With the increasing popularity of Big Data, there is consequently a surge in Hadoop job opportunities more than before. Therefore, to help you get that Hadoop expert role, you can use these interview questions and answers we have put together for you in this article to help you get through your interview. Perhaps knowing the facts like salary range that make Hadoop and Big Data roles lucrative will motivate you to pass that interview, right? đ€
According to indeed.com, a US-based Big Data Hadoop developer earns an average salary of $144,000. According to itjobswatch.co.uk, a Big Data Hadoop developerâs average salary is ÂŁ66,750. In India, the indeed.com source states that they would earn an average salary of âč 16,00,000.
Lucrative, donât you think? Now, letâs jump in to learn about Hadoop.
What is Hadoop?
Hadoop is a popular framework written in Java that uses programming models to process, store and analyze large sets of data. By default, its design allows scaling up from single servers to multiple machines that offer local computation and storage. Additionally, its ability to detect and handle application layer failures resulting in highly available services makes Hadoop quite reliable. Letâs jump right into the commonly asked Hadoop interview questions and their correct answers.
Hadoopâs Interview Questions and Answers
What is the Storage Unit in Hadoop?
Answer: Hadoopâs storage unit is called the Hadoop Distributed File System(HDFS).
How is Network Attached Storage Different from Hadoop Distributed File System?
Answer: HDFS, which is Hadoopâs primary storage, is a distributed filesystem that stores massive files using commodity hardware. On the other hand, NAS is a file-level computer data storage server that provides heterogeneous client groups with access to the data. While data storage in NAS is on dedicated hardware, HDFS distributes the data blocks across all machines within the Hadoop cluster. NAS uses high-end storage devices, which is rather costly, while the commodity hardware used in HDFS is cost-effective. NAS separately stores data from computations hence making it unsuitable for MapReduce. On the contrary, HDFSâ design allows it to work with the MapReduce framework. Computations move to the data in the MapReduce framework instead of data to computations.
Explain MapReduce in Hadoop and Shuffling
Answer: MapReduce refers to two distinct tasks Hadoop programs perform to enable large scalability across hundreds to thousands of servers within a Hadoop cluster. Shuffling, on the other hand, transfers map output from Mappers to the necessary Reducer in MapReduce.
Give a Glimpse into Apache Pig Architecture
Answer: Apache Pig architecture has a Pig Latin interpreter that processes and analyses large datasets using Pig Latin scripts. Apache pig also consists of sets of datasets on which data operations like join, load, filter, sort, and group are performed. The Pig Latin language uses execution mechanisms like Grant shells, UDFs, and embedded for writing Pig scripts that perform required tasks. Pig makes programmersâ work easier by converting these written scripts into Map-Reduce jobs series. Apache Pig architecture components include:
Parser â It handles the Pig Scripts by checking the scriptâs syntax and performing type checking. The parserâs output represents Pig Latinâs statements and logical operators and is called DAG (directed acyclic graph). Optimizer â The optimizer implements logical optimizations like projection and pushdown on the DAG. Compiler â Compiles the optimized logical plan from the optimizer into a series of MapReduce jobs. Execution Engine â This is where the final execution of the MapReduce jobs into the desired output occurs. Execution Mode â The execution modes in Apache pig mainly include local and Map Reduce.
List the Difference Between Local Metastore and Remote Metastore
Answer: The Metastore service in Local Metastore runs in the same JVM as Hive but connects to a database running in a separate process on the same or a remote machine. On the other hand, Metastore in the Remote Metastore runs in its JVM separate from Hive service JVM.
What are the Five Vâs of Big Data?
Answer: These five Vâs stand for Big Dataâs main characteristics. They include:
Value: Big data seeks to provide significant benefits from high Return on Investment (ROI) to an organization that uses big data in its data operations. Big data brings this value from its insight discovery and pattern recognition, resulting in stronger customer relations and more effective operations, among other benefits. Variety: This represents the heterogeneity of the type of data types gathered. The various formats include CSV, videos, audio, etc. Volume: This defines the significant amount and size of data managed and analyzed by an organization. This data depicts exponential growth. Velocity: This is the exponential speed rate for data growth. Veracity: Veracity refers to how âuncertainâ or âinaccurateâ data available is due to data being incomplete or inconsistent.
Explain Different Data Types of Pig Latin.
