Big Data Hadoop Training in Chennai
Learn Hadoop Training in Chennai at FITA – No 1 Big Data Hadoop Training Institute in Chennai. Call 98404-11333 for more details to learn Big Data Training in Chennai.
What is Big Data and Hadoop?
Big data refers to the large and complex set of data that are difficult to process using traditional processing systems. Stock exchanges like NYSE and BSE generates Terabytes of data every day. Social media sites like Facebook generates data that are approximately 500 times bigger than stock exchanges.
Hadoop is an open source project by Apache used for storage and processing of large volume of unstructured data in a distributed environment. Hadoop can scale up from single server to thousands of servers. Hadoop framework is used by large giants like Amazon, IBM, New York Times, Google, Facebook, Yahoo and the list is growing every day. Due to the larger investments companies make for Big Data the need for Hadoop Developers and Data Scientists who can analyse the data increases day by day.
Who Should Join Hadoop Training Chennai?
Big Data industry has gained significant growth in recent years and recent surveys have estimated that the Big Data market is more than a $50 billion industry. Gartner survey has confirmed that 64% companies have invested in Big Data in 2013 and the number keeps increasing every year. With the challenges in handling and arriving at meaningful insights from Bigdata, opportunities are boundless for everyone who wants to get into Big data Hadoop ecosystem. Software Professionals working in outdated technologies, JAVA Professionals, Analytics Professionals, ETL Professionals, Data warehousing Professionals, Testing Professionals, Project Managers can undergo our Hadoop training in Chennai and make a career shift. Our Big Data Training in Chennai will give hands-on experience to you to meet the demands of industry needs.
Why Big data Training in Chennai at FITA
High Level Hadoop Training Syllabus
A survey from FastCompany reveals that for every 100 open Big Data jobs, there are only two qualified candidates. Are you ready for the Shift?
By the end of Hadoop Training in Chennai at FITA you will Learn
Scope of Hadoop in Future
Big Data Analytics job has become a trending one currently and it is believed to have a great scope in future as well. There is a survey which states Big Data Management and Analytics job opportunities has been increased in 2017 when compared to the past 2 years. This leads many IT professionals to switch their career to Hadoop by taking up Hadoop Training in Chennai. Many organizations prefer Big Data Analytics as it is necessary to store their large amount of data and retrieve the information when it is wanted. After this, many other organizations that have not used Big Data have also started using it in their organization which makes the demand for Big Data Analytics in town. One of the main advantages of Hadoop is the salary aspects, when you become Big Data Analyst with a proper training you may have a very good package over a year of experience, this is the main reason for people preferring Big Data Training in Chennai. Adding to it, there are lots of job opportunities available in India as well as abroad which gives you the hope of onsite jobs too. Putting upon all these factors in a count, Big Data Hadoop is trusted to have the stable platform in future. If you are in a dilemma in taking up Hadoop Training Chennai then it is the right time to make your move.
Advantages of Big Data Hadoop
– structured –unstructured (images, videos)
FITA Academy is located in Prime location in Chennai at Velachery and T Nagar. We offer both weekend and weekdays courses to facilitate job seekers, fresh graduates and working professionals. Interested in our Hadoop Training in Chennai, call 98404-11333 or walk-in to our office to have a discussion with our student counsellor43 to know about Hadoop course syllabus, duration and fee structure.
It’s the right time to upgrade your knowledge with Hadoop Training in Chennai, don’t get left behind the bend. The Hadoop expert’s professional program delivers the most precise and standard big data credential.
Looking for Hadoop Training in Chennai? Join FITA and Get Trained from the Big Data Leaders! Hadoop Training Chennai at FITA is rated as the best by Professionals!
Hi FITA, I had a very good experience in learning from your institute. The overall training went very smooth and good. The trainer explained all the concepts very clearly and especially he cearly explains any no. of doubts asked. Enough materials were provided for each concept. The assignments were given for each topic and those assignments are good enough to explore in depth of each concept. On the whole had very good interactive learning sessions with much practical hands on works.
Let me start from the day of admission. I didn’t know the content of the hadoop system but this training has helped me to get into all the topics with hands on. Initially the program started with interaction and introduction. The training materials and guidance helped to work without any one. Finally the training was very good and I was able to understand the concepts. The training arrangements were good in the academy!!!
