#zookeeper. hdfs

[tejaug]

Hadoop Eco System

The Hadoop ecosystem is a framework and suite of technologies for handling large-scale data processing and analysis. It’s built around the Hadoop platform, which provides the essential infrastructure for storing and processing big data. The ecosystem includes various tools and technologies that complement and extend Hadoop’s capabilities. Key components include:

Hadoop Distributed File System (HDFS): A distributed file system that provides high-throughput access to application data. It’s designed to store massive data sets reliably and to stream those data sets at high bandwidth to user applications.

MapReduce: A programming model and processing technique for distributed computing. It processes large data sets with a parallel, distributed algorithm on a cluster.

YARN (Yet Another Resource Negotiator): Manages and monitors cluster resources and provides a scheduling environment.

Hadoop Common: The standard utilities that support other Hadoop modules.

Pig: A platform for analyzing large data sets. Pig uses a scripting language named Pig Latin.

Hive: A data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis.

HBase: A scalable, distributed database that supports structured data storage for large tables.

Sqoop: A tool designed for efficiently transferring bulk data between Apache Hadoop and structured data stores such as relational databases.

Zookeeper: A centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services.

Oozie: A workflow scheduler system to manage Hadoop jobs.

Flume: A distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.

Apache Spark: An open-source, distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Although Spark is part of the broader Hadoop ecosystem, it does not use the MapReduce paradigm for data processing; it has its own distributed computing framework.

These components are designed to provide a comprehensive ecosystem for processing large volumes of data in various ways, including batch processing, real-time streaming, data analytics, and more. The Hadoop ecosystem is widely used in industries for big data analytics, including finance, healthcare, media, retail, and telecommunications.

Hadoop Training Demo Day 1 Video:

You can find more information about Hadoop Training in this Hadoop Docs Link

Conclusion:

Unogeeks is the №1 IT Training Institute for Hadoop Training. Anyone Disagree? Please drop in a comment

You can check out our other latest blogs on Hadoop Training here — Hadoop Blogs

Please check out our Best In Class Hadoop Training Details here — Hadoop Training

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[manasranjanmurlidharrana]

How Mr. Manasranjan Murlidhar Rana Helped Union Bank Switzerland as a Certified Hadoop Administrator

Mr. Manasranjan Murlidhar Rana is a certified Hadoop Administrator and an IT professional with 10 years of experience. During his entire career, he has contributed a lot to Hadoop administration for different organizations, including the famous Union Bank of Switzerland.

Mr. Rana’s Knowledge in Hadoop Architecture and its Components

Mr. Manasranjan Murlidhar Rana has vast knowledge and understanding of various aspects related to Hadoop Architecture and its different components. These are MapReduce, YARN, HDFS, HBase, Pig, Flume, Hive, and Zookeeper. He even has the experience to build and maintain multiple clusters in Hadoop, like the production and development of diverse sizes and configurations.

His contribution is observed in the establishment of rack topology to deal with big Hadoop clusters. In this blog post, we will discuss in detail about the massive contribution of Manasranjan Murlidhar Rana as a Hadoop Administrator to deal with various operations of the Union Bank of Switzerland.

Role of Mr. Rana in Union Bank of Switzerland

Right from the year 2016 to until now, Mr. Manasranjan Murlidhar Rana played the role of a Hadoop Administrator with 10 other members for his client named Union Bank of Switzerland. During about 4 years, he worked a lot to enhance the process of data management for his client UBS.

1. Works for the Set up of Hadoop Cluster

Manasranjan Murlidhar Rana and his entire team were involved in the set up of the Hadoop Cluster in UBS right from the beginning to the end procedure. In this way, the complete team works hard to install, configure, and monitor the complete Hadoop Cluster effectively. Here, the Hadoop cluster refers to a computation cluster designed to store and analyze unstructured data in a well-distributed computational environment.

2. Handles 4 Different Clusters and Uses Ambari Server

Mr. Manasranjan Murlidhar Rana is responsible for handling four different clusters of the software development process. These are DEV, UAT, QA, and Prod. He and his entire team even used the innovative Ambari server extensively to maintain different Hadoop cluster and its components. The Ambari server collects data from a cluster and thereby, controls each host.

3. Cluster Maintenance and Review of Hadoop Log Files

Mr. Manasranjan Murlidhar Rana and his team have done many things to maintain the entire Hadoop cluster, along with commissioning plus decommissioning of data nodes. Moreover, he contributed to monitoring different software development related clusters, troubleshoot and manage the available data backups, while reviewed log files of Hadoop. He also reviewed and managed log files of Hadoop as an important of the Hadoop administration to communicate, troubleshoot, and escalate tons of issues to step ahead in the right direction.

4. Successful Installation of Hadoop Components and its Ecosystem

Hadoop Ecosystem consists of Hadoop daemons. Hadoop Daemons in terms of computation imply a process operating in the background and they are of five types, i.e. DataNode, NameNode, TaskTracker, JobTracker, and Secondary NameNode.

Besides, Hadoop has few other components named Flume, Sqoop and HDFS, all of which have specific functions. Indeed, installation, configuration, and maintenance of each of the Hadoop daemons and Hadoop ecosystem components are not easy.

However, based on the hands-on experience of Mr. Manasranjan Rana, he succeeded to guide his entire team to install Hadoop ecosystems and its components named HBase, Flume, Sqoop, and many more. Especially, he worked to use Sqoop to import and export data in HDFS, while to use Flume for loading any log data directly into HDFS.

5. Monitor the Hadoop Deployment and Other Related Procedures

Based on the vast knowledge and expertise to deal with Hadoop elements, Mr. Manasranjan Murlidhar Rana monitored systems and services, work for the architectural design and proper implementation of Hadoop deployment and make sure of other procedures, like disaster recovery, data backup, and configuration management.

6. Used Cloudera Manager and App Dynamics

Based on the hands-on experience of Mr. Manasranjan Murlidhar Rana to use App Dynamics, he monitored multiple clusters and environments available under Hadoop. He even checked the job performance, workload, and capacity planning with the help of the Cloudera Manager. Along with this, he worked with varieties of system engineering aspects to formulate plans and deploy innovative Hadoop environments. He even expanded the already existing Hadoop cluster successfully.

7. Setting Up of My-SQL Replications and Maintenance of My-SQL Databases

Other than the expertise of Mr. Manasranjan Murlidhar Rana in various aspects of Bigdata, especially the Hadoop ecosystem and its components, he has good command on different types of databases, like Oracle, Ms-Access, and My-SQL.

Thus, according to his work experience, he maintained databases by using My-SQL, established users, and maintained the backup or recovery of available databases. He was also responsible for the establishment of master and slave replications for the My-SQL database and helped business apps to maintain data in various My-SQL servers.

Therefore, with good knowledge of Hadoop Ambari Server, Hadoop components, and demons, along with the entire Hadoop Ecosystem, Mr. Manasranjan Murlidhar Rana has given contributions towards the smart management of available data for the Union Bank of Switzerland.

Find Mr. Manasranjan Murlidhar Rana on Social Media. Here are some social media profiles:-

https://giphy.com/channel/manasranjanmurlidharrana https://myspace.com/manasranjanmurlidharrana https://mix.com/manasranjanmurlidhar https://www.meetup.com/members/315532262/ https://www.goodreads.com/user/show/121165799-manasranjan-murlidhar https://disqus.com/by/manasranjanmurlidharrana/

[letsneeharikathings-blog]

HADOOP BIG DATA ANALYTICS WEBINAR

Course Highlights :

What you will learn ?

HDFS, MapReduce, Pig,Hive, Hbase,NOSql, Hbase, *Flume,Kafka,Yarn,zookeeper

And also you get 👇

1. Live Practice session on each Module 2. Assignments to practice 3. Material guide 4. 2 Real Time Projects 5. Case Study approach 6. Hands On training Experience 7. Real Time Experts as trainers from Microsoft 8. Mock Interviews 9. Resume Guidance 10. FAQs in the interview 11. Dedicated student level manager 12. Placement assistance 13. Certification 14. Kaggle and git hub and git lab Authorization accounts and profile generation.

To know more attend the demo by registering here 👇

https://bit.ly/20thJuneWebinarBigData

[kdeven57-blog]

In this Apache Hadoop tutorial you will learn Hadoop from the basics to pursue a big data Hadoop job role. Through this tutorial you will know the Hadoop architecture, its main components like HDFS, MapReduce, HBase, Hive, Pig, Sqoop, Flume, Impala, Zookeeper and more. You will also learn Hadoop installation, how to create a multi-node Hadoop cluster and deploy it successfully. Learn Big Data Hadoop from Intellipaat Hadoop training and fast-track your career.

Hadoop Tutorial – Learn Hadoop from Experts

In this Apache Hadoop tutorial you will learn Hadoop from the basics to pursue a big data Hadoop job role. Through this tutorial you will know the Hadoop architecture, its main components like HDFS, MapReduce, HBase, Hive, Pig, Sqoop, Flume, Impala, Zookeeper and more. You will also learn Hadoop installation, how to create a multi-node Hadoop cluster and deploy it successfully. Learn Big Data Hadoop from Intellipaat Hadoop training and fast-track your career.

Overview of Apache Hadoop

As Big Data has taken over almost every industry vertical that deals with data, the requirement for effective and efficient tools for processing Big Data is at an all-time high. Hadoop is one such tool that has brought a paradigm shift in this world. Thanks to the robustness that Hadoop brings to the table, users can process Big Data and work around it with ease. The average salary of a Hadoop Administrator which is in the range of US$130,000 is also very promising.

