30+ hours of interactive learning
Course delivered by certified instructors with 12+ years of experience
Real-world projects and case studies
Access to Kovid Cluster for 3 years
Course completion certificate
Free career counseling by industry experts
100% money-back guarantee*
Hadoop is the leading framework for Big Data -Forbes
The worldwide market for Hadoop is projected to grow double, and is estimated to reach $203 billion by 2020 -IDC
Big Data professionals earn 30 to 40 percent more salaries than other IT professionals -Randstad
What is Big Data?
Big Data is a massive term used for the complex or large data sets that are critical to be sorted using the traditional data processing systems or by the on-hand data management tools.
What is Hadoop?
Hadoop is an open source, Java-based programming framework that supports the processing of large data sets in a distributed computing environment.
Hadoop Big Data Market Forecast
IDC reports that the worldwide market for Hadoop Big Data is projected to grow double in the coming years, and will reach to $203 billion by 2020.
Why Hadoop Big Data Certification Program?
- 2.5 quintillion of data is generated every day, and 90 percent of the today’s world data is created in the last two years alone –IBM Marketing report.
- As the data continues to grow sharply, it is estimated that the world is going to face a shortage of 1.7 million certified Hadoop Big Data professionals in the coming 3 years –McKinsey Global Institute.
- Industries like Automobile, Banking, Communications, Finance, IT, Healthcare, Manufacturing, Retail Trading etc. are in the hunt of certified Big Data professionals –Economic Times.
- Big Data professionals earn 30 to 40 percent more salaries than the other IT professionals –Randstad.
Big Data Hadoop Developer training program from Kovid Academy is efficiently framed to assist the participants to understand all the core concepts of the Hadoop Big Data, by offering a comprehensive introduction to Hadoops and its ecosystem components.
By the end of this course, you will be able to:
- Explain what Big Data is and its importance in the present market.
- State how Big Data can be treated as an exceptional replacement of the traditional systems.
- Define the core components of Big Data.
- Explain the various distributed computing program models and their usage.
- State how NoSQL database can perform exceptionally well when compared to others.
- Develop, debug, optimize, and deploy the various Big Data programs like Map Reduce, Spark, Pig, and Hive.
- Define the concepts related to the Big Data Administration.
- Explain what Cloud is and the procedure for deploying the Cloudera cluster in the Amazon Cloud.
Who can attend the course?
This course is designed for:
- Graduates or Post-graduates who want to get into the role of Big Data Developer or Analytics
- Data Analysts
- Solution Architects
- Technical Consultants
- Any IT Professionals
Knowledge of Core Java, Linux, SQL, and Eclipse will be beneficial (but not mandatory).
Course CurriculumDownload Brochure
Module 1: Introduction
- What is Big Data?
- Use Cases
- Distributed Computing and Storage
Module 2: More of Big Data
- Career Paths
- Linux Introduction
Module 3: HDFS
- Use Case
Module 4: Big Data Ecosystem
Module 5: Hadoop 1x and 2x
Module 6: MapReduce Programs
Module 7: Spark
Module 8: Hive
- Use Case
Module 9: Pig
- Use Case
Module 10: NoSQL
- Getting started
- Different types of NoSQL
- RDBMS vs NoSQL
- Introduction to Columnar Databases
Module 11: HBase
- Use Case
- Advanced features
Module 12: Projects
- Real Time
Questions & Answers
The market for ‘Big Data’ is experiencing a tremendous growth, thereby creating a huge demand for skilled and trained Big Data professionals across the globe. Though the demand is massive, the supply certainly seems to be falling short of the demand. The core reason might be the lack of proper homework before attending the interviews.
To make things smoother for you during the interview preparation process, we have listed top 50 commonly asked questions along with the best suited answers, which can help you to successfully crack the Big Data Hadoop interview.
Note: All the questions and answers are prepared by the subject experts who are associated with Kovid Academy.
1. What is Big Data?
The term ‘Big data’ is used to represent a collection of large and complex data sets, which are difficult to capture, store, process, share, analyze, and visualize using the traditional RDBMS tools.
2. Explain the five V’s of Big Data?
Big Data is often described using the five V’s, which are:
- Volume – the amounts of data generated every day, i.e. in Petabytes and Exabytes.
- Velocity – the speed at which the data is generated every second. After the advent of social media, it probably takes seconds for any news to get viral across the Internet.
- Variety – the different types of data generated every day that comes in a variety of formats like texts, audios, videos, csv, etc.
