerned in Hadoop job submission:

• Hadoop job consumers submit the duty jar/executable and configuration to the ResourceManager.
• ResourceManager then programing tasks and observance them.
• Finally, job standing and diagnostic data are provided to the consumer.

66. However will the MapReduce framework read its input internally?

Ans:

It views the computer file set as a group of pairs and processes the map tasks in a very utterly parallel manner.

67. What are the essential parameters of Mapper?

Ans:

The basic parameters of plotter ar listed below:

1. LongWritable and Text

2. Text and IntWritable

68. What are Writables and make a case for their importance in Hadoop?

Ans:

• Writables are interfaces in Hadoop. They act as a wrapper category to the majority of primitive knowledge kinds of Java.
• A serializable object that executes an easy and economical publication protocol, supported DataInput and DataOutput.
• Writables are used for making serialized knowledge sorts in Hadoop.

69. Why is comparison of sorts vital for MapReduce?

Ans:

• It is necessary for MapReduce as within the sorting section the keys are compared with each other.
• For a Comparison of sorts, the WritableComparable interface is enforced.

70. What’s “speculative execution” in Hadoop?

Ans:

In Apache Hadoop, if nodes don’t fix or diagnose the slow-running tasks, the master node will redundantly perform another instance of identical task on another node as a backup (the backup task is termed a Speculative task). This method is termed Speculative Execution in Hadoop.

71. What are the ways used for restarting the NameNode in Hadoop?

Ans:

The ways used for restarting the NameNodes ar the following:

• You can use /sbin/hadoop-daemon.sh stop namenode command for stopping the NameNode one by one then begin the NameNode victimization /sbin/hadoop-daemon.sh begin namenode.
• Use /sbin/stop-all.sh then use /sbin/start-all.sh command for stopping all the demons 1st then begin all the daemons.

72. What’s the distinction between associate degree “HDFS Block” and “MapReduce Input Split”?

Ans:

• HDFS Block is the physical division of the disk that has the minimum quantity of information which will be read/write, whereas MapReduce InputSplit is the logical division of information created by the InputFormat per the MapReduce job configuration.
• HDFS divides knowledge into blocks, whereas MapReduce divides knowledge into input splits and empowers them to plotter operate.

73. What are the various modes within which Hadoop will run?

Ans:

Standalone Mode(local mode) – this can be the default mode wherever Hadoop is designed to run. During this mode, all the elements of Hadoop like DataNode, NameNode, etc., run as one Java method and are helpful for debugging.

Pseudo Distributed Mode(Single-Node Cluster) – Hadoop runs on one node in a very pseudo-distributed mode. every Hadoop daemon works in a very separate Java method in Pseudo-Distributed Mode, whereas in native mode, every Hadoop daemon operates as one Java method.

Fully distributed mode (or multiple node cluster) – All the daemons are dead in separate nodes building into a multi-node cluster within the fully-distributed mode.

74. Why can’t aggregation be performed in Mapperside?

Ans:

We cannot perform Aggregation in mapping as a result of it needs sorting of information, that happens solely at the Reducer aspect.For aggregation, we’d like the output from all the plotter functions, that isn’t attainable throughout the map section as map tasks are going to be running in several nodes, wherever knowledge blocks are gifts.

75. What’s the importance of “RecordReader” in Hadoop?

Ans:

RecordReader in Hadoop uses the information from the InputSplit as input and converts it into Key-value pairs for plotter.

76. What’s the aim of Distributed Cache in a MapReduce Framework?

Ans:

The Purpose of Distributed Cache within the MapReduce framework is to cache files once required by the applications. When you have cached a file for employment, the Hadoop framework can build it offered to each and every knowledge node wherever map/reduces tasks are operative.

77. However, do reducers communicate with one another in Hadoop?

Ans:

Reducers continuously run in isolation and also the Hadoop Mapreduce programming paradigm ne’er permits them to speak with one another.

78. What’s an Identity Mapper?

Ans:

Identity plotter may be a default plotter category that mechanically works once no plotter is laid out in the MapReduce driver category. IdentityMapper.class is employed as a default value once JobConf.setMapperClass isn’t set.

79. What area unit the phases of MapReduce Reducer?

Ans:

The MapReduce reducer has 3 phases:

Shuffle section – during this section, the sorted output from a plotter is AN input to the Reducer. This framework can fetch the relevant partition of the output of all the mappers by exploitation communications protocol.

Sort section – during this section, the input from varied mappers is sorted supported connected keys. This framework teams reduce inputs by keys. Shuffle and type phases occur at the same time.

Reduce section – during this section, scale back task aggregates the key-value pairs once shuffling and sorting phases. The OutputCollector.collect() technique, writes the output of the scale back task to the Filesystem.

80. What’s the aim of MapReduce Partitioner in Hadoop?

Ans:

It makes it positive that everyone the values of one key pass to the same reducers by permitting the even distribution over the reducers.

81. However, can you write a custom partitioner for a Hadoop MapReduce job?

Ans:

• Build a brand new category that extends Partitioner category.
• Override the get partition technique within the wrapper.
• Add the custom partitioner to the duty as a config file or by exploitation the strategy set Partitioner.

82. What’s a Combiner?

Ans:

A Combiner may be a semi-reducer that executes the native scale back task. It receives inputs from the Map category and passes the output key-value pairs to the reducer category.

