Overview
Hadoop is an Apache project (i.e. an open source software) to store & process Big Data. Hadoop stores Big Data in a distributed & fault tolerant manner over commodity hardware. Afterwards, Hadoop tools are used to perform parallel data processing over HDFS (Hadoop Distributed File System).
As organisations have realized the benefits of Big Data Analytics, so there is a huge demand for Big Data & Hadoop professionals. Companies are looking for Big data & Hadoop experts with the knowledge of Hadoop Ecosystem and best practices about HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop & Flume.
Exam & Certification
- Once you complete this master’s program, you will receive the course completion certificate by ICIT
ICIT Course Completion Certificate will be awarded upon the completion of the project work (after the expert review) and upon scoring at least 50% marks in the quiz. ICIT certification is well recognized in top MNCs .
Who should attend?
The market for Big Data analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Hiring managers are looking for certified Big Data Hadoop professionals. Our Big Data & Hadoop Certification Training helps you to grab this opportunity and accelerate your career. Our Big Data Hadoop Course can be pursued by professional as well as freshers. It is best suited for:
- Software Developers, Project Managers
- Software Architects
- ETL and Data Warehousing Professionals
- Data Engineers
- Data Analysts & Business Intelligence Professionals
- DBAs and DB professionals
- Senior IT Professionals
- Testing professionals
- Mainframe professionals
- Graduates looking to build a career in Big Data Field
For pursuing a career in Data Science, knowledge of Big Data, Apache Hadoop & Hadoop tools are necessary.
FAQ's
1. Explain “Big Data” and what are five V’s of Big Data?
“Big data” is the term for a collection of large and complex data sets, that makes it difficult to process using relational database management tools or traditional data processing applications. It is difficult to capture, curate, store, search, share, transfer, analyze, and visualize Big data. Big Data has emerged as an opportunity for companies. Now they can successfully derive value from their data and will have a distinct advantage over their competitors with enhanced business decisions making capabilities.
♣ Tip: It will be a good idea to talk about the 5Vs in such questions, whether it is asked specifically or not!
- Volume: The volume represents the amount of data which is growing at an exponential rate i.e. in Petabytes and Exabytes.
- Velocity: Velocity refers to the rate at which data is growing, which is very fast. Today, yesterday’s data are considered as old data. Nowadays, social media is a major contributor to the velocity of growing data.
- Variety: Variety refers to the heterogeneity of data types. In another word, the data which are gathered has a variety of formats like videos, audios, csv, etc. So, these various formats represent the variety of data.
- Veracity: Veracity refers to the data in doubt or uncertainty of data available due to data inconsistency and incompleteness. Data available can sometimes get messy and may be difficult to trust. With many forms of big data, quality and accuracy are difficult to control. The volume is often the reason behind for the lack of quality and accuracy in the data.
- Value: It is all well and good to have access to big data but unless we can turn it into a value it is useless. By turning it into value I mean, Is it adding to the benefits of the organizations? Is the organization working on Big Data achieving high ROI (Return On Investment)? Unless, it adds to their profits by working on Big Data, it is useless.
2. What is Hadoop and its components.
When “Big Data” emerged as a problem, Apache Hadoop evolved as a solution to it. Apache Hadoop is a framework which provides us various services or tools to store and process Big Data. It helps in analyzing Big Data and making business decisions out of it, which can’t be done efficiently and effectively using traditional systems.
♣ Tip: Now, while explaining Hadoop, you should also explain the main components of Hadoop, i.e.:
- Storage unit– HDFS (NameNode, DataNode)
- Processing framework– YARN (ResourceManager, NodeManager)
3. What are HDFS and YARN?
HDFS (Hadoop Distributed File System) is the storage unit of Hadoop. It is responsible for storing different kinds of data as blocks in a distributed environment. It follows master and slave topology.
♣ Tip: It is recommended to explain the HDFS components too i.e.
- NameNode: NameNode is the master node in the distributed environment and it maintains the metadata information for the blocks of data stored in HDFS like block location, replication factors etc.
- DataNode: DataNodes are the slave nodes, which are responsible for storing data in the HDFS. NameNode manages all the DataNodes.
YARN (Yet Another Resource Negotiator) is the processing framework in Hadoop, which manages resources and provides an execution environment to the processes.
♣ Tip: Similarly, as we did in HDFS, we should also explain the two components of YARN:
- ResourceManager: It receives the processing requests, and then passes the parts of requests to corresponding NodeManagers accordingly, where the actual processing takes place. It allocates resources to applications based on the needs.
- NodeManager: NodeManager is installed on every DataNode and it is responsible for the execution of the task on every single DataNode.
4. Tell me about the various Hadoop daemons and their roles in a Hadoop cluster.
Generally approach this question by first explaining the HDFS daemons i.e. NameNode, DataNode and Secondary NameNode, and then moving on to the YARN daemons i.e. ResorceManager and NodeManager, and lastly explaining the JobHistoryServer.
- NameNode: It is the master node which is responsible for storing the metadata of all the files and directories. It has information about blocks, that make a file, and where those blocks are located in the cluster.
- Datanode: It is the slave node that contains the actual data.
- Secondary NameNode: It periodically merges the changes (edit log) with the FsImage (Filesystem Image), present in the NameNode. It stores the modified FsImage into persistent storage, which can be used in case of failure of NameNode.
- ResourceManager: It is the central authority that manages resources and schedule applications running on top of YARN.
- NodeManager: It runs on slave machines, and is responsible for launching the application’s containers (where applications execute their part), monitoring their resource usage (CPU, memory, disk, network) and reporting these to the ResourceManager.
- JobHistoryServer: It maintains information about MapReduce jobs after the Application Master terminates.