- Veracity – the uncertainties or the messiness of the data. With different forms of big data, it gets difficult to control the accuracy and quality. The volume often makes up the core reason behind the lack of accuracy and quality of the data.
- Value – having access to big data is always a good thing, but failing to extract the real value from it is completely useless. Extracting value means, drawing benefits to the organizations; achieving the return on investment (ROI); and making profits for the businesses working on big data.
3. On what concept the Hadoop framework works?
The Hadoop Framework works on:
- Hadoop Distributed File System: HDFS is a Java-based storage unit in Hadoop, which offers reliable and scalable storage of large datasets. It is responsible for storing different types of data in the form of blocks.
- Hadoop MapReduce: MapReduce is a Java-based programming paradigm that offers scalability across different Hadoop clusters. It is responsible for distributing the workload into different tasks to run in parallel. The job of ‘Map’ is to split the datasets into tuples or key-value pairs, and the ‘Reduce’ then takes the output from Map and combines it with data tuples into a smaller set of tuples.
- Hadoop YARN: Yet Another Resource Negotiator is the architectural framework in Hadoop that allows multiple data processing engines to handle stored data in a single platform, disclosing a new completely method to analytics.
Note: Reduce jobs are performed only after the execution of Map jobs.
4. What is Hadoop and mention the key components of Hadoop?
Apache Hadoop is the best solution for ‘Big Data’ problem. Hadoop is an open source Apache framework written in Java, which offers different tools and services to process and analyze big data, and helps to draw effective business decisions.
The main components of Hadoop are:
- YARN – processing framework (NodeManager, ResourceManager)
- HDFS – Storage unit (DataNode, Namenode)
5. List the differences between Hadoop 1.x and Hadoop 2.x.
In Hadoop 1.x, NameNode is the single point of failure (SPOF).
In Hadoop 2.x, there are two NameNodes i.e. Active NameNode and Passive NameNode. If in any case, the Active NameNode gets failed, then the Passive NameNode will take the charge. In Hadoop 2.x, YARN offers a central resource manager that allows running multiple applications on Hadoop.
6. What is YARN?
Yet Another Resource Negotiator (YARN) is a processing framework in Hadoop, used for resource management and as an execution environment for the processes. YARN consists of two key components, namely:
- NodeManager – installed on every DataNode, the NodeManager is responsible for task execution on each Datanode.
- ResourceManager – responsible for the receipt of process requests, and then transfers it to the corresponding NodeManagers. The ResourceManager is also responsible for resource allocation based on the requirements.
7. What is HDFS?
Hadoop Distributed File System (HDFS) is a Java-based storage unit in Hadoop, which follows Master-Slave topology. HDFS offers reliable and scalable storage of large datasets, and is also responsible for storing different types of data in the form of blocks. HDFS consists of two key components, namely:
- DataNode – is the slave node, responsible for storing the data in HDFS
- NameNode – is the master node, responsible for maintaining the metadata information and managing the DataNodes.
8. What happens if a DataNode fails?
If a DataNode gets failed, then
- The NameNode and JobTracker will detect the failure
- All the tasks will be re-scheduled on the failed node
- User data will be replicated to another node by NameNode
9. Explain how NameNode tackles with the DataNode failures?
The NameNode receives a signal from the DataNode at regular time intervals, informing that the DataNode is functioning properly. If the NameNode does not receive any signal from the DataNode for a specific period, then that DataNode is marked as dead.
10. Define Active and Passive NameNodes?
- The NameNode that works and runs in the cluster is known as ‘Active NameNode’.
- The NameNode that is in a standby state, and have the same data as Active Namenode, is known as ‘Passive NameNode’.
On the failure of Active NameNode, the passive NameNode takes the responsibility. This is the reason that the cluster never fails.
Intrested to learn more about Hadoop Big Data, click here.
11. State the differences between RDBMS and Hadoop.
Following are the key differences between Hadoop and RDBMS.
12. State the common Input formats defined in Hadoop?
The basic input formats of Hadoop are:
- TextInputFormat – is the default input format in Hadoop.
- KeyValueInputFormat – used in plain text files, where the files are divided into lines.
- SequenceFileInputFormat – used to read the files in sequence.
13. Why it is frequent to add/remove nodes to/from a Hadoop cluster?
Because of the features like ‘utilization of commodity hardware’ and ‘ease of scale’, the DataNodes in Hadoop cluster get crashes more often, which makes it compulsory to frequently add (Commission) and remove (Decommission) nodes to/from the Hadoop cluster.
14. What will happen when two different clients attempt to access the same file stored in HDFS?
Whenever a client requests the NameNode for accessing a file, the NameNode initially checks for the lease of that file.