Become a Spark and Hadoop Developer by going through this online Big Data Hadoop training!

Watch this Hadoop Tutorial for Beginners video before going further on this Hadoop tutorial.

Apache Hadoop is a Big Data ecosystem consisting of open source components that essentially change the way large datasets are analyzed, stored, transferred and processed. Contrasting to traditional distributed processing systems, Hadoop facilitates multiple kinds of analytic workloads on same datasets at the same time.

Qualities That Make Hadoop Stand out of the Crowd

Single namespace by HDFS makes content visible across all the nodes

Easily administered using High-Performance Computing (HPC)

Querying and managing distributed data are done using Hive

Pig facilitates analyzing the large and complex datasets on Hadoop

HDFS is designed specially to give high throughput instead of low latency.

Interested in learning Hadoop? Click here to learn more from this Big Data Hadoop Training in London!

What is Apache Hadoop?

Apache Hadoop is an open-source data platform or framework developed in Java, dedicated to store and analyze the large sets of unstructured data.

With the data exploding from digital mediums, the world is getting flooded with cutting-edge big data technologies. However, Apache Hadoop was the first one which caught this wave of innovation.

 Recommended Audience 

Intellipaat’s Hadoop tutorial is designed for Programming Developers and System Administrators

Project Managers eager to learn new techniques of maintaining large datasets

Experienced working professionals aiming to become Big Data Analysts

Mainframe Professionals, Architects & Testing Professionals

Entry-level programmers and working professionals in Java, Python, C++, eager to learn the latest Big Data technology.

 If you have any doubts or queries related to Hadoop, do post them on Big Data Hadoop and Spark Community!

Originally published at www.intellipaat.com on August 12, 2019

[berryinfotech]

Big Data Hadoop Training

About Big Data Hadoop Training Certification Training Course

It is an all-inclusive Hadoop Big Data Training Course premeditated by industry specialists considering present industry job necessities to offers exhaustive learning on big data and Hadoop modules. This is an industry recognized Big Data Certification Training course that is known as combination of the training courses in Hadoop developer, Hadoop testing, analytics and Hadoop administrator. This Cloudera Hadoop training will prepare you to clear big data certification.            

Big data Hadoop online training program not only prepare applicant with the important and best concepts of Hadoop, but also give the required work experience in Big Data and Hadoop by execution of actual time business projects.

Big Data Hadoop Live Online Classes are being conducted by using professional grade IT Conferencing System from Citrix.  All the student canintermingle with the faculty in real-time during the class by having chat and voice. There student need to install a light- weight IT application on their device that could be desktop, laptop, mobile and tablet.

So, whether you are planning to start your career, or you need to leap ahead by mastering advanced software, this course covers all things that is expected of expert Big Data professional. Learn skills that will distinguish you instantly from other Big Data Job seekers with exhaustive coverage of Strom, MongoDB, Spark and Cassandra. Quickly join the institution that is well-known worldwide for its course content, hands-on experience, delivery and market- readiness.

Know about the chief points of our Big Data Hadoop Training Online

The Big Data Hadoop Certification course is specially designed to provide you deep knowledge of the Big Data framework by using the Hadoop and Spark, including HDFS, YARN, and MapReduce. You will come to know how to use Pig, Impala to procedure and analyse large datasets stored in the HDFS, and usage Sqoop and Flume for data absorption along with our big Data training.

With our big data course, you will also able to learn the multiple interactive algorithms in Spark and use Spark SQL for creating, transforming and querying data forms. This is guarantee that you will become master real- time data processing by using Spark, including functional programming in Spark, implementing Spark application, using Spark RDD optimization techniques and understanding parallel processing in Spark.

As a part of big data course, you will be needed to produce real- life business- based projects by using CloudLab in the domains of banking, social media, insurance, telecommuting and e-commerce.  This big data Hadoop training course will prepare you for the Cloudera CCA1775 big data certification.

What expertise you will learn with this Big Data Hadoop Training?

Big data Hadoop training will permit you to master the perceptions of the Hadoop framework and its deployment in cluster environment. You would learn to:

Let’s understand the dissimilar components/ features of Hadoop ecosystem such as - HBase, Sqoop, MapReduce, Pig, Hadoop 2.7, Yarn, Hive, Impala, Flume and Apache Spark with this Hadoop course.

·         Be prepared to clear the Big Data Hadoop certification

·         Work with Avro data formats

·         Practice real- life projects by using Hadoop and Apache Spark

·         Facility to make you learn Spark, Spark RDD, Graphx, MLlib writing Spark applications

·         Detailed understanding of Big data analytics

·         Master Hadoop administration activities like cluster,monitoring,managing,troubleshooting and administration

·         Master HDFS, MapReduce, Hive, Pig, Oozie, Sqoop, Flume, Zookeeper, HBase

Setting up Pseudo node and Multi node cluster on Amazon EC2

Master fundamentals of Hadoop 2.7 and YARN and write     applications using them

Configuring ETL tools like Pentaho/Talend to work with     MapReduce, Hive, Pig, etc

Hadoop testing applications using MR Unit and other automation     tools.

[karonbill]

HCIA-Big Data V3.0 H13-711_V3.0 Real Questions and Answers

Anyone who want to achieve the HCIA-Big Data Certification can choose PassQuestion HCIA-Big Data V3.0 H13-711_V3.0 Real Questions and Answers for your best preparation.All of our H13-711_V3.0 Questions and Answers are created by our experts that will help you achieve the best outcome in a short time. It will help you build confidence and you will be able to find out important tips to attempt your H13-711_V3.0 exam.Make sure to go through all the HCIA-Big Data V3.0 H13-711_V3.0 Real Questions and Answers that will help you prepare for the real exam and you will be able to clear your HCIA-Big Data V3.0 exam on the first attempt.

HCIA-Big Data Certification

After passing HCIA-Big Data Certification,you should learn the knowledge of Technical principles and architecture of common and important big data components. Knowledge and skills required for big data pre-sales, big data project management, and big data development.This exam is suitable for those who desire to become big data engineers. Those who desire to obtain the HCIA-Big Data certification. Junior big data engineers.

Notice: There are currently two versions you can take for your HCIA-Big Data Certification, HCIA-Big Data V2.0 certification will be brought offline on February 28, 2022.HCIA-Big Data V3.0 is now recommended.

Huawei HCIA-Big Data V3.0 Certification Exam Information

Certification: HCIA-Big Data Exam Code: H13-711 Exam Name: HCIA-Big Data V3.0 Language: ENU Exam Format: Single Answer, Multiple Answer, True-false Question Exam Cost: 300USD Exam Duration: 90 mins Pass Score/ Total Score: 600/1000

Exam Content

HCIA-Big Data V3.0 exam covers: (1) Development trend of the big data industry, big data features, and Huawei Kunpeng big data. (2) Basic technical principles of common and important big data components (including HDFS, ZooKeeper, Hive, HBase, MapReduce, YARN, Spark, Flink, Flume, Loader, Kafka, LDAP and Kerberos, Elasticsearch and Redis). (3) Huawei big data solutions, functions and features, and success stories in the big data industry.

Key Points Percentage

1. Big Data Development Trend and Kunpeng Big Data Solution 3% 2. HDFS and ZooKeeper 12% 3. Hive - Distributed Data Warehouse 10% 4. HBase Technical Principles 11% 5. MapReduce and YARN Technical Principles 9% 6. Spark In-Memory Distributed Computing 7% 7. Flink, Stream and Batch Processing in a Single Engine 8% 8. Flume - Massive Log Aggregation 7% 9. Loader Data Conversion 5% 10. Kafka - Distributed Publish-Subscribe Messaging System 9% 11. LDAP and Kerberos 5% 12. Elasticsearch - Distributed Search Engine 5% 13. Redis In-Memory Database 5% 14. Huawei Big Data Solution 4%

View Online HCIA-Big Data V3.0 H13-711_V3.0 Free Questions

In Fusioninsight HD, which of the following is not part of the flow control feature of Hive? (Multiple choice) A. Support threshold control of the total number of established connections B. Support threshold control of the number of connections that each user has established C. Support threshold control of the number of connections established by a specific user D. Support threshold control of the number of connections established per unit time Answer: ABD

Which of the following descriptions are correct about Fusionlnsight HD cluster upgrade? (Multiple choice) A. It is not possible to manually switch between active and standby OMS during the upgrade process B. Keep the root account passwords of all hosts in the cluster consistent C. Keep the network open. Avoid abnormal upgrades due to network problems D. Expansion cannot be done during the observation period Answer: ABCD

When the Fusionlnsight HD product deploys Kerberos and LDAP services, which of the following descriptions is correct? (Multiple choice) A. Before deploying Kerberos service, LDAP service must be deployed B. LDAP service and Kerberos service must be deployed on the same node C. Kerberos service and LDAP service are deployed on the same node to facilitate data access and improve performance D. LDAP service can be shared by multiple clusters Answer: AC

Which of the following targets can Fusioninsight HD Loader export HDFS data to? (Multiple choice) A. SFTP server B. FTP server C. Oracle database D. DB2 database Answer: ABCD

What are the key features of Streaming in Huawei's big data product Fusioninsight HD? (Multiple choice) A. Flexibility B. Scalability C. Disaster tolerance D. Message reliability Answer: ABCD