Answer: The data types in Pig Latin include atomic data types and complex data types. The Atomic data types are the basic data types used in every other language. They include the following:
Int â This data type defines a signed 32-bit integer. Example: 13 Long â Long defines a 64-bit integer. Example: 10L Float â Defines a signed 32-bit floating point. Example: 2.5F Double â Defines a signed 64-bit floating point. Example: 23.4 Boolean â Defines a Boolean value. It includes: True/False Datetime â Defines a date-time value. Example: 1980-01-01T00:00.00.000+00:00
Complex data types include:
Map- Map refers to a key-value pair set. Example: [âcolorâ#âyellowâ, ânumberâ#3] Bag â It is a collection of a set of tuples, and it uses the â{}â symbol. Example: {(Henry, 32), (Kiti, 47)} Tuple â A tuple defines an ordered set of fields. Example : (Age, 33)
What are Apache Oozie and Apache ZooKeeper?
Answer: Apache Oozie is a Hadoop scheduler in charge of scheduling and binding Hadoop jobs together as a single logical work. Apache Zookeeper, on the other hand, coordinates with various services in a distributed environment. It saves the developers time by simply exposing simple services like synchronization, grouping, configuration maintenance, and naming. Apache Zookeeper also provides off-the-shelf support for queuing and leader election.
What is the Role of the Combiner, RecordReader, and Partitioner in a MapReduce Operation?
Answer: The combiner acts like a mini reducer. It receives and works on data from map tasks and then passes the dataâs output to the reducer phase. The RecordHeader communicates with the InputSplit and converts the data into key-value pairs for the mapper to read suitably. The Partitioner is responsible for deciding the number of reduced tasks required to summarize data and confirming how the combiner outputs are sent to the reducer. The Partitioner also controls key partitioning of the intermediate map outputs.
Mention Different Vendor-Specific Distributions of Hadoop.
Answer: The various vendors that extend Hadoop capabilities include:
Why is HDFS Fault-Tolerant?
Answer: HDFS replicates data on different DataNodes, making it fault-tolerant. Storing the data in different nodes allows retrieval from other nodes when one mode crashes.
Differentiate Between a Federation and High Availability.
Answer: HDFS Federation offers fault tolerance that allows continuous data flow in one node when another crashes. On the other hand, High availability will require two separate machines configuring the active NameNode and the secondary NameNode on the first and second machines separately. Federation can have an unlimited number of unrelated NameNodes, while in High availability, only two related NameNodes, active and standby, which work continuously, are available. NameNodes in the federation share a metadata pool, with each NameNode having its dedicated pool. In High Availability, however, the active NameNodes run each one at a time while the standby NameNodes stay idle and only update their metadata occasionally.
How to Find the Status of Blocks and FileSystem Health?
Answer: You use the hdfs fsck / command at both the root user level and an individual directory to check the HDFS filesystemâs health status. HDFS fsck command in use: The commandâs description:
-files: Print the files you are checking. âlocations: Prints all blocksâ locations while checking.
Command to check the status of the blocks:
When Do You Use the rmadmin-refreshNodes and dfsadmin-refreshNodes Commands?
Answer: These two commands are helpful in refreshing node information either during commissioning or when node commissioning is complete. The dfsadmin-refreshNodes command runs the HDFS client and refreshes the NameNodeâs node configuration. The rmadmin-refreshNodes command, on the other, executes the ResourceManagerâs administrative tasks.
What is a Checkpoint?
Answer: Checkpoint is an operation that merges the file systemâs last changes with the most recent FSImage so that the edit log files remain small enough to quicken the process of starting a NameNode. Checkpoint occurs in the Secondary NameNode.
Why Do We Use HDFS for Applications Having Large Data Sets?
Answer: HDFS provides a DataNode and NameNode architecture which implements a distributed file system. These two architectures provide high-performance access to data over highly scalable clusters of Hadoop. Its NameNode stores the file systemâs metadata in RAM, which results in the amount of memory limiting the number of HDFS file system files.
What Does the âjpsâ Command Do?
Answer: The Java Virtual Machine Process Status (JPS) command checks whether specific Hadoop daemons, including NodeManager, DataNode, NameNode, and ResourceManager, are running or not. This command is required to run from the root to check the operating nodes in the Host.
What is âSpeculative Executionâ in Hadoop?
Answer: This is a process where the master node in Hadoop, instead of fixing detected slow tasks, launches a different instance of the same task as a backup task(speculative task) on another node. Speculative execution saves a lot of time, especially within an intensive workload environment.