FITA is one of the best training institutes in Chennai for Hadoop Training. Trainers are very friendly in nature and classes are handled by them is also good. My trainers provided many examples and live scenarios for better understanding of the concepts. They also provided a small project to practice with. Thank you FITA for offering best training on Hadoop Technologies. I recommend FITA to take Hadoop Training in Chennai.
I’ve Joined for HADOOP classes in FITA Academy Velachery. All topics are covered step by step fully practical based. So we can learn easily. Especially trainers are good friendly manner any doubts we can ask at the time he will practically explain all our queries. Definitely everyone get interest on First day itself. Thanks FITA for my Career growth.
Took training in BigData/Hadoop. experienced teacher with good knowledge. The subject taught was relevant to current industry requirements.
Hadoop session was Extraordinary. The training covered a lot of topics in depth. We also have good hands on experience with real-time scenarios. The trainer was very experienced and was able to answer our queries and doubts. The training would be a good launchpad for all aspiring candidates.
I have completed Hadoop Admin Course in FITA Academy. I had a good experience with practical sessions. The trainer has a good knowledge and taught me clearly with practical sessions and they coordinated well for the classes. I suggest my friends to learn at FITA
I have taken a course on hadoop. I really had a very good experience. Classes was fully practical based. Trainer ranjith was so friendly and supportive. fita provides very good learning atmosphere. It made me strong in the field of hadoop. If you want to learn hadoop try fita.
Hadoop Industry Updates
What is new in Hadoop?
The industry standard hardware from Hadoop helps to store the data for the analysis of the data applied to the structured and unstructured data. To move the data the bulk load processing and streaming techniques are used. Apache squoop is used to move the data through bulk load process. Apache flume and Apache kafka is used to move the data through streaming. The data process options are fast and grouped as batch. The fast in memory is called as the Apache spark and the data processing as batch is called as Apache hive or Apache pig. Join the Hadoop Training in Chennai to know about the industrial updates and industrial demand for the hadoop technology. Cloudera and Apache impala have turneddata analysis to BI quality. It has compatibility with all leading BI tools and the high performance of the SQL help for the analysis of the patterns in the data.
Innovation from Santander
The latest innovation of Santander UK’s next generation is the data warehousing and steaming analytics to improve the customer experience. Apache kudu is used for the fast analytics. This is used for the operations like offloading workload from existing legacy systems, ask questions regarding the customer behavior and ask questions regarding the current status of the bank. With the help of Apache Kafka the data streams can be easily moved to online. Apache kudu vault is conforming the data events from the Hub, satellite and link structure of the Data Vault 2.0 methodology. The elastic event delivery platform is based on the scalaAkka and Apache Kafka for the data transformation. The fast data, timely decisions, reusable patterns and high speed are essential factors for the reusable platform and architecture. The big community followers and high level products show the demand for the Big Data Training in Chennai. For the sake of financial security and enhance the customer satisfaction the Santander UK innovated the real time insight. The cluster used by the legacy systems requires the raw event streams that are canonical. This canonical event stream is redistributed to the other systems. The other systems like HDFS file system, Apache HBase or Apache kudu. This innovation was awarded as the data impact award finalist.
Hadoop 3 demand for the Java 8 and to work withhadoop3 java 7 is not helpful for the developers. The erasure encoding in HDFS will provide the fault tolerance and reduce the storage overhead. The smaller units in the sequential data are divided as bit, byte and block. Join the Big Data Course in Chennai and head the big team of data analysts in a reputed company with the help of the practical knowledge and the constant interest towards learning. These smaller units are saved in different disks in the hadoop. The compared with the HDFS replication the overhead cost of the Erasure coding is comparatively less. The factors like the storage, network and CPU decides the overheads of the erasure coding.Yarn 2 supports the flows or logical applications are supported by the notion of flows explicitly. The time line collector in the YARN separates the data and sent it to the resource manager timeline collector. The shell script rewrite is designed with new features like all the variables in one location which is called as hadoop-env.sh, it is easy to start a daemon command, if pdsh is installed then ssh connections are used in the operations, without symlinkinghadoop is honoured now, the error messages are handled well by displaying it to the user.
The namenode extensions, client extensions, datanode extensions, and erasure coding policy forms the architecture of the HDFS erasure encoding. YARN timeline service v.2 is updated on the hadoop 3. The version 2 brings the scalable distributed writer architecture and a scalable backend storage. The queries from the YARN application are dedicated to the REST API. One collector is allocated to each YARN application and the APacheHBase is used as the primary backing storage. The Big Data Training is the best training to get placed in the big company and dream high with the top salary in the industry. The two major challenges are resolved with the updations in the YARN. The challenges are revolving around the scalability, reliability and usability. The scalability is reached with the seperation of the writes and the reads of data. The REST API help to resolve the problems from the queries and differentiate the queries. To process the large size data the HBase handles the response time very well.