83. What’s the utilization of SequenceFileInputFormat in Hadoop?

Ans:

It’s a compressed computer file format optimized for passing the info between outputs of 1 MapReduce job to the input of another MapReduce job.

84. What’s Apache Pig?

Ans:

Apache Pig may be a high-level scripting language used for making programs to run on Apache Hadoop.The language employed in this platform is named Pig Latin.

85. What area unit the advantages of Apache Pig over MapReduce?

Ans:

• Pig Latin may be a high-level scripting language whereas MapReduce may be a low-level processing paradigm.
• Without several complicated Java implementations in MapReduce, programmers will perform an equivalent implementation terribly simply exploiting Pig Latin.
• Apache Pig decreases the length of the code by approx twenty times (according to Yahoo). Hence, this reduces development time by nearly sixteen times.
• Pig offers varied inbuilt operators for information operations like filters, joins, sorting, ordering, etc., whereas to perform these same functions in MapReduce is a vast task.

86. What area unit the Hadoop Pig information types?

Ans:

Hadoop Pig runs atomic information varieties and complicated information varieties:

Atomic information varieties: These areas unit the fundamental information types that are employed in all the languages like int, string, float, long, etc.

Complex information Types: These area units Bag, Map, and Tuple.

87. List the varied relative operators employed in “Pig Latin”?

Ans:

• SPLIT
• LIMIT
• CROSS
• COGROUP
• GROUP
• STORE
• DISTINCT
• ORDER BY
• JOIN
• FILTER
• FOREACH
• LOAD

88. What’s Apache Hive?

Ans:

Apache Hive offers an info question interface to Apache Hadoop. It reads, writes, and manages massive datasets that are units residing in distributed storage and queries through SQL syntax.

89. Where does Hive store table information in HDFS?

Ans:

/usr/hive/warehouse is the default location wherever Hive stores the table information in HDFS.

90. will the default “Hive Metastore” be employed by multiple users (processes) at an equivalent time?

Ans:

By default, Hive Metastore uses the hat info. So, it’s unattainable for multiple users or processes to access it at an equivalent time.

91. What’s a SerDe?

Ans:

SerDe may be a combination of Serializer and Deserializer. It interprets the results of a record ought to be processed by permitting Hive to browse and write from a table.

92. What square measures the variations between Hive and RDBMS?

Ans:

Hive	RDBMS
Schema on Reading.	Schema on write.
Batch processing jobs.	Real-time jobs.
Data hold on on HDFS.	knowledge hold on on the inner structure.
Processed victimization MapReduce.	Processed victimization info.

93. What’s Associate in Nursing Apache HBase?

Ans:

Apache HBase is two-dimensional and a column-oriented key datastore runs on prime of HDFS (Hadoop Distributed File System). it’s designed to supply high table-update rates and a fault-tolerant thanks to storing an oversized assortment of distributed knowledge sets.

94. What square measures the assorted parts of Apache HBase?

Ans:

Region Server: These squares measure the employee nodes that handle scan, write, update, and delete requests from purchasers. The region Server method runs on every and each node of the Hadoop cluster.

HMaster: It monitors and manages the Region Server within the Hadoop cluster for load leveling.

ZooKeeper: ZHBase employs ZooKeeper for a distributed atmosphere. It keeps track of every and each region server that’s given within the HBase cluster.

95. What’s WAL in HBase?

Ans:

Write-Ahead Log (WAL) could be a file storage and it records all changes to knowledge in HBase. it’s used for convalescent knowledge sets.The WALL ensures all the changes to the information may be replayed once a RegionServer crashes or becomes unobtainable.

96. What square measures the variations between the computer database and HBase?

Ans:

Relational info	HBase
It could be a row-oriented datastore.	it’s a column-oriented datastore.
It’s a schema-based database.	Its schema is a lot of versatile and fewer restrictive.
Suitable for structured knowledge.	appropriate for each structured and unstructured data.
Supports denotative integrity.	Doesn’t supports denotative integrity.
It includes skinny tables.	It includes sparsely inhabited tables.
Accesses records from tables victimization SQL queries.	Accesses knowledge from HBase tables victimization Apis and MapReduce.

97. What’s Apache Spark?

Ans:

Apache Spark is Associate in Nursing ASCII text file framework used for period of time knowledge analytics in an exceedingly distributed computing atmosphere. It’s a knowledge process engine that has quicker analytics than Hadoop MapReduce.

98. Will we have a tendency to build “Spark” with any specific Hadoop version?

Ans:

Yes, we will build “Spark” for any specific Hadoop version.

99. What’s RDD?

Ans:

RDD(Resilient Distributed Datasets) could be an elementary organization of Spark. it’s a distributed assortment of objects, and every dataset in RDD is more distributed into logical partitions and computed on many nodes of the cluster.

100. What’s Apache ZooKeeper?

Ans:

Apache ZooKeeper could be a centralized service used for managing varied operations in an exceedingly distributed atmosphere. It maintains configuration knowledge, performs synchronization, naming, and grouping.

101. What’s Apache Oozie?

Ans:

Apache Oozie could be a computer hardware that controls the advancement of Hadoop jobs. There square measure 2 styles of Oozie jobs:

Oozie advancement – it’s a group of actions sequenced in an exceedingly management dependency DAG(Direct Acyclic Graph) for execution.

Oozie arranger – If you would like to trigger workflows supported the provision of knowledge or time then you’ll use Oozie arranger Engine.