Name the Three Modes in Which Hadoop Can Run.
Answer: The three primary nodes that Hadoop runs on include:
Standalone Node is the default mode that runs the Hadoop services using the local FileSystem and a single Java process. Pseudo-distributed Node executes all Hadoop services using a single ode Hadoop deployment. Fully-distributed Node runs Hadoop master and slave services using separate nodes.
What is a UDF?
Answer: UDF(User Defined Functions) lets you code your custom functions which you can use to process column values during an Impala query.
What is DistCp?
Answer: DistCp or Distributed Copy, in short, is a useful tool for large inter or intra-cluster copying of data. Using MapReduce, DistCp effectively implements the distributed copy of a large amount of data, among other tasks like error handling, recovery, and reporting.
Explain a Metastore in Hive.
Answer: Hive metastore is a service that stores Apache Hive metadata for the Hive tables in a relational database like MySQL. It provides the metastore service API that allows cent access to the metadata.
Define RDD.
Answer: RDD, which stands for Resilient Distributed Datasets, is Sparkâs data structure and an immutable distributed collection of your data elements that computes on the different cluster nodes.
How can Native Libraries be Included in YARN Jobs?
Answer: You can implement this by either using -Djava.library. path option on the command or by setting LD+LIBRARY_PATH in .bashrc file using the following format:
Explain âWALâ in HBase.
Answer: The Write Ahead Log(WAL) is a recovery protocol that records MemStore data changes in HBase to the file-based storage. WAL recovers this data if the RegionalServer crashes or before flushing the MemStore.
Is YARN a Replacement for Hadoop MapReduce?
Answer: No, YARN is not a Hadoop MapReduce replacement. Instead, a powerful technology called Hadoop 2.0 or MapReduce 2 supports MapReduce.
What is the Difference Between ORDER BY and SORT BY in HIVE?
Answer: While both commands fetch data in a sorted manner in Hive, results from using SORT BY may only be partially ordered. Additionally, SORT BY requires a reducer to order the rows. These reducers required for the final output may also be multiple. In this case, the final output may be partially ordered. On the other hand, ORDER BY only requires one reducer for a total order in output. You can also use the LIMIT keyword that reduces the total sort time.
What is the Difference Between Spark and Hadoop?
Answer: While both Hadoop and Spark are distributed processing frameworks, their key difference is their processing. Where Hadoop is efficient for batch processing, Spark is efficient for real-time data processing. Additionally, Hadoop mainly reads and writes files to HDFS, while Spark uses the Resilient Distributed Dataset concept to process data in RAM. Based on their latency, Hadoop is a high-latency computing framework without an interactive mode to process data, while Spark is a low-latency computing framework that processes data interactively.
Compare Sqoop and Flume.
Answer: Sqoop and Flume are Hadoop tools that gather data collected from various sources and load the data into HDFS.
Sqoop(SQL-to-Hadoop) extracts structured data from databases, including Teradata, MySQL, Oracle, etc., while Flume is useful for extracting unstructured data from database sources and loading them into HDFS. In terms of driven events, Flume is event-driven, while Sqoop is not driven by events. Sqoop uses a connector based-architecture where connectors know how to connect to a different data source. Flume uses an agent-based architecture, with the code written it being the agent in charge of fetching the data. Because of Flumeâs distributed nature, it can easily collect and aggregate data. Sqoop is useful for parallel data transfer, which results in the output being in multiple files.
Explain the BloomMapFile.
Answer: The BloomMapFile is a class extending the MapFile class and uses dynamic bloom filters that provide a quick membership test for keys.
List the Difference Between HiveQL and PigLatin.
Answer: While HiveQL is a declarative language similar to SQL, PigLatin is a high-level procedural Data flow language.
What is Data Cleansing?
Answer: Data cleansing is a crucial process of getting rid of or fixing identified data errors which include incorrect, incomplete, corrupt, duplicate, and wrongly formatted data within a dataset. This process aims to improve the quality of data and provide more accurate, consistent, and reliable information necessary for efficient decision-making within an organization.
Conclusionđ
With the current Big dataâs and Hadoop job opportunities surge, you may want to better your chances of getting in. This articleâs Hadoop interview questions and answers will help you ace that upcoming interview. Next, you can check out good resources to learn Big Data and Hadoop. Best of luck! đ