The flows are explicit in the YARN version 2 and the storage system with the application master, node managers and resource managers are well planned. The data that belong to the application are collected in the application master, The resource manager collect the data with the time line collecter. Big Data Hadoop training with the expert trainers makes the subject still more interesting and provides in-depth knowledge in to the subject. To make the volume as reasonable the resource manager emits the YARN generic life cycle. The time line collector on the node which is running the application master with the node managers also collects and writes the data to the time line collector. The storage is backed up with the application master, node managers and the resource managers. The queries are handled by the REST API.
The new features in the shell script of the Hadoop also help to fix the bugs. The new hadoop-env.sh aid for the collection of the variables in one location. The daemon is edited and it is easy to start a daemon in hadoop3. Daemon is used for the operations such as daemon stop, stop a daemon, and daemon status. The error messages are handled by the log and pid dirs on daemon start up. The unprotected errors are generally displayed to the user and it elminates the user satisafaction of using the system. So, the new hadoop 3 help for the elimination of error messages and efficient bug fixing. Join the Hadoop Training in Chennai and see the difference in the number of interviews you get. The right knowledge by the right time is important to get the success in the job.
The client jars in hadoop 3
The two depencies such as hadoop-client-api and hadoop-client-runtime artifacts are the two dependencies in the hadoop 3.The jars help to resolve the version conflicts in the hadoop. The version conflicts aids to the leakage in the classpath which is protected with the jars. It becomes easy for the hbase to talk to the hadoop cluster and there is not need for the depencies for the communication. The best training institutes extend their support till certification and provide the required help for the Big Data Certification in Chennai.
YARN contaniners and guaranteed containers help for the completion of the data analysis with out any failure. The distributed scheduler allow for the opportunistic container and it is implemented through the AMRMProtocol interceptor. These containers are allocated with the two properties such as the allocation and enabling the container. After adding the opportunistic contaner the web UI page contains different set of informations regarding the containers. The informations in the Web UI page are the total number of opportunistic containers on each node, the memory usage for the containers, The CPU virtual cores of the containers, the queued list of the containers in each node of the Hadoop. There are two ways to allocate the opportunistic container and they are centralized allocation and a distributed allocation. Gurantee containers are the capacoty scheduler where as opportunistic containers are used for execution of the application. If the management of the opportunistic containers are slow then it gives changes in the nodes. This condition leads to imbalance in the nodes. Big Data Training and Placement in Chennai help for the students till they get placed. The interview questions and the mock interviews are helpful to prepare yourself for the highly competitive job interviews.
For the shuffle intensive jobs the task level native optimization is a big boon. The map reduce is updated with this new feature. The nativemapoutputcollector will handle the mapper with sort, spill and IFile serialization. The native code is used to merge the code and handle the jobs effectively. Hadoop three help for the effective system maintenance. Big Data Training is suitable for the candidates with less interest in the programming and more interest in the analysis.
When handling the big volumes of data the fault tolerance is essential. The critical deployments demand for the fault tolerance. If one name node is active and the other 2 name nodes are passive then accordingly the fault in the name nodes are tolerable by the the architecture of the name nodes. Thus the name node with the changes and the ephemeral range help for the tolerance. Auto tuning and simplification of configuration makes the administration of the hadoop as a easy task. Join the Big Data Course in Chennai to set the regime to search the job rigorously.
Hadoop and Cloudera
The functions of the Cloudera or Hadoop or the Vsphere is to take care of the qualities such as maintenance mode, rack awareness, high availability, replication of data and the protection of data. Cloudera is the famous open source platform for the distribution. Know about the Best Big Data Training after a through analysis of the reviews and take demo class also as a deciding factor. For the virtual machines running on the top of the Vsphere the single user mode is used for the deployment process. There are so many services in the Hadoop like the HBase, Impala, and spark. For using all these services Cloudera manager is essential. To spin up these services the cloudera distribution helps for monitoring and managing the services. Join the Big Data and Hadoop Training in Chennai to get placed in the big companies and learn the technology from the tech savvy people.