- If the lease is not granted to any client, then the NameNode grants the lease to the requested client for accessing the file.
- If the lease is already granted to any other client, then it will send a notice to the requested client saying that the lease of the file is already granted to some other client, and will reject the lease request.
Note: HDFS supports exclusive writes only.
15. Can DataNode and NameNode be the commodity hardware?
DataNodes stores data like the commodity hardware, and are required in large numbers. Whereas, the NameNodes consists of the metadata about the blocks stored in HDFS, which requires good memory space (RAM).
16. How HDFS is Fault-Tolerant?
Whenever data is stored in HDFS, the NameNode replicates the data to different DataNodes. By chance, if a DataNode goes down, then the NameNode using the replicas will copy the data to another DataNode, making the data available every time. This makes HDFS a fault-tolerant.
Note: The default RF (Replication Factor) is 3, which can be changed as per the need.
17. Define Blocks in HDFS and state how it can be configured?
When a file is put into the HDFS file system, it is split into blocks based on the block size and then replicated across different machines.
- Data in HDFS are stored as blocks, and distributed across Hadoop cluster.
- Files in HDFS are divided into lock-sized chunks, which are then stored as independent units.
The parameter dfs.block.size can be used in hdfs-site.xml file to configure the block size in the Hadoop environment.
18. Explain the difference between HDFS Block and Input Split.
The physical division of the data is known as ‘HDFS Block’, and the logical division of the data is known as ‘Input Split’. For storing the data, HDFS divides the data into blocks, and for processing the data, MapReduce divides the data into input splits and then assigns to the mapper function.
19. What do you mean by Rack Awareness and state the Block Placement Policy in Hadoop?
An algorithm used by the NameNode to decide how blocks and its replicas are placed, depending on the rack definitions for reducing the network traffic between the DataNodes in the same rack. For example, the default value of RD is 3, it means for every block two replicas will exist in one rack. If a third copy is created then it will be placed in another rack. This criterion is known as Block Placement Policy.
20. How to restart daemons in Hadoop?
- To stop any NameNode individually, ./sbin /hadoop-daemon.sh stop namenode command is used, and to start it again ./sbin/hadoop-daemon.sh start namenode command is used.
- To stop all the daemons at a time, ./sbin/stop-all.sh command is used, and to start all the daemons again ./sbin/start-all.sh command is used.
Note: Daemons in Hadoop resides in the sbin directory.
Learn about the secret recipe behind the Amazon’s Big Success.
21. What is the use of ‘jps’ command?
‘jps’ command is used to check whether Hadoop daemons are running or not. The command lists all the daemons that are running on the machine such as, DataNode, Namenode, NodeManager, ResourceManager etc.
22. State the modes in which Hadoop can run.
Hadoop can run in the following three modes:
- Local (Standalone) Mode: It is the default mode that uses the local filesystem. The mode contains Hadoop components like DataNode, NameNode, NodeManager, ResourceManager, etc.
- Pseudo-Distributed Mode: The mode in which a single-node Hadoop deployment is carried out. In this mode, all Hadoop services (including master and slave services) are executed on a single compute node.
- Fully-Distributed Mode: The mode in which the master and slave services are executed on different nodes.
23. What do mean by Speculative Execution in Hadoop?
Whenever the master node finds any node executing a task slowly, then it will create another instance of the same task on a different node. Hence, the node which finishes the task first will be accepted and the other will be killed.
Note: Speculative execution can be applied for both mapper and reducer tasks.
24. What is a Task Tracker?
Task tracker is a daemon that runs on DataNode for managing the execution of tasks on the slave node. Whenever a client submits a job, the job tracker initializes the job and splits the work and assigns to different task trackers to perform MapReduce tasks. During the task, the task tracker will parallelly communicate with the job tracker by sending signals. If in any case, the job tracker does not receive the signals from the task tracker in the specified time, then it will assume that the task tracker as dead and assigns the same task to another task tracker in the cluster.
25. What is a Job Tracker?
Job tracker is a daemon for tracking and submitting the MapReduce jobs in Hadoop. Job tracker assigns the tasks to different task trackers, and based on the signals received from the task taskers, the job tracker decides whether the assigned task is completed or not. If in any case, the job tracker goes down then all the running jobs will be halted.
Note: In a Hadoop cluster, there will be only one job tracker but many task trackers.