Which of the following sub-products does the Fusioninsight family include? (Multiple choice) A. Fusioninsight Miner B. Fusioninsight Farmer C. Fusioninsight HD D. GaussDB 200 Answer: ABCD

[mmorellm]

Open Source Definitely Changed Storage Industry With Linux and other technologies and products, it impacts all areas. By Philippe Nicolas | February 16, 2021 at 2:23 pm It’s not a breaking news but the impact of open source in the storage industry was and is just huge and won’t be reduced just the opposite. For a simple reason, the developers community is the largest one and adoption is so wide. Some people see this as a threat and others consider the model as a democratic effort believing in another approach. Let’s dig a bit. First outside of storage, here is the list some open source software (OSS) projects that we use every day directly or indirectly: Linux and FreeBSD of course, Kubernetes, OpenStack, Git, KVM, Python, PHP, HTTP server, Hadoop, Spark, Lucene, Elasticsearch (dual license), MySQL, PostgreSQL, SQLite, Cassandra, Redis, MongoDB (under SSPL), TensorFlow, Zookeeper or some famous tools and products like Thunderbird, OpenOffice, LibreOffice or SugarCRM. The list is of course super long, very diverse and ubiquitous in our world. Some of these projects initiated some wave of companies creation as they anticipate market creation and potentially domination. Among them, there are Cloudera and Hortonworks, both came public, promoting Hadoop and they merged in 2019. MariaDB as a fork of MySQL and MySQL of course later acquired by Oracle. DataStax for Cassandra but it turns out that this is not always a safe destiny … Coldago Research estimated that the entire open source industry will represent $27+ billion in 2021 and will pass the barrier of $35 billion in 2024. Historically one of the roots came from the Unix – Linux transition. In fact, Unix was largely used and adopted but represented a certain price and the source code cost was significant, even prohibitive. Projects like Minix and Linux developed and studied at universities and research centers generated tons of users and adopters with many of them being contributors. Is it similar to a religion, probably not but for sure a philosophy. Red Hat, founded in 1993, has demonstrated that open source business could be big and ready for a long run, the company did its IPO in 1999 and had an annual run rate around $3 billion. The firm was acquired by IBM in 2019 for $34 billion, amazing right. Canonical, SUSE, Debian and a few others also show interesting development paths as companies or as communities. Before that shift, software developments were essentially applications as system software meant cost and high costs. Also a startup didn’t buy software with the VC money they raised as it could be seen as suicide outside of their mission. All these contribute to the open source wave in all directions. On the storage side, Linux invited students, research centers, communities and start-ups to develop system software and especially block storage approach and file system and others like object storage software. Thus we all know many storage software start-ups who leveraged Linux to offer such new storage models. We didn’t see lots of block storage as a whole but more open source operating system with block (SCSI based) storage included. This is bit different for file and object storage with plenty of offerings. On the file storage side, the list is significant with disk file systems and distributed ones, the latter having multiple sub-segments as well. Below is a pretty long list of OSS in the storage world. Block Storage Linux-LIO, Linux SCST & TGT, Open-iSCSI, Ceph RBD, OpenZFS, NexentaStor (Community Ed.), Openfiler, Chelsio iSCSI, Open vStorage, CoprHD, OpenStack Cinder File Storage Disk File Systems: XFS, OpenZFS, Reiser4 (ReiserFS), ext2/3/4 Distributed File Systems (including cluster, NAS and parallel to simplify the list): Lustre, BeeGFS, CephFS, LizardFS, MooseFS, RozoFS, XtreemFS, CohortFS, OrangeFS (PVFS2), Ganesha, Samba, Openfiler, HDFS, Quantcast, Sheepdog, GlusterFS, JuiceFS, ScoutFS, Red Hat GFS2, GekkoFS, OpenStack Manila Object Storage Ceph RADOS, MinIO, Seagate CORTX, OpenStack Swift, Intel DAOS Other data management and storage related projects TAR, rsync, OwnCloud, FileZilla, iRODS, Amanda, Bacula, Duplicati, KubeDR, Velero, Pydio, Grau Data OpenArchive The impact of open source is obvious both on commercial software but also on other emergent or small OSS footprint. By impact we mean disrupting established market positions with radical new approach. It is illustrated as well by commercial software embedding open source pieces or famous largely adopted open source product that prevent some initiatives to take off. Among all these scenario, we can list XFS, OpenZFS, Ceph and MinIO that shake commercial models and were even chosen by vendors that don’t need to develop themselves or sign any OEM deal with potential partners. Again as we said in the past many times, the Build, Buy or Partner model is also a reality in that world. To extend these examples, Ceph is recommended to be deployed with XFS disk file system for OSDs like OpenStack Swift. As these last few examples show, obviously open source projets leverage other open source ones, commercial software similarly but we never saw an open source project leveraging a commercial one. This is a bit antinomic. This acts as a trigger to start a development of an open source project offering same functions. OpenZFS is also used by Delphix, Oracle and in TrueNAS. MinIO is chosen by iXsystems embedded in TrueNAS, Datera, Humio, Robin.IO, McKesson, MapR (now HPE), Nutanix, Pavilion Data, Portworx (now Pure Storage), Qumulo, Splunk, Cisco, VMware or Ugloo to name a few. SoftIron leverages Ceph and build optimized tailored systems around it. The list is long … and we all have several examples in mind. Open source players promote their solutions essentially around a community and enterprise editions, the difference being the support fee, the patches policies, features differences and of course final subscription fees. As we know, innovations come often from small agile players with a real difficulties to approach large customers and with doubt about their longevity. Choosing the OSS path is a way to be embedded and selected by larger providers or users directly, it implies some key questions around business models. Another dimension of the impact on commercial software is related to the behaviors from universities or research centers. They prefer to increase budget to hardware and reduce software one by using open source. These entities have many skilled people, potentially time, to develop and extend open source project and contribute back to communities. They see, in that way to work, a positive and virtuous cycle, everyone feeding others. Thus they reach new levels of performance gaining capacity, computing power … finally a decision understandable under budget constraints and pressure. Ceph was started during Sage Weil thesis at UCSC sponsored by the Advanced Simulation and Computing Program (ASC), including Sandia National Laboratories (SNL), Lawrence Livermore National Laboratory (LLNL) and Los Alamos National Laboratory (LANL). There is a lot of this, famous example is Lustre but also MarFS from LANL, GekkoFS from University of Mainz, Germany, associated with the Barcelona Supercomputing Center or BeeGFS, formerly FhGFS, developed by the Fraunhofer Center for High Performance Computing in Germany as well. Lustre was initiated by Peter Braam in 1999 at Carnegie Mellon University. Projects popped up everywhere. Collaboration software as an extension to storage see similar behaviors. OwnCloud, an open source file sharing and collaboration software, is used and chosen by many universities and large education sites. At the same time, choosing open source components or products as a wish of independence doesn’t provide any kind of life guarantee. Rremember examples such HDFS, GlusterFS, OpenIO, NexentaStor or Redcurrant. Some of them got acquired or disappeared and create issue for users but for sure opportunities for other players watching that space carefully. Some initiatives exist to secure software if some doubt about future appear on the table. The SDS wave, a bit like the LMAP (Linux, MySQL, Apache web server and PHP) had a serious impact of commercial software as well as several open source players or solutions jumped into that generating a significant pricing erosion. This initiative, good for users, continues to reduce also differentiators among players and it became tougher to notice differences. In addition, Internet giants played a major role in open source development. They have talent, large teams, time and money and can spend time developing software that fit perfectly their need. They also control communities acting in such way as they put seeds in many directions. The other reason is the difficulty to find commercial software that can scale to their need. In other words, a commercial software can scale to the large corporation needs but reaches some limits for a large internet player. Historically these organizations really redefined scalability objectives with new designs and approaches not found or possible with commercial software. We all have example in mind and in storage Google File System is a classic one or Haystack at Facebook. Also large vendors with internal projects that suddenly appear and donated as open source to boost community effort and try to trigger some market traction and partnerships, this is the case of Intel DAOS. Open source is immediately associated with various licenses models and this is the complex aspect about source code as it continues to create difficulties for some people and entities that impact projects future. One about ZFS or even Java were well covered in the press at that time. We invite readers to check their preferred page for that or at least visit the Wikipedia one or this one with the full table on the appendix page. Immediately associated with licenses are the communities, organizations or foundations and we can mention some of them here as the list is pretty long: Apache Software Foundation, Cloud Native Computing Foundation, Eclipse Foundation, Free Software Foundation, FreeBSD Foundation, Mozilla Foundation or Linux Foundation … and again Wikipedia represents a good source to start.