Deployment of cloudera has a long process of deployment such as base VM template, Centos guest configuration, VMs required for the deployment, directories to be created for the cloudera manager VM, prepare the data nodes and name nodes. After this finally the Cloudera is deployed to use the multiple services of the Hadoop. Join the Big Data Hadoop Training and revalue your knowledge with the latest industrial updates. The coordination between Cloudera and Horton works leads to increase in the partnership with the public cloud vendors.
R interface in Impala
The R along with the popular package dplyr is used for the interative SQL queries. The new R package provides a grammar for the data manipulation and they are mutate(), select(), filter(), summarise() and arrange(). The SQL commands are directly executed on Impala using the implyr in R. It becomes easy to communicate with other self-service data science tools with the help of the implyr. RStudio gives updates on dplyr, DBI, dbplyr and odbc and the job of data scientists becomes easy.The Best Hadoop Training in Chennai treat each students as the pillars and community followers to grow the technology.
New features in H-base
For the vast usage and the best software eco system No SQL system is a suitable one. As it handles huge volume of data it is not possible to connect the data base with relational data. The no SQL data base supports for the ACID feature, the default implementations and the different columns per individual row in the same table is possible. The HDFS data nodes support for the smooth ditribution of the data accross the nodes. RDMS is suitable for the static data and for the dynamic data Hadoop is suitable. There are so many structures used to store the data like the binary trees, red black trees, heaps and vectors. There is a new model in the H-base which is called as LSM tree which has two sub divisions to operate the data. One is called as in memory tree and the other one is called as the disk store tree. The in-memory tree consists of the latest data and the disk store tree consists of the balance part of the data. Take the list of Hadoop Training Institute in Chennai and prepare your mind for the best training to learn the technology intensively.
Hadoop and Business
The usage of data analysis is huge in the business and the verge of the technology decides the business opportunities. Banking and Securities industries is prone to the challenges in the industry like the fraud detection, archival of audit, enterprise credit risk reporting, customer data transformation, and social analytics for trading. To track the fraud detections in the financial markets the network analytics and natural language processors are used widely which is operated by the Hadoop. In the media big data take part to make the content for the different types of audiences, recommend the content which is highly on demand, and show the performance of the content in different locality or different devices. In the health care sector the data from the app gives history about the usage of the medicine. Google maps are used to know about the health care information to track the spread of the chronic diseases. Big data is used to overcome the challenges in the manufacturing industry. The finance industry, health care industry and the streaming industry is booming to the top with the Hadoop technology.
Comparison of Hadoop2 and hadoop3
The processing of data is important function in the Hadoop technology than the interaction with the user for the user satisfaction. If there is network failure and some parts of data are not available then HDFS recover the data needed in an efficient way. The partitioning process in hadoop separates the data as per the date or time, country or state, department, product type to do the batch processing. Static and dynamic partition both are done by the hive in hadoop. Join the Hadoop Training in Chennai to derive the benefits of learning Haoop with latest updates from the industry.
When analyzing the data, analysis is moved to the place of the data and it is not easy to move the data to the place of the application and this is the concept behind the data analysis. This is the reason why the processing and storage of data is fast in the Hadoop system to support the analysis. Java 8 is used in the new hadoop system whereas java 7 was used in the previous versions. Hadoop is updated with many different concepts and let us put light on the latest changes to know about the improvement. Hadoop 2 was released in the year 2013 and hadoop 3 in the year 2017 for the data analysis and find below the detailed comparison of the two versions. Join the Big Data Training in Chennai which takes the learners to prospective job in the job industry.
The storage option in Hadoop3
The fault tolerance in hadoop 2 and hadoop 3 are the same but hadoop 3 requires less space when compared to hadoop2. For every two blocks of data it creates one parity block which requires less space in the disk. Hadoop storage is through disk and not through RAM which makes hadoop the best solution for many of the big volume of databases. There are many libraries for the spark which is from the hadoop ecosystem, like Mlib. For using the Sql queries Spark SQL is used. Big Data Course in Chennai trains the candidates with the latest concepts and suppresses the candidates from the dearth of knowledge.
Hadoop 2 requires more disk space than hadoop3 due to the change in the architectural pattern of fault tolerance. Spark requires the RAM storage and it is more costly than Hadoop. Join the Big Data Training and know about the value of data and data analysis.
Data processing in Hadoop3
Live data processing is the trending one in business as many companies are demanding for immediate status. Apache spark is used for the data processing with live streams and it deals with the interactive mode. Map reduce, hive and pig are used for the data processing.