26. State the difference between NameNode, Backup Node, and Checkpoint NameNode.
- NameNode – is the core of the HDFS file system, which stores/manages the metadata. It has a directory tree structure of all the files stored in the HDFS file system on a Hadoop cluster. NameNode uses the following files for namespace-
- fsimage file: keeps a track of the latest checkpoint of the namespace
- edits file: a log of changes that have been made to namespace since the checkpoint
- Checkpoint Node – keeps a track of the latest checkpoint in a directory, which has the similar structure as that of NameNode’s directory. By downloading fsimage and edits files from the NameNode, the Checkpoint node creates checkpoints for namespace on regular time intervals, and then merges it locally. The newly created image after merging is updated back to NameNode.
- Backup Node – offers the similar functionality as Checkpoint node, but also maintains an updated in-memory copy of the file system namespace in synchronization with the NameNode.
27. What is MapReduce?
MapReduce is a Java-based programming paradigm that offers scalability across different Hadoop clusters. In Hadoop, MapReduce is used to process large datasets over a cluster of computers using parallel programming.
28. What is the syntax to run a MapReduce program?
To run a MapReduce program, the following syntax is used-
hadoop_jar_file.jar /input_path /output_path
29. State the main Configuration Parameters in a MapReduce program?
Following are the main configuration parameters in a MapReduce program.
- Input locations of the jobs in distributed file system
- Output location of the jobs in distributed file system
- Data – Input format
- Data – Output format
- Class having the Map function
- Class having the Reduce function
- JAR file having mapper, reducer, and driver classes
30. What is meant by Shuffling in MapReduce?
Shuffling is the process by which the system performs the sorting operation, and transfers the output from the map jobs to the reducer as inputs.
31. State the core methods of a Reducer.
Following are the three important methods of a Reducer.
- setup () – it is used to configure different parameters like input data size, distributed cache, heap size, etc.
public void setup (context)
- reduce () – it is the core of reducer that is called once per key with the related reduce task.
Public void reduce (key, value, context)
- cleanup () – it is the method called once for cleaning the temporary files
public void cleanup (context)
32. Explain how Reducers communicate with each other?
This will be the tricky question asked by the interviewer to test your subject knowledge. Because, the MapReduce programming paradigm does not allow the reducers to communicate with each other.
Note: Reducers run in isolation.
33. Explain about Distributed Cache in a ‘MapReduce Framework’.
Distributed cache is a MapReduce facility used to cache files required by the applications. It means, once a file has been cached for a job, then Hadoop will make it accessible to every data node where the Map/Reduce jobs are executing. This makes us to access the cache file as a local file in the Mapper or Reducer job.
34. What is the role of a MapReduce Partitioner in Hadoop?
The role of a MapReduce Partitioner is to ensure that all the values of a single key pass to the same reducer, hence allowing uniform distribution of the map output across the reducers. The MapReduce Partitioner forwards the Mapper output to the reducer, by considering which reducer is accountable for the specific key.
35. What is the use of a Combiner?
The Combiner is used to enhance the productivity of the MapReduce jobs by reducing the amount of data needed to be transferred to the reducers. In short, a combiner is considered as a ‘mini reducer’ that performs the local ‘reduce’ task.
36. What is the purpose of RecordReader in Hadoop?
RecordReader in Hadoop is used to load the data from its source and converts it into (key, value) pairs befitting for reading by the Mapper.
37. Define how Hadoop is different from other data processing tools?
When compared with other data processing tools, Hadoop allows us to modify (add/remove) the count of Mappers without worrying about the volume of data that is to be processed.
38. Name a few advantages of Apache Pig over MapReduce.
Apache Pig is a platform originally developed by Yahoo, used to analyze large data sets. This platform is designed to deliver an abstraction over MapReduce, thereby shortening the complications of writing a lengthy MapReduce program. Following are the key advantages of Apache Pig over MapReduce.
39. Mention the different Data Types available in Pig Latin.
The Pig Latin platform is designed to handle atomic data types and complex data types.
- Atomic data types – also called as ‘Scala data types’ are the basic data types that are used in almost every programming language.
Data types like int, float, string, char, byte, long, and double.
- Complex data types – the exclusive data types used for complex operations in Pig.
Data types like Map, Tuple, and Bag.
40. Define UDF.
UDF stands for User Defined Functions.
Built-in functions in every language are limited. If you want to use a new function, then it is required to create a user defined function programmatically by using the languages like Java, Python, Ruby, etc. and then embed it in a script file.
41. Mention the different Relational Operators available in Pig Latin.
Following are the different relational operators available in Pig Latin.
- order by
- for each
42. Why do we need Apache Hive?
Apache Hive is a tool in Hadoop ecosystem that offers an interface to organize, manage, and query data in a database like fashion. It allows to write SQL like queries, and is highly suitable for accessing and analyzing the data in Hadoop with the help of SQL syntax.