Open Source Definitely Changed Storage Industry - StorageNewsletter

[arthur-damasio]

Overview: Apache Hadoop é uma estrutura de código aberto destinada a facilitar a interação com big data. No entanto, para aqueles que não estão familiarizados com essa tecnologia, surge uma pergunta: o que é big data? Big data é um termo dado aos conjuntos de dados que não podem ser processados de maneira eficiente com a ajuda de metodologia tradicional, como RDBMS. . O Hadoop fez seu lugar nas indústrias e empresas que precisam trabalhar em grandes conjuntos de dados que são sensíveis e precisam de tratamento eficiente, é uma estrutura que permite o processamento de grandes conjuntos de dados que residem na forma de clusters. Por ser um framework, o Hadoop é composto por vários módulos que são suportados por um grande ecossistema de tecnologias. . Introdução: Hadoop Ecosystem é uma plataforma ou suite que fornece vários serviços para resolver os problemas de big data, inclui projetos Apache e soluções comerciais. Existem quatro elementos principais do Hadoop: HDFS, MapReduce, YARN e Hadoop Common. A maioria das soluções são usada para complementar/dar suporte a esses elementos principais. . Todas essas ferramentas funcionam em conjunto para fornecer serviços como ingestão, análise, armazenamento e manutenção de dados etc. . A seguir estão os componentes que formam coletivamente um ecossistema Hadoop: . HDFS: Armazena os dados de forma distribuída (Hadoop Distributed File System) YARN: Negociador de recursos MapReduce: Processamento de dados baseado em programação Spark: Processamento de dados em memória In-Memory PIG, HIVE: Processamento baseado em consulta de serviços de dados HBase: Banco de dados NoSQL Mahout, Spark MLLib: Bibliotecas de Machine Learning Solar, Lucene: Serviços de pesquisa e indexação Zookeeper: Gerencia o cluster Oozie: Agendamento de Job . Sabendo esses conceitos você já esta entrando para Industria 4.0, esta preparado? Segue @arthurdamasio_ para saber mais desse mundo (em São Paulo, Brazil) https://www.instagram.com/p/CImOMqApGGs/?igshid=1nraf3ommuzd4

[prasoonroxtar-blog]

BIG DATA SERVICES

Coding Brains is the top software service provider company supporting clients capitalize on the transformational potential of Big Data.

Utilizing our business domain expertise, we assist clients in integrating Big Data into their over all IT architecture and implement the Big Data Solution to take your business to a whole new level. With the belief that companies will rely on Big Data for business decision making, our team has focused on the delivery and deployment of Big Data solutions to assist corporations with strategic decision making.

Our Services related to Big Data analytics support in analyzing the information to give you smart insights into new opportunities. Our data scientists consist of a unique approach to develop services that analyze each piece of information before making any critical business decision. Big Data Analytics Consulting & Strategy support you to opt for an appropriate technology that complements your data warehouse.

APACHE HADOOP

With the utilization of Apache Hadoop consulting, we help Business verticals to leverage the advantage of a large amount of information by organizing it in an appropriate way to gain smarter insights. We have a team of professionals who gain upper hand in Big Data technology and expert in offering integrated services across the Hadoop ecosystem of HDFS, Map Reduce, Sqoop, Oozie, and Flume.

APACHE HIVE

At Coding Brains, Our Hive development and integration services are designed to enable SQL developers to write Hive Query Language statements that are similar to standard SQL ones. Our objective to bring the familiarity of relational technology to big data processing utilizing HQL and other structures and processes of relational databases.

APACHE MAHOUT

We offer solutions related to Apache Mahout designed to make business intelligence apps. Our team specializes in developing scalable applications that meet user expectations. Focused on deriving smarter insights, we have expertise in Apache Mahout and proficiency in implementing Machine Learning algorithms.

APACHE PIG

Coding Brains offers services in Apache Pig, an open- source platform and a high-level data analysis language that is utilized to examine large data sets. Our Big Data Professionals use Pig to execute Hadoop jobs in MapReduce and Apache Spark. We help simplify the implementation of technology and navigate the complexities.

APACHE ZOOKEEPER

Our team delivers high-end services in Apache ZooKeeper, an open-source project dealing with managing configuration information, naming, as well as group solutions that are deployed on the Hadoop cluster to administer the infrastructure.

APACHE KAFKA

As a leading IT Company, We provide comprehensive services in Apache Kafka, a streaming platform. Our Big Data team expert in developing enterprise-grade streaming apps as well as for streaming data pipelines that present the opportunity to exploit information in context-rich scenarios to drive business results.

NOSQL DATABASE

Our services provide elements such as quality, responsiveness, and consistency to deliver development solutions for exceptional results. We understand your business-critical requirement and deliver NoSQL technologies that are most appropriate for your apps. We support enterprises to seize new market opportunities and strategically respond to threats by empowering better client experiences.

APACHE SPARK

Our expert team of professionals provides Apache Spark solutions to companies worldwide providing high-performance advantages and versatility. In order to stay ahead and gain the business benefit, we offer processing and analysis to support the Big Data apps utilizing options like machine learning, streaming analytics and more.

APACHE THRIFT

We support enterprises to deal with the complex situation where polyglot systems make the overall administration complicated. Exploiting Apache Thrift, we enable diverse modules of a system to cross-communicate flawlessly and integrate various aspects of the IT infrastructure.

Contact Us

[manasranjanmurlidharrana]

Mr. Manasranjan Murlidhar Rana has vast knowledge and understanding of various aspects related to Hadoop Architecture and its different components. These are MapReduce, YARN, HDFS, HBase, Pig, Flume, Hive, and Zookeeper. He even has the experience to build and maintain multiple clusters in Hadoop, like the production and development of diverse sizes and configurations.

https://www.slideserve.com/manasranjanmurlidharrana/how-mr-manasranjan-murlidhar-rana-helped-union-bank-switzerland-as-a-certified-powerpoint-ppt-presentation

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Hadoop Big data Training at RM Infotech Laxmi Nagar Delhi NCR

History:

In 2006, the hadoop project was founded by Doug Cutting, It is an open source implementations of internal systems which is now being used to manage and process massive data(s) volumes. In short,

With Hadoop , a large amount of data of all varieties is continually stored and added multiple processing and analytics frameworks rather than moving the data(s) because moving data is typical and very expensive.

What are the career prospects in Hadoop?

Do you know that in next 3 years more than half of the data in this world will move to Hadoop? No wonder McKinsey Global Institute estimates shortage of 1.7 million Big Data professionals over next 3 years.

Hadoop Market is expected to reach $99.31B by 2022 at a CAGR of 42.1% -Forbes

Average Salary of Big Data Hadoop Developers is $135k (Indeed.com salary data)

According to experts –  India alone will face a shortage of close to 2 Lac Data Scientists. Experts predict, a significant gap in job openings and professionals with expertise in big data skills. Thus, this is the right time for IT professionals to make the most of this opportunity by sharpening their big data skill set.

Who should take this course?

This course is designed for anyone who:-

wants to get into a career in Big Data

wants to analyse large amounts of unstructured data

wants to architect a big data project using Hadoop and its eco system components

Why from RM Infotech

100% Practical and Job Oriented

Experienced Training having 8+ yrs of Industry Expertise in Big Data.

Learn how to analyze large amounts of data to bring out insights

Relevant examples and cases make the learning more effective and easier

Gain hands-on knowledge through the problem solving based approach of the course along with working on a project at the end of the course

Placement Assistance

Training Certificate

Course Contents

Lectures 30 X 2 hrs. (60 hrs) Weekends. Video 13 Hours Skill level all level Languages English Includes Lifetime access Money back guarantee! Certificate of Completion * Hadoop Distributed File System * Hadoop Architecture * MapReduce & HDFS * Hadoop Eco Systems * Introduction to Pig * Introduction to Hive * Introduction to HBase * Other eco system Map * Hadoop Developer * Moving the Data into Hadoop * Moving The Data out from Hadoop * Reading and Writing the files in HDFS using java   * The Hadoop Java API for MapReduce o Mapper Class o Reducer Class o Driver Class * Writing Basic MapReduce Program In java * Understanding the MapReduce Internal Components * Hbase MapReduce Program * Hive Overview * Working with Hive * Pig Overview * Working with Pig * Sqoop Overview * Moving the Data from RDBMS to Hadoop * Moving the Data from RDBMS to Hbase * Moving the Data from RDBMS to Hive * Market Basket Algorithms * Big Data Overview * Flume Overview * Moving The Data from Web server Into Hadoop * Real Time Example in Hadoop * Apache Log viewer Analysis * Introduction In Hadoop and Hadoop Related Eco System. * Choosing Hardware For Hadoop Cluster nodes * Apache Hadoop Installation o Standalone Mode o Pseudo Distributed Mode o Fully Distributed Mode * Installing Hadoop Eco System and Integrate With Hadoop o Zookeeper Installation o Hbase Installation o Hive Installation o Pig Installation o Sqoop Installation o Installing Mahout * Horton Works Installation * Cloudera Installation * Hadoop Commands usage * Import the data in HDFS * Sample Hadoop Examples (Word count program and Population problem) * Monitoring The Hadoop Cluster o Monitoring Hadoop Cluster with Ganglia o Monitoring Hadoop Cluster with Nagios o Monitoring Hadoop Cluster with JMX * Hadoop Configuration management Tool * Hadoop Benchmarking 1. PDF Files + Hadoop e Books 2. Life time access to videos tutorials 3. Sample Resumes 4. Interview Questions 5. Complete Module & Frameworks Code

Hadoop Training Syllabus

Other materials provided along with the training

* 13 YEARS OF INDUSTRY EXPERIENCE * 9 YEARS OF EXPERIENCE IN ONLINE AND CLASSROOM TRAINING

ABOUT THE TRAINER

Duration of Training

Duration of Training will be 12 Weeks (Weekends) Saturday and Sunday 3 hrs.

Course Fee

Course Fee in 15,000/- (7,500/- X 2 installments) 2 Classes are Free as Demo. 100% Money back Guarantee if not satisfied with Training. Course Fee includes Study Materials, Videos, Software support, Lab, Tution Fee.

Batch Size

Maximum 5 candidates in a single batch.

Contact Us

To schedule Free Demo Kindly Contact :-

Parag Saxena.

RM Infotech Pvt Ltd,

332 A, Gali no - 6, West Guru Angad Nagar,

Laxmi Nagar, Delhi - 110092.

Mobile : 9810926239.

website : http://www.rminfotechsolutions.com/javamain/hadoop.html

[hadoopinstituteindia-blog]

BigData Hadoop Training

Orapro technologies is the leading provider for quality Data Analytics training . Students will learn the HDFS, Map-Reduce, HBASE,  HIVE, SQOOP, PIG, FLUME, OOZIE, and ZOOKEEPER, etc., We provide BigData Live projects.

[siva3155]

300+ TOP BIG DATA Interview Questions and Answers

BIG Data Interview Questions for freshers experienced :-

1. What is Big Data? Big Data is relative term. When Data can’t be handle using conventional systems like RDBMS because Data is generating with very high speed, it is known as Big Data. 2. Why Big Data? Since Data is growing rapidly and RDBMS can’t control it, Big Data technologies came into picture. 3. What are 3 core dimension of Big Data. Big Data have 3 core dimensions: Volume Variety Velocity 4. Role of Volume in Big Data Volume: Volume is nothing but amount of data. As Data is growing with high speed, a huge volume of data is getting generated every second. 5. Role of variety in Big Data Variety: So many applications are running nowadays like mobile, mobile sensors etc. Each application is generating data in different variety. 6. Role of Velocity in Big Data Velocity: This is speed of data getting generated. for example: Every minute, Instagram receives 46,740 new photos. So day by day speed of data generation is getting higher. 7. Remaining 2 less known dimension of Big Data There are two more V’s of Big Data. Below are less known V’s: Veracity Value 8. Role of Veracity in Big Data Veracity: Veracity is nothing but the accuracy of data. Big Data should have some accurate data in order to process it. 9. Role of Value in Big Data Value: Big Data should contain some value to us. Junk Values/Data is not considered as real Big Data. 10. What is Hadoop? Hadoop: Hadoop is a project of Apache. This is a framework which is open Source. Hadoop is use for storing Big data and then processing it.

BIG DATA Interview Questions 11. Why Hadoop? In order to process Big data, we need some framework. Hadoop is an open source framework which is owned by Apache organization. Hadoop is the basic requirement when we think about processing big data. 12. Connection between Hadoop and Big Data Big Data will be processed using some framework. This framework is known as Hadoop. 13. Hadoop and Hadoop Ecosystem Hadoop Ecosystem is nothing but a combination of various components. Below are the components which comes under Hadoop Ecosystem’s Umbrella: HDFS YARN MapReduce Pig Hive Sqoop, etc. 14. What is HDFS. HDFS: HDFS is known as Hadoop Distributed File System. Like Every System have one file system in order to see/manage files stored, in the same way Hadoop is having HDFS which works in distributed manner. 15. Why HDFS? HDFS is the core component of Hadoop Ecosystem. Since Hadoop is a distributed framework and HDFS is also distributed file system. It is very well compatible with Hadoop. 16. What is YARN YARN: YARN is known as Yet Another Resource Manager. This is a project of Apache Hadoop. 17. Use of YARN. YARN is use for managing resources. Jobs are scheduled using YARN in Apache Hadoop. 18. What is MapReduce? MapReduce: MapReduce is a programming approach which consist of two steps: Map and Reduce. MapReduce is the core of Apache Hadoop. 19. Use of MapReduce MapReduce is a programming approach to process our data. MapReduce is use to process Big Data. 20. What is Pig? This is a project of Apache. It is a platform using which huge datasets are analyzed. It runs on the top of MapReduce. 21. Use of Pig Pig is use for the purpose of analyzing huge datasets. Data flow are created using Pig in order to analyze data. Pig Latin language is use for this purpose. 22. What is Pig Latin Pig Latin is a script language which is used in Apache Pig to create Data flow in order to analyze data. 23. What is Hive? Hive is a project of Apache Hadoop. Hive is a dataware software which runs on the top of Hadoop. 24. Use of Hive Hive works as a storage layer which is used to store structured data. This is very useful and convenient tool for SQL user as Hive use HQL. 25. What is HQL? HQL is an abbreviation of Hive Query Language. This is designed for those user who are very comfortable with SQL. HQL is use to query structured data into hive. 26. What is Sqoop? Sqoop is a short form of SQL to Hadoop. This is basically a command line tool to transfer data between Hadoop and SQL and vice-versa. Q27) Use of Sqoop? Sqoop is a CLI tool which is used to migrate data between RDBMS to Hadoop and vice-versa. Q28) What are other components of Hadoop Ecosystem? Below are other components of Hadoop Ecosystem: a) HBase b) Oozie c) Zookeeper d) Flume etc. Q29) Difference Between Hadoop and HDFS Hadoop is a framework while HDFS is a file system which works on the top of Hadoop. Q30) How to access HDFS below is command: hdfs fs or hdfs dfs Q31) How to create directory in HDFS below is command: hdfs fs -mkdir Q32) How to keep files in HDFS below is command: hdfs fs -put or hdfs fs -copyfromLocal Q33) How to copy file from HDFS to local below is command: hdfs fs -copyToLocal Q34) How to Delete directory from HDFS below is command: hdfs fs -rm Q35) How to Delete file from HDFS below is command: hdfs fs -rm Become an Big Data Hadoop Certified Expert in 25Hours Q36) How to Delete directory and files recursively from HDFS below is command: hdfs fs -rm -r Q37) How to read file in HDFS below is command: hdfs fs -cat Managed/internal table Here once the table gets deleted both meta data and actual data is deleted –>external table Here once the table gets deleted only the mata data gets deleted but not the actual data. Q63) How to managed create a table in hive? hive>create table student(sname string, sid int) row format delimited fileds terminated by ‘,’; //hands on hive>describe student; Q64) How to load data into table created in hive? hive>load data local inpath /home/training/simple.txt into table student; //hands on hive> select * from student; Q65) How to create/load data into exteranal tables? *without location hive>create external table student(sname string, sid int) row format delimited fileds terminated by ‘,’; hive>load data local inpath /home/training/simple.txt into table student; *With Location hive>create external table student(sname string, sid int) row format delimited fileds terminated by ‘,’ location /Besant_HDFS; Here no need of load command Became an Big Data Hadoop Expert with Certification in 25hours Q66) Write a command to write static partitioned table. hive>create table student(sname string, sid int) partitioned by(int year) row format delimited fileds terminated by ‘,’; Q67) How to load a file in static partition? hive>load data local inpath /home/training/simple2018.txt into table student partition(year=2018); Q68) Write a commands to write dynamic partitioned table. Answer: –> create a normal table hive>create table student(sname string, sid int) row format delimited fileds terminated by ‘,’; –>load data hive>load data local inpath /home/training/studnetall.txt into table student ; –>create a partitioned table hive>create table student_partition(sname string, sid int) partitioned by(int year) row format delimited fileds terminated by ‘,’; –>set partitions hive>set hive.exec.dynamic.partition.mode = nonstrict; –>insert data hive>insert into table student_partition select * from student; –>drop normal table hive>drop table student; Q69) What is pig? Answer:Pig is an abstraction over map reduce. It is a tool used to deal with huge amount of structured and semi structed data. Q70) What is atom in pig? its a small piece of data or a filed eg: ‘shilpa’ Q71) What is tuple? ordered set of filed (shilpa, 100) Q72) Bag in pig? un-ordered set of tuples eg.{(sh,1),(ww,ww)} Q73) What is relation? bag of tuples Q74) What is hbase? its a distributed column oriented database built on top of hadoop file system it is horizontally scalable Q75) Difference between hbase and rdbms RDMBS is schema based hbase is not RDMBS only structured data hbase structured and semi structured data. RDMBS involves transactions Hbase no transactions Q76) What is table in hbase? collection of rows Q77) What is row in hbase? collection of column families Q78) Column family in hbase? Answer:collection of columns Q79) What is column? Answer:collection of key value pair Q80) How to start hbase services? Answer: >hbase shell hbase>start -hbase.sh Q81) DDL commands used in hbase? Answer: create alter drop drop_all exists list enable is_enabled? disable is_disbled? Q82) DML commands? Answer: put get scan delete delete_all Q83) What services run after running hbase job? Answer: Name node data node secondary NN JT TT Hmaster HRegionServer HQuorumPeer Q84) How to create table in hbase? Answer:>create ’emp’, ‘cf1′,’cf2’ Q85) How to list elements Answer:>scan ’emp’ Q86) Scope operators used in hbase? Answer: MAX_FILESIZE READONLY MEMSTORE_FLUSHSIZE DEFERRED_LOG_FLUSH Q87) What is sqoop? sqoop is an interface/tool between RDBMS and HDFS to importa nd export data Q88) How many default mappers in sqoop? 4 Q89) What is map reduce? map reduce is a data processing technique for distributed computng base on java map stage reduce stage Q90) list few componets that are using big data Answer: facebook adobe yahoo twitter ebay Q91) Write a quert to import a file in sqoop $>sqoop-import –connect jdbc:mysql://localhost/Besant username hadoop password hadoop table emp target_dir sqp_dir fields_terminated_by ‘,’ m 1 Q92) What is context in map reduce? it is an object having the information about hadoop configuration Q93) How job is started in map reduce? To start a job we need to create a configuration object. configuration c = new configuration(); Job j = new Job(c,”wordcount calculation); Q94) How to load data in pig? A= load ‘/home/training/simple.txt’ using PigStorage ‘|’ as (sname : chararray, sid: int, address:chararray); Q95) What are the 2 modes used to run pig scripts? local mode pig -x local pig -x mapreduce Q96) How to show up details in pig ? dump command is used. grunt>dump A; Q97) How to fetch perticular columns in pig? B = foreach A generate sname, sid; Q100) How to restrict the number of lines to be printed in pig ? c=limit B 2; Get Big Data Hadoop Online Training Q101) Define Big Data Big Data is defined as a collection of large and complex of unstructured data sets from where insights are derived from the Data Analysis using open-source tools like Hadoop. Q102) Explain The Five Vs of Big Data The five Vs of Big Data are – Volume – Amount of data in the Petabytes and Exabytes Variety – Includes formats like an videos, audio sources, textual data, etc. Velocity – Everyday data growth which are includes conversations in forums,blogs,social media posts,etc. Veracity – Degree of accuracy of data are available Value – Deriving insights from collected data to the achieve business milestones and new heights Q103) How is Hadoop related to the Big Data ? Describe its components? Apache Hadoop is an open-source framework used for the storing, processing, and analyzing complex unstructured data sets for the deriving insights and actionable intelligence for businesses. The three main components of Hadoop are- MapReduce – A programming model which processes large datasets in the parallel HDFS – A Java-based distributed file system used for the data storage without prior organization YARN – A framework that manages resources and handles requests from the distributed applications Q104) Define HDFS and talk about their respective components? The Hadoop Distributed File System (HDFS) is the storage unit that’s responsible for the storing different types of the data blocks in the distributed environment. The two main components of HDFS are- NameNode – A master node that processes of metadata information for the data blocks contained in the HDFS DataNode – Nodes which act as slave nodes and a simply store the data, for use and then processing by the NameNode. Q105) Define YARN, and talk about their respective components? The Yet Another Resource Negotiator (YARN) is the processing component of the Apache Hadoop and is responsible for managing resources and providing an execution environment for said of processes. The two main components of YARN are- ResourceManager– Receives processing requests and allocates its parts to the respective Node Managers based on processing needs. Node Manager– Executes tasks on the every single Data Node Q106) Explain the term ‘Commodity Hardware? Commodity Hardware refers to hardware and components, collectively needed, to run the Apache Hadoop framework and related to the data management tools. Apache Hadoop requires 64-512 GB of the RAM to execute tasks, and any hardware that supports its minimum for the requirements is known as ‘Commodity Hardware. Q107) Define the Port Numbers for NameNode, Task Tracker and Job Tracker? Name Node – Port 50070 Task Tracker – Port 50060 Job Tracker – Port 50030 Q108) How does HDFS Index Data blocks? Explain. HDFS indexes data blocks based on the their respective sizes. The end of data block points to address of where the next chunk of data blocks get a stored. The DataNodes store the blocks of datawhile the NameNode manages these data blocks by using an in-memory image of all the files of said of data blocks. Clients receive for the information related to data blocked from the NameNode. 109. What are Edge Nodes in Hadoop? Edge nodes are gateway nodes in the Hadoop which act as the interface between the Hadoop cluster and external network.They run client applications and cluster administration tools in the Hadoop and are used as staging areas for the data transfers to the Hadoop cluster. Enterprise-class storage capabilities (like 900GB SAS Drives with Raid HDD Controllers) is required for the Edge Nodes,and asingle edge node for usually suffices for multiple of Hadoop clusters. Q110) What are some of the data management tools used with the Edge Nodes in Hadoop? Oozie,Ambari,Hue,Pig and Flume are the most common of data management tools that work with edge nodes in the Hadoop. Other similar tools include to HCatalog,BigTop and Avro. Q111) Explain the core methods of a Reducer? There are three core methods of a reducer. They are- setup() – Configures different to parameters like distributed cache, heap size, and input data. reduce() – A parameter that is called once per key with the concerned on reduce task cleanup() – Clears all temporary for files and called only at the end of on reducer task. Q112) Talk about the different tombstone markers used for deletion purposes in HBase.? There are three main tombstone markers used for the deletion in HBase. They are- Family Delete Marker – Marks all the columns of an column family Version Delete Marker – Marks a single version of an single column Column Delete Marker– Marks all the versions of an single column Q113) How would you transform unstructured data into structured data? How to Approach: Unstructured data is the very common in big data. The unstructured data should be transformed into the structured data to ensure proper data are analysis. Q114) Which hardware configuration is most beneficial for Hadoop jobs? Dual processors or core machines with an configuration of 4 / 8 GB RAM and ECC memory is ideal for running Hadoop operations. However, the hardware is configuration varies based on the project-specific workflow and process of the flow and need to the customization an accordingly. Q115) What is the use of the Record Reader in Hadoop? Since Hadoop splits data into the various blocks, RecordReader is used to read the slit data into the single record. For instance, if our input data is the split like: Row1: Welcome to Row2: Besant It will be read as the “Welcome to Besant” using RecordReader. Q116) What is Sequencefilein put format? Hadoop uses the specific file format which is known as the Sequence file. The sequence file stores data in the serialized key-value pair. Sequencefileinputformat is an input format to the read sequence files. Q117) What happens when two users try to access to the same file in HDFS? HDFS NameNode supports exclusive on write only. Hence, only the first user will receive to the grant for the file access & second that user will be rejected. Q118) How to recover an NameNode when it’s are down? The following steps need to execute to the make the Hadoop cluster up and running: Use the FsImage which is file system for metadata replicate to start an new NameNode. Configure for the DataNodes and also the clients to make them acknowledge to the newly started NameNode. Once the new NameNode completes loading to the last for checkpoint FsImage which is the received to enough block reports are the DataNodes, it will start to serve the client. In case of large of Hadoop clusters, the NameNode recovery process to consumes a lot of time which turns out to be an more significant challenge in case of the routine maintenance. Q119) What do you understand by the Rack Awareness in Hadoop? It is an algorithm applied to the NameNode to decide then how blocks and its replicas are placed. Depending on the rack definitions network traffic is minimized between DataNodes within the same of rack. For example, if we consider to replication factor as 3, two copies will be placed on the one rack whereas the third copy in a separate rack. Q120) What are the difference between of the “HDFS Block” and “Input Split”? The HDFS divides the input data physically into the blocks for processing which is known as the HDFS Block. Input Split is a logical division of data by the mapper for mapping operation. Q121) DFS can handle a large volume of data then why do we need Hadoop framework? Hadoop is not only for the storing large data but also to process those big data. Though DFS (Distributed File System) tool can be store the data, but it lacks below features- It is not fault for tolerant Data movement over the network depends on bandwidth. Q122) What are the common input formats are Hadoop? Text Input Format – The default input format defined in the Hadoop is the Text Input Format. Sequence File Input Format – To read files in the sequence, Sequence File Input Format is used. Key Value Input Format – The input format used for the plain text files (files broken into lines) is the Key Value for Input Format. Q123) Explain some important features of Hadoop? Hadoop supports are the storage and processing of big data. It is the best solution for the handling big data challenges. Some of important features of Hadoop are 1. Open Source – Hadoop is an open source framework which means it is available free of cost Also,the users are allowed to the change the source code as per their requirements. 2. Distributed Processing – Hadoop supports distributed processing of the data i.e. faster processing. The data in Hadoop HDFS is stored in the distributed manner and MapReduce is responsible for the parallel processing of data. 3. Fault Tolerance – Hadoop is the highly fault-tolerant. It creates three replicas for each block at different nodes, by the default. This number can be changed in according to the requirement. So, we can recover the data from the another node if one node fails. The detection of node of failure and recovery of data is done automatically. 4. Reliability – Hadoop stores data on the cluster in an reliable manner that is independent of the machine. So, the data stored in Hadoop environment is not affected by the failure of machine. 5. Scalability – Another important feature of Hadoop is the scalability. It is compatible with the other hardware and we can easily as the new hardware to the nodes. 6. High Availability – The data stored in Hadoop is available to the access even after the hardware failure. In case of hardware failure, the data can be accessed from the another path. Q124) Explain the different modes are which Hadoop run? Apache Hadoop runs are the following three modes – Standalone (Local) Mode – By default, Hadoop runs in the local mode i.e. on a non-distributed,single node. This mode use for the local file system to the perform input and output operation. This mode does not support the use of the HDFS, so it is used for debugging. No custom to configuration is needed for the configuration files in this mode. In the pseudo-distributed mode, Hadoop runs on a single of node just like the Standalone mode. In this mode, each daemon runs in the separate Java process. As all the daemons run on the single node, there is the same node for the both Master and Slave nodes. Fully – Distributed Mode – In the fully-distributed mode, all the daemons run on the separate individual nodes and thus the forms a multi-node cluster. There are different nodes for the Master and Slave nodes. Q125) What is the use of jps command in Hadoop? The jps command is used to the check if the Hadoop daemons are running properly or not. This command shows all the daemons running on the machine i.e. Datanode, Namenode, NodeManager, ResourceManager etc. Q126) What are the configuration parameters in the “MapReduce” program? The main configuration parameters in “MapReduce” framework are: Input locations of Jobs in the distributed for file system Output location of Jobs in the distributed for file system The input format of data The output format of data The class which contains for the map function The class which contains for the reduce function JAR file which contains for the mapper, reducer and the driver classes Q127) What is a block in HDFS? what is the default size in Hadoop 1 and Hadoop 2? Can we change the block size? Blocks are smallest continuous of data storage in a hard drive. For HDFS, blocks are stored across Hadoop cluster. The default block size in the Hadoop 1 is: 64 MB The default block size in the Hadoop 2 is: 128 MB Yes,we can change block size by using the parameters – dfs.block.size located in the hdfs-site.xml file. Q128) What is Distributed Cache in the MapReduce Framework? Distributed Cache is an feature of the Hadoop MapReduce framework to cache files for the applications. Hadoop framework makes cached files for available for every map/reduce tasks running on the data nodes. Hence, the data files can be access the cache file as the local file in the designated job. Q129) What are the three running modes of the Hadoop? The three running modes of the Hadoop are as follows: Standalone or local: This is the default mode and doesn’t need any configuration. In this mode, all the following components for Hadoop uses local file system and runs on single JVM – NameNode DataNode ResourceManager NodeManager Pseudo-distributed: In this mode, all the master and slave Hadoop services is deployed and executed on a single node. Fully distributed: In this mode, Hadoop master and slave services is deployed and executed on the separate nodes. Q130) Explain JobTracker in Hadoop? JobTracker is a JVM process in the Hadoop to submit and track MapReduce jobs. JobTracker performs for the following activities in Hadoop in a sequence – JobTracker receives jobs that an client application submits to the job tracker JobTracker notifies NameNode to determine data node JobTracker allocates TaskTracker nodes based on the available slots. It submits the work on the allocated TaskTracker Nodes, JobTracker monitors on the TaskTracker nodes. Q131) What are the difference configuration files in Hadoop? The different configuration files in Hadoop are – core-site.xml – This configuration file of contains Hadoop core configuration settings, for example, I/O settings, very common for the MapReduce and HDFS. It uses hostname an port. mapred-site.xml – This configuration file specifies a framework name for MapReduce by the setting mapreduce.framework.name hdfs-site.xml – This configuration file contains of HDFS daemons configuration for settings. It also specifies default block for permission and replication checking on HDFS. yarn-site.xml – This configuration of file specifies configuration settings for the ResourceManager and NodeManager. Q132) What are the difference between Hadoop 2 and Hadoop 3? Following are the difference between Hadoop 2 and Hadoop 3 – Kerberos are used to the achieve security in Hadoop. There are 3 steps to access an service while using Kerberos, at a high level. Each step for involves a message exchange with an server. Authentication – The first step involves authentication of the client to authentication server, and then provides an time-stamped TGT (Ticket-Granting Ticket) to the client. Authorization – In this step, the client uses to received TGT to request a service ticket from the TGS (Ticket Granting Server) Service Request – It is the final step to the achieve security in Hadoop. Then the client uses to service ticket to authenticate an himself to the server. Q133) What is commodity hardware? Commodity hardware is an low-cost system identified by the less-availability and low-quality. The commodity hardware for comprises of RAM as it performs an number of services that require to RAM for the execution. One doesn’t require high-end hardware of configuration or super computers to run of Hadoop, it can be run on any of commodity hardware. Q134) How is NFS different from HDFS? There are a number of the distributed file systems that work in their own way. NFS (Network File System) is one of the oldest and popular distributed file an storage systems whereas HDFS (Hadoop Distributed File System) is the recently used and popular one to the handle big data. Q135) How do Hadoop MapReduce works? There are two phases of the MapReduce operation. Map phase – In this phase, the input data is split by the map tasks. The map tasks run in the parallel. These split data is used for analysis for purpose. Reduce phase – In this phase, the similar split data is the aggregated from the entire to collection and shows the result. Q136) What is MapReduce? What are the syntax you use to run a MapReduce program? MapReduce is a programming model in the Hadoop for processing large data sets over an cluster of the computers, commonly known as the HDFS. It is a parallel to programming model. The syntax to run a MapReduce program is the hadoop_jar_file.jar /input_path /output_path. Q137) What are the different file permissions in the HDFS for files or directory levels? Hadoop distributed file system (HDFS) uses an specific permissions model for files and directories. 1. Following user levels are used in HDFS – Owner Group Others. 2. For each of the user on mentioned above following permissions are applicable – read (r) write (w) execute(x). 3. Above mentioned permissions work on differently for files and directories. For files The r permission is for reading an file The w permission is for writing an file. For directories The r permission lists the contents of the specific directory. The w permission creates or deletes the directory. The X permission is for accessing the child directory. Q138) What are the basic parameters of a Mapper? The basic parameters of a Mapper is the LongWritable and Text and Int Writable Q139) How to restart all the daemons in Hadoop? To restart all the daemons, it is required to the stop all the daemons first. The Hadoop directory contains sbin as directory that stores to the script files to stop and start daemons in the Hadoop. Use stop daemons command /sbin/stop-all.sh to the stop all the daemons and then use /sin/start-all.sh command to start all the daemons again. Q140) Explain the process that overwrites the replication factors in HDFS? There are two methods to the overwrite the replication factors in HDFS – Method 1: On File Basis In this method, the replication factor is the changed on the basis of file using to Hadoop FS shell. The command used for this is: $hadoop fs – setrep –w2/my/test_file Here, test_file is the filename that’s replication to factor will be set to 2. Method 2: On Directory Basis In this method, the replication factor is changed on the directory basis i.e. the replication factor for all the files under the given directory is modified. $hadoop fs –setrep –w5/my/test_dir Here, test_dir is the name of the directory, then replication factor for the directory and all the files in it will be set to 5. Q141) What will happen with a NameNode that doesn’t have any data? A NameNode without any for data doesn’t exist in Hadoop. If there is an NameNode, it will contain the some data in it or it won’t exist. Q142) Explain NameNode recovery process? The NameNode recovery process involves to the below-mentioned steps to make for Hadoop cluster running: In the first step in the recovery process, file system metadata to replica (FsImage) starts a new NameNode. The next step is to configure DataNodes and Clients. These DataNodes and Clients will then acknowledge of new NameNode. During the final step, the new NameNode starts serving to the client on the completion of last checkpoint FsImage for loading and receiving block reports from the DataNodes. Note: Don’t forget to mention, this NameNode recovery to process consumes an lot of time on large Hadoop clusters. Thus, it makes routine maintenance to difficult. For this reason, HDFS high availability architecture is recommended to use. Q143) How Is Hadoop CLASSPATH essential to start or stop Hadoop daemons? CLASSPATH includes necessary directories that contain the jar files to start or stop Hadoop daemons. Hence, setting the CLASSPATH is essential to start or stop on Hadoop daemons. However, setting up CLASSPATH every time its not the standard that we follow. Usually CLASSPATH is the written inside /etc/hadoop/hadoop-env.sh file. Hence, once we run to Hadoop, it will load the CLASSPATH is automatically. Q144) Why is HDFS only suitable for large data sets and not the correct tool to use for many small files? This is due to the performance issue of the NameNode.Usually, NameNode is allocated with the huge space to store metadata for the large-scale files. The metadata is supposed to be an from a single file for the optimum space utilization and cost benefit. In case of the small size files, NameNode does not utilize to the entire space which is a performance optimization for the issue. Q145) Why do we need Data Locality in Hadoop? Datasets in HDFS store as the blocks in DataNodes the Hadoop cluster. During the execution of the MapReducejob the individual Mapper processes to the blocks (Input Splits). If the data does not reside in the same node where the Mapper is the executing the job, the data needs to be copied from DataNode over the network to mapper DataNode. Now if an MapReduce job has more than 100 Mapper and each Mapper tries to copy the data from the other DataNode in cluster simultaneously, it would cause to serious network congestion which is an big performance issue of the overall for system. Hence, data proximity are the computation is an effective and cost-effective solution which is the technically termed as Data locality in the Hadoop. It helps to increase the overall throughput for the system. Enroll Now! Q146) What’s Big Big Data or Hooda? Only a concept that facilitates handling large data databases. Hadoop has a single framework for dozens of tools. Hadoop is primarily used for block processing. The difference between Hadoop, the largest data and open source software, is a unique and basic one. Q147) Big data is a good life? Analysts are increasing demand for industry and large data buildings. Today, many people are looking to pursue their large data industry by having great data jobs like freshers. However, the larger data itself is just a huge field, so it’s just Hadoop jobs for freshers Q148) What is the great life analysis of large data analysis? The large data analytics has the highest value for any company, allowing it to make known decisions and give the edge among the competitors. A larger data career increases the opportunity to make a crucial decision for a career move. Q149) Hope is a NoSQL? Hadoop is not a type of database, but software software that allows software for computer software. It is an application of some types, which distributes noSQL databases (such as HBase), allowing thousands of servers to provide data in lower performance to the rankings Q150) Need Hodop to Science? Data scientists have many technical skills such as Hadoto, NoSQL, Python, Spark, R, Java and more. … For some people, data scientist must have the ability to manage using Hoodab alongside a good skill to run statistics against data set. Q151)What is the difference between large data and large data analysis? On the other hand, data analytics analyzes structured or structured data. Although they have a similar sound, there are no goals. … Great data is a term of very large or complex data sets that are not enough for traditional data processing applications Q152) Why should you be a data inspector? A data inspector’s task role involves analyzing data collection and using various statistical techniques. … When a data inspector interviewed for the job role, the candidates must do everything they can to see their communication skills, analytical skills and problem solving skills Q153) Great Data Future? Big data refers to the very large and complex data sets for traditional data entry and data management applications. … Data sets continue to grow and applications are becoming more and more time-consuming, with large data and large dataprocessing cloud moving more Q154) What is a data scientist on Facebook? This assessment is provided by 85 Facebook data scientist salary report (s) employees or based on statistical methods. When a factor in bonus and extra compensation, a data scientist on Facebook expected an average of $ 143,000 in salary Q155) Can Hedop Transfer? HODOOP is not just enough to replace RDGMS, but it is not really what you want to do. … Although it has many advantages to the source data fields, Hadoopcannot (and usually does) replace a data warehouse. When associated with related databases. However, this creates a powerful and versatile solution. Get Big Data Hadoop Course Now! Q156) What’s happening in Hadoop? MapReduce is widely used in I / O forms, a sequence file is a flat file containing binary key / value pairs. Graphical publications are stored locally in sequencer. It provides Reader, Writer and Seater classes. The three series file formats are: Non-stick key / value logs. Record key / value records are compressed – only ‘values’ are compressed here. Pressing keys / value records – ‘Volumes’ are collected separately and shortened by keys and values. The ‘volume’ size can be configured. Q157) What is the Work Tracker role in Huda? The task tracker’s primary function, resource management (managing work supervisors), resource availability and monitoring of the work cycle (monitoring of docs improvement and wrong tolerance). This is a process that runs on a separate terminal, not often in a data connection. The tracker communicates with the label to identify the location of the data. The best mission to run tasks at the given nodes is to find the tracker nodes. Track personal work trackers and submit the overall job back to the customer. MapReduce works loads from the slush terminal. Q158) What is the RecordReader application in Hutch? Since the Hadoop data separates various blocks, recordReader is used to read split data in a single version. For example, if our input data is broken: Row1: Welcome Row2: Intellipaat It uses “Welcome to Intellipaat” using RecordReader. Q159)What is Special Execution in Hooda? A range of Hadoop, some sloping nodes, are available to the program by distributing tasks at many ends. Tehre is a variety of causes because the tasks are slow, which are sometimes easier to detect. Instead of identifying and repairing slow-paced tasks, Hopep is trying to find out more slowly than he expected, then backs up the other equivalent task. Hadoop is the insulation of this backup machine spectrum. This creates a simulated task on another disk. You can activate the same input multiple times in parallel. After most work in a job, the rest of the functions that are free for the time available are the remaining jobs (slowly) copy copy of the splash execution system. When these tasks end, it is reported to JobTracker. If other copies are encouraging, Hudhoft dismays the tasktakers and dismiss the output. Hoodab is a normal natural process. To disable, set mapred.map.tasks.speculative.execution and mapred.reduce.tasks.speculative.execution Invalid job options Q160) What happens if you run a hood job? It will throw an exception that the output file directory already exists. To run MapReduce task, you need to make sure you do not have a pre-release directory in HDFS. To delete the directory before you can work, you can use the shell: Hadoop fs -rmr / path / to / your / output / or via Java API: FileSystem.getlocal (conf) .delete (outputDir, true); Get Hadoop Course Now! Q161) How can you adjust the Hadoopo code? Answer: First, check the list of currently running MapReduce jobs. Next, we need to see the orphanage running; If yes, then you have to determine the location of the RM records. Run: “ps -ef | grep -I ResourceManager” And search result log in result displayed. Check the job-id from the displayed list and check if there is any error message associated with the job. Based on RM records, identify the employee tip involved in executing the task. Now, log on to that end and run – “ps -ef | grep -iNodeManager” Check the tip manager registration. Major errors reduce work from user level posts for each diagram. Q162) How should the reflection factor in FFAS be constructed? Answer: Hdfs-site.xml is used to configure HDFS. Changing the dfs.replication property on hdfs-site.xml will change the default response to all files in HDFS. You can change the reflection factor based on a file you are using Hadoop FS shell: $ hadoopfs -setrep -w 3 / n / fileConversely, You can also change the reflection factors of all the files under a single file. $ hadoopfs-setrep -w 3 -R / my / dir Now go through the Hadoop administrative practice to learn about the reflection factor in HDFS! Q163) How to control the release of the mapper, but does the release issue not? Answer: To achieve this summary, you must set: conf.set (“mapreduce.map.output.compress”, true) conf.set (“mapreduce.output.fileoutputformat.compress”, incorrect) Q164) Which companies use a hoop? Learn how Big Data and HADOOP have changed the rules of the game in this blog post. Yahoo (the largest contribution to the creation of the hawkop) – Yahoo search engine created for Hadoop, Facebook – Analytics, Amazon, Netflix, Adobe, Ebay, Spadys, Twitter, Adobe. Q165) Do I have to know Java to learn the habit? The ability of MapReduce in Java is an additional plus but not needed. … learn the Hadoop and create an excellent business with Hadoo, knowing basic basic knowledge of Linux and Java Basic Programming Policies Q166) What should you consider when using the second name line? Secondary mode should always be used on a separate separate computer. This prevents intermittent interaction with the mainstream. Q167) Name the Hadoop code as executable modes. There are various methods to run the Hadoop code – Fully distributed method Pseudosiphrit method Complete mode Q168)Name the operating system supported by the hadoop operation. Linux is the main operating system. However, it is also used as an electric power Windows operating system with some additional software. Q169) HDFS is used for applications with large data sets, not why Many small files? HDFS is more efficient for a large number of data sets, maintained in a file Compared to smaller particles of data stored in multiple files. Saving NameNode The file system metadata in RAM, the amount of memory that defines the number of files in the HDFS file System. In simpler terms, more files will generate more metadata, which means more Memory (RAM). It is recommended that you take 150 bytes of a block, file or directory metadata. Q170) What are the main features of hdfssite.xml? There are three important properties of hdfssite.xml: data.dr – Identify the location of the data storage. name.dr – Specify the location of the metadata storage and specify the DFS is located On disk or remote location. checkpoint.dir – for the second name name. Q171) What are the essential hooping tools that improve performance? Big data? Some of the essential hoopoe tools that enhance large data performance – Hive, HDFS, HBase, Avro, SQL, NoSQL, Oozie, Clouds, Flume, SolrSee / Lucene, and ZooKeeper Q172) What do you know about Fillil soon? The sequence is defined as a flat file containing the binary key or value pairs. This is important Used in MapReduce’s input / output format. Graphical publications are stored locally SequenceFile. Several forms of sequence – Summary of record key / value records – In this format, the values are compressed. Block compressed key / value records – In this format, the values and keys are individually The blocks are stored and then shortened. Sticky Key / Value Entries – In this format, there are no values or keys. Get 100% Placement Oriented Training in Hadoop! Q173) Explain the work tracker’s functions. In Hadoop, the work tracker’s performers perform various functions, such as – It manages resources, manages resources and manages life cycle Tasks. It is responsible for finding the location of the data by contacting the name Node. It performs tasks at the given nodes by finding the best worker tracker. Work Tracker Manages to monitor all task audits individually and then submit The overall job for the customer. It is responsible for supervising local servicemen from Macpute’s workplace Node. Q174) The FASAL is different from NAS? The following points distinguish HDFS from NAS – Hadoop shared file system (HDFS) is a distributed file system that uses data Network Attached Storage (NAS) is a file-wide server Data storage is connected to the computer network. HDFS distributes all databases in a distributed manner As a cluster, NAS saves data on dedicated hardware. HDFS makes it invaluable when using NAS using materials hardware Data stored on highhend devices that include high spending The HDFS work with MapReduce diagram does not work with MapReduce Data and calculation are stored separately. Q175)Does the HDFS go wrong? If so, how? Yes, HDFS is very mistaken. Whenever some data is stored in HDFS, name it Copying data (copies) to multiple databases. Normal reflection factor is 3. It needs to be changed according to your needs. If DataNode goes down, NameNode will take Copies the data from copies and copies it to another node, thus making the data available automatically. TheThe way, as the HDFS is the wrong tolerance feature and the fault tolerance Q176) Distinguish HDFS Block and Input Unit. The main difference between HDFS Block and Input Split is HDFS Black. While the precise section refers to the input sector, the business section of the data is knownData. For processing, HDFS first divides the data into blocks, and then stores all the packages Together, when MapReduce divides the data into the first input section then allocate this input and divide it Mapper function. Q177) What happens when two clients try to access the same file on HDFS? Remember that HDFS supports specific characters Only at a time). NName client nameNode is the nameNode that gives the name Node Lease the client to create this file. When the second client sends the request to open the same file To write, the lease for those files is already supplied to another customer, and the name of the name Reject second customer request. Q178) What is the module in HDFS? The location for a hard drive or a hard drive to store data As the volume. Store data blocks in HDFS, and then distributed via the hoodo cluster. The entire file is divided into the first blocks and stored as separate units. Q179) What is Apache? YARN still has another resource negotiation. This is a hoodup cluster Management system. It is also the next generation introduced by MapReduce and Hoodab 2 Account Management and Housing Management Resource Management. It helps to further support the hoodoop Different processing approaches and wide-ranging applications. Q180) What is the terminal manager? Node Manager is TARStracker’s YARN equivalent. It takes steps from it Manages resourceManager and single-source resources. This is the responsibility Containers and ResourceManager monitor and report their resource usage. Each Single container processes operated at slavery pad are initially provided, monitored and tracked By the tip manager associated with the slave terminal. Q181) What is the recording of the Hope? In Hadoop, RecordReader is used to read a single log split data. This is important Combining data, Hatopo divides data into various editions. For example, if input data is separated Row1: Welcome Line 2: The Hoodah’s World Using RecordReader, it should be read as “Welcome to the Hope World”. Q182) Shorten up the mappers do not affect the Output release? Answer: In order to minimize the output of the maple, the output will not be affected and set as follows: Conf.set (“mapreduce.map