Difference between batch processing and live processing
Hadoop requires coding for some of the functions whereas Spark requires less coding. Hadoop is the engine with basic functions and incase of designing the other operations it requires plug-in component. Uber and Ola are the popular cab companies with the real time analysis. Hadoop Course in Chennai is the right course for the learners with analytics interest. The generated data is processed with very less time to improve the business. SWOT analysis is analyzing the strength, weakness, opportunity and threats to the business and this is derived after conducting the complex event processing. The CEP and Hadoop are used to provide the scalable in-memory layer to do the real time analysis in Hadoop.
Programming languages used
Both hadoop 2 and Hadoop 3 supports multiple programming languages. The wide range of languages used for the Hadoop eco system is Java, Scala, python and R. Java 8 is used in Hadoop3, Java 7 is used in Hadoop 2 and Scala is used in Spark for the development. Join the Best Big Data Training in Chennai at FITA and gain the practical knowledge with less effort.
Speed of Hadoop3
The speed of the hadoop 3 is comparatively high than the hadoop 2. The native java implementation on Hadoop makes hadoop 3 30 percent faster than Hadoop2. The native java is implemented in the map output of the Map Reduce in Hadoop eco system. Spark is 10 times faster than hadoop and process the information 100 times faster.
Security with Hadoop 3
The Kerberos which is the computer network authentication protocol is used in the Hadoop which made it the secure platform. Spark is considered as less security when comparing with Hadoop and Spark make use of the shared secret password. The HDFS file system in the Adoop cluster access the read and write requests. Apache H Base and Apache Accumulo store their data in HDFS. The authentication communication and the access to the data are checked by the Accumulo and H Base. The SQL queries are submitted by the Apache hive to the HDFS. Join the Hadoop Training in Velachery to know about the industrial challenges and industrial updates in Hadoop.
Changes in the Fault tolerance
There are so many replications of data to manage the fault or recovery of information. Hadoop 3 uses the erasure coding to avoid the replication. Hadoop creates one parity of block for every two blocks. Fault tolerance or failure management in Spark is processed with DAG. DAG stands for the Directed Acyclic Graph which is designed with vertices and edges. The RDD is calculated in the vertices and operation on RDD is saved on edges. Thus the data recovery is handled in Spark.
Changes in YARN
Hadoop 3 is updated with version 2 of YARN and there is separation in the collection of data, writing of data and reading of data. YARN is the resource manager which takes care of the CPU or memory or disk. The new version of YARN supports the logical groups and provides the metrics at the level of the flows.
Name Nodes in Hadoop 3
The previous version of Hadoop supported for the single name node and this new version of hadoop support for the multiple name nodes. Name node is the master and center piece of the HDFS. Data is stored in the data node and Meta data is stored in the name node. Name node occupies lot of memory in hadoop cluster as all the locations are stored in the name node. Big Data Training in Velachery offers the detailed training in the HDFS,YARN and Mapreduce to make the students ready for the interviews.
File system in Hadoop 3
Hadoop 3 supports all types of file system like Amazon S3, Azure storage, Microsoft Azure Data lake and Aliyun object storage system. Spark supports the Amazon S3 and HDFS.Spark operates on top of Hadoop and it also comes under the hadoop eco system. Spark is fast and Hadoop is suitable for the special features. Hadoop Training in Tambaram at FITA receives good feedback from the student’s year on year and we take our profession as the base to all the other software professions. So, we serve the learning community to make learning as an interesting task.
It is predicted from the study that big data will be used by the 80 percent of the companies by the year 2020. Retail industry, manufacturing industry, banking industry, finance industry, and health care industry are using big data for the analysis.
HDFS and Map Reduce is a perfect blend of technologies which make use of the positive, negative and neutral comment of the customer to know about the sentimental behavior of the customer. Join the Big Data Training in Tambaram to become a Hadoop developer or Hadoop admin. To analyze the comments the comments are added to the HDFS files or analyze the comments in the batch mode with map reduce. The Hive table is added with the timestamp attribute, who commented attribute, comment ID attribute and attitude with values. These changes will tell about the sentiment or behavior of the customer.
FITA Academy provides the best Big data Hadoop Training in Chennai with the help of Big Data professionals. Spend your valuable time to visit our branches in Chennai. FITA Academy is located at three main areas of Chennai, Velachery, T Nagar and OMR. People also search for
Hadoop Training in Velachery
Hadoop Training in Tambaram
Hadoop Training in OMR
Hadoop Training in Porur
Hadoop Training in Anna Nagar
Hadoop Training in T Nagar
Hadoop Training in Adyar
Hadoop Interview Questions
Hadoop technology is largely used by the web 2.0 companies like Google and Facebook as it is highly scalable open source data management system. Some of the branches of Hadoop are Hadoop architecture, Map Reduce, HDFS, YARN, pig, Hive, SPark, Oozie, Hbase, Squoop etc. Let me fetch the difficult questions from all these branches and help the learners to clear the interview with less effort. The data processing tools are located on the same server and the distributed file system on the cluster made the Hadoop as the fast and efficient system to process the terabytes of data.
- Explain the term Map Reduce?
To process the large data sets in the hadoop cluster the Map Reduce framework is used. There are two sets in the data process and they are the mapping of data and reduce process of the data which means filtering the data as per the query. Hadoop Training in Chennai teach about how to manage huge volume of data and analyze the huge volume of data.
- Explain the process of the Hadoop Map Reduce works?
Map Reduce count the words in each document and reduce the words or phase in to splits for the analysis. The map task is performed in the Map Reduce.
- Explain the term shuffling in Map Reduce?
The process of transferring the map outputs after the system performs the sort is called as shuffle. The system transfers the map outputs to the reducer as inputs in the Map Reduce. Big Data Training in Chennai aids for the advanced data analysis and this help to improve the profitability of the business.
- Define the term distributed Cache in Map Reduce Framework?
Distributed Cache is used to share some files from the nodes in the Hadoop Cluster and the file can be an executable jar files or simple properties file.
- Describe the actions followed by the Job tracker in Hadoop?
The Job tracker performs the actions like submitting the job to the job tracker from the client application, to determine the data location the job tracker communicates to the name mode, the task tracker nodes are located to near the data or with the available slots job tracker, the work is submitted by the job tracker to the chosen task tracker nodes, if there is failure in the task then the job tracker notify and decides what to do then, and the job tracker monitor the task tracker in the nodes.
- Mention what is the heartbeat in HDFS?
Data node and a name node pass signal and task tracker and job tracker also pass signal and this signal is called as the heart-beat of the HDFS. If there is any issue with the job tracker or the name node then the signal is not responded to the signal and then it is understood that there is some issues with the data node or task tracker.
- What is the purpose of using the Hadoop in the MapReduce job?
Combiners are used to increase the efficiency of the Map Reduce program, the data and the code can be reduced using the combiners. If the operation is cumulative and associative then reducer code is used as a combiner and it is also used to reduce the data before transferring. Big Data Course in Chennai help the employers to get high salary as it is the back bone of any business.
- Explain the scenarios in which the data node fails?
The data node fails when the tasks are re-scheduled in the node, the failure is detected from the jobtracker and the namenode, and the user data in the name node is replicated to another node.
- What are the two basic parameters of a mapper?
Longwritable and Text, Text and intWritable are the two parameters in a mapper.
- Describe the function of MApReduce partitioner?
The function of the MapReduce partitioner is to check the process of the key’s value goes to the reducer. These will distribute the map output evenly over the reducers. Big Data Course improves the job prospects to the freshers and experienced.
- Mention the difference between input split and the HDFS Block?
The HDFS block is the physical division of the data and the logical division of data is known as the input split of data.
- Describe the term textinformat in the Hadoop?
In testinformat the value is the content of the line, key is the byte offset of the line, and the text is the record in each line.
- Mention the configuration parameters which are needed to run the Mapreduce job?
Input format, output format, job’s input locations in the distributed file system, job’s output location in the distributed file system, class containing the map function, class containing the reduce function and the JAR file containing the mapper, reducer and driver classes are the configuration parameters in the MapReduce job.
- Describe the term WebDAV in Hadoop?
To access HDFS as a standard file system and expose the HDFS over WebDAV. HDFS file systems are mounted as file systems on most of the operating systems. WebDAV is a set of extensions to HTTP and it is used to support the editing and updating of the files. Big Data Training is the in-demand technology of this decade because of the wide of its componenets such as HDFS, YARN, Mapreduce, pig, hive, and sqoop etc.
- What is the function of the Squoop in hadoop?
To transform the data from MySQL or Oracle squoop is used. To export data from HDFS to RDMS and to import Data from RDMS to HDFS Squoop is used.
- Explain the function of a job tracker when scheduling a task?
To check whether the job tracker is active and functioning well the task tracker sends heartbeat messages to the job tracker. The number of available slots and this gives updation to the job tracker regarding the cluster work to be delegated.
- Describe the sequencefileinputformat in the hadoop?
Sequencefileinputformat is used to read the files in sequence and it pass the data from one mapreduce job to the other mapreduce job. It is a binary file format which is optimized for passing the data.
- Explain the function of the conf.setMapper class?
Conf.setMapperclass sets the stuff related to map job such as reading data and generating a key value pair out of the mapper and it is called as a mapper class. Big Data Hadoop Training in Chennai trains the candidates with the rela time projects and the practical knowledge which makes the students as like experienced professional in the hadoop technology.
- List out the core components of Hadoop?
The core components of hadoop are HDFS and Mapreduce. Big Data Training and Placement in Chennai know about the standards needed for the industry and train the students as per the need of the job industry.
- Describe the functions of the namenode in hadoop?
Namenode consists of information which run job tracker and consists of metadata. It is the master node on which the job tracker runs.
Hadoop Job Openings
Job Title: Hadoop Developer
Responsibility: do the data analysis; handle the big volume of data.
Job Description: Do the environment setup, knowledge on tool for the data ingestion, use the sqoop with Ooie for job scheduler, build the blocks of hadoop, HDFS, Mapreduce, and Yarn, Experience with spark, python and knowledge of data analysis tools like apache nifi is an addded advantage. Knowledge about Hive, pig and Hbase is required for the data visualization.
Company Name: BCT consulting private limited
Contact Details: www.bahwancybertek.com is the website and contact person name is Senthil Kumar
Date of Interview: After the screening the company HR person will intimate the date.
Job Title: Hadoop Tester
Responsibility: 5 plus years of experience and need immediate joiners.
Job Description: Knowledge of Hadoop and testing knowledge.
Company Name: Future Focus Infotech
Contact Details: firstname.lastname@example.org
Date of Interview: Send the detailed biodata with all the details like current company, payroll company, total experience, real experience, current CTC, expected CTC, notice period, current location and preferred location. After the screening the HR will intimate the date.
Job Title: Hadoop Developer
Responsibility: Transform the business requirements in to specifications, policies, business procedures, and measurement and data mappings. Use the tools like SSIS, SSRS, HDFS, Squoop, Hive, Impala, HBase, Solr and other big data technologies.
Job Description: Understand the multiple sources of data, ensure the deliverables with the quality standards, inter-personal skills to coordinate with the co-workers, and work collaboratively. He or she should have 3 to 6 years of experience in the respective job role.
Company Name: Mbit Computraining Pvt. Ltd.
Contact Details: Drop your resume to email@example.com
Date of Interview: After the screening the contact person from the company will tell the interview date.
Job Tittle: Senior Hadoop Developer
Responsibility: Develop solutions as per the requirements and architecture, design good data solutions, model the data for the reporting, dashboard information and analytics.
Job Description: 5 to 10 years of hands on experience in Apache NiFi, Flume, hadoop HDFS, hadoop mapreduce, hive, HBase, Pig, Pig, Spark, Mahout, Oozie,and Sqoop. Experience in spark, Scala, hortonworks data flow, VM’s, Linux OS and Ubuntu OS.
Company Name: MSR COSMOS PVT ltd.
Contact Details: Drop your email to firstname.lastname@example.org and know about the interview details.
Date of Interview: After screening the interview the HR will send the call letter.
Job Title: VP-Bigdata Hadoop Spark Developer
Responsibility: The responsibility of the joinee includes managing the team of big data developers, Do the analysis and help the team regarding the solutions to the behavioural analytics solutions with the pyspark.
Job Description: Experience in the field with 12 to 16 yars in the development and desing. Proven record in the team management and project execution for a period of 5 years, experience in Bigdata, python, spark, and trade surveillance will be preferred.
Company Name: Citicorp Services India PVT Ltd.
Location: Pune (Kharadi)
Contact Details: Sent your bio-data to email@example.com and after the screening you will get the interview schedule.
Date of Interview: After the screening the HR will send the call letter to the candidate.
Job Title: Hadoop Developer
Responsibility: To handle the data analysis and manage the team members to anticipate the solutions to the problems.
Job Description: Take care of development and design for the hadoop eco system. To analyze the big pool of data and handle the hadoop clusters.
Company Name: Avantha Holdings Limited
Location: Delhi NCR
Date of Interview: After screening the bio-data the call letter will be sent to the respective candidates.
Hadoop Sample Resumes
Execution of applications in Hadoop is done using the MapReduce algorithm the in which the data is processed in a parallel manner with others. In other words, Hadoop is used in order to develop various applications that will be able to perform complete statistical analysis over huge amounts of data. Thus, there are numerous uses of joining Hadoop Training in Chennai.
Modules of Hadoop
The various modules present in Hadoop are enlisted below:
HDFS: HDFS stands for Hadoop Distributed File System. According to the paper published by Google on the basis of HDFS states that files will be broken into small blocks and stored in nodes over distributed architecture.
It has numerous similarities with the existing distributed file systems. This is highly fault-tolerant and is designed to be used on low-cost hardware along with producing high throughput access to the application data. Therefore, by learning in-depth knowledge become an expert in Big Data Training in Chennai.
Hadoop framework consists of the following two modules −
- Hadoop Common − Java libraries and utilities that are required by other Hadoop modules.
- Hadoop YARN – It is a framework for scheduling job along with cluster resource management.
Map Reduce: It is a framework that helps Java programs to do parallel computation on data with the usage of key value pair. This takes input data and converts into a data set that can be computed in the Key value pair. The output is consumed by reduce task followed by the desired output.
How does Hadoop work?
It is expensive to build large servers with heavy configurations in order to handle large scale processing. Hadoop enables execution of code across a cluster of computers and this includes the given core tasks that is performed by Hadoop −
- Data is divided into directories and files. And Files are further divided into consistent sized blocks of 128M and 64M.
- Files are then shared across various cluster nodes for the further process.
- HDFS supervises the whole process.
- Checking the execution of code successfully.
- Blocks are copied for handling hardware failure.
- Performing the sorting of data that takes place among map and reduce stages.
- Sending the previously sorted data to specific computer.
- Scripting the debugging logs for each job.
Hadoop Operation modes
After the downloading of Hadoop, begins the process of operating Hadoop cluster in any of the following modes supported by it:
- Standalone Mode– By default, it is configured in this mode and can be executed as a single java process.
- Pseudo Distributed Mode − Each Hadoop daemon like hdfs or yarn will be executed as a separate java process and this mode is useful for development stage.
- Fully Distributed Mode– It is fully distributed with minimum two machines as a cluster..
The production environment for Hadoop is UNIX, still it can also be used in Windows by deploying Cygwin. Java 1.6 and later version is needed to run Map Reduce Programs. For the installation of Hadoop from tar ball on UNIX environment you need
- Java Installation
- SSH installation
- Hadoop Installation
- File Configuration
Join our Hadoop Training Institute in Chennai and get yourself equipped with the latest trends in the market.
Hadoop File System was developed with the use of distributed file system design. And is run on commodity hardware. HDFS posses large amount of data with providing easier access. HDFS makes applications accessible to parallel processing.
Features of HDFS
- It is appropriate for distributed storage along with processing.
- It provides a command interface in order to interact with HDFS.
- It also provides file permissions along with authentication.
- The built-in servers namely namenode and datanode aid the user to easily check the status of cluster.
Trends of Big data Hadoop
Big data is a vast field to get into and data is considered the next precious asset for human race. There are many innovations done in and around Big data in the market. The experts rate FITA as no.1 Big Data Hadoop Training in Chennai. The top trending features are listed below:
Bots replacing individuals making it simple!
In this fast moving world, it is necessary to be smarter with the evolution of technology. It is human nature to make mistakes and thus some of the leading companies has made the usage of Robots for support services.
Siri may be the lead for this innovative idea out forth amidst the MNCs. Another well-known example is the deployment of Chatbots for taking orders over text and MasterCard replies to the queries related to the transaction.
There is already a good preservation of amount for every interaction, which is $0.70 and is expected to increase in the forth-coming year.
Artificial Intelligence more accessible
The usage of integration AI enabled functionality is to estimate to reach 75% by the end of the year 2018.
The Glucon Network Project, of Microsoft has been merged with Amazon. This project allows the developers to build and deploy their models in the cloud.
Swift online purchase
E-commerce has a great impact on our daily life, as people prefer digitalization to traditional shopping methods. IBM’s Watson is a great example for that provides slew of order administration. In the year 2016, an AI gift concierge namely Gifts When You Need (GWYN) was launched by 1-800-Flowers.com. It was a huge success in the market. In this the information provided by customers about a specific gift beneficiary, software tailors recommend gift after the comparison of purchased specification provided by similar recipients.
FITA rated as No: 1 Training Institute for Big Data Hadoop Training in Velachery.