43. Name the different types of tables available in Hive.
In Hive, two types of tables are available, namely:
- Managed Table – also called as ‘internal table’ is the default table available in Apache Hive. The storage location of the table can be modified while creating the table.
The default location of the table is /usr/hive/warehouse.
- External Table – is used for external use as when the data is used outside of the Hive.
- In Managed table, both the data and schema are under the control of the Hive.
- In External table, only the schema is under the control of the Hive.
44. What is Apache HBase?
Apache HBase is an open source, distributed, scalable, multidimensional NoSQL database is written in Java language. It runs on the top of HDFS, and offers Google’s BigTable like capabilities to Hadoop. Apache HBase is designed to deliver a fault-tolerant way of storing large data sets.
45. Mention the core components of Apache HBase.
Following are the three key components of Apache HBase.
- Region Server – A table is divided into various regions, and a collection of regions is served to the clients by the Region Server.
- HMaster – is used tor coordinating and managing the Region Server (like the way NameNode manages the DataNode in HDFS).
- ZooKeeper – acts like a coordinator in the HBase distributed environment, which helps in maintaining server state within the cluster by interacting through sessions.
46. State the importance of WAL in HBase.
WAL stands for Write Ahead Log. It is a file attached to every Region Server, which stores the new data that hasn’t committed to the permanent storage. WAL is highly useful in the situations of Region Server failure for recovering the data sets.
47. Name a few differences between HBase and Relational databases.
48. What are the different types of NoSQL databases?
There are more than 150 NoSQL databases, but they are categorized into KV, Columnar, Document and the Graph databases. Depending on the requirement the appropriate database can be used.
49. What is Apache ZooKeeper?
Apache ZooKeeper is a distributed co-ordination service originally developed by Yahoo, used for managing a large set of hosts. With its simple API architecture, it becomes easier to coordinate and manage a service in the distributed environment.
50. What is Apache Oozie?
Apache Oozie is a workflow scheduler system used to run and manage Hadoop jobs. It allows combining various complex jobs to be run in a sequential order towards the attainment of a bigger task. The kinds of jobs performed by Oozie are:
- Oozie Workflow Jobs – represented as DAGs (Directed Acyclic Graphs) to specify the sequence of actions that are to be executed.
- Oozie Cooridnator Jobs – are recurrent Oozie Workflow jobs that are triggered by time and data availability.
Also read the tips required to crack the Hadoop Developer interview.
As you now have all the questions and answers important from the Hadoop Big Data interview perspective, don’t get late to start preparing on these FAQs for successfully cracking the Hadoop job interviews.
Frequently Asked Questions
The Big Data Hadoop Developer training program from Kovid Academy will cover all the core components of the Hadoop eco-system, along with the detailed discussions on- how to use Hadoop (CLASSIC and YARN) and Apache softwares like Hive, Spark, Pig, HBase, Zookeeper, Flume, Oozie, Sqoop, Cassandra, and MongoDB.
The instructors/trainers at Kovid Academy are highly qualified and certified professionals, who holds around 12+ years of industry relevant experience.
This course is designed to match both the freshers and experienced professionals who are aspiring to make a career in Hadoop Big Data/Analytics field. To do so, basic knowledge of Core Java, Linux, SQL, and Eclipse are beneficial (but not mandatory).
The participants are recommended to have a-
- 64-bit operating system.
- i3 or higher processor with virtualization support.
- Minimum of 4 GB RAM (8 GB RAM is recommended).
- 100 GB of free hard disk space.
- Internet connectivity with minimum 1 MBPS speed.
Not to worry. All your sessions will be recorded, and uploaded in the LMS. You will be able to view it later as per your convenience.
Yes. Relevant course material and adequate references will be provided.
After successfully completing the training program, you are required to undertake a project work. Based on certain performance parameters and evaluation of the project work, you will be awarded with the Kovid certificate.
Yes, if you cancel your enrollment, then refund will be made to you after excluding the applicable cancellation fee. Please refer to our cancellation/refund policy for complete information.
In any unlikely event, if you are not satisfied with the course/training you may within the first session/day wish to withdraw from the course, then contact us on the same day and raise the refund request. Please refer to our cancellation/refund policy for complete information.
All our trainings are delivered through two interactive modes i.e. Live Online and Classroom. You can select anyone based on your comfort levels.
Yes, we offer group discounts for the training programs. For more information on this, please contact at firstname.lastname@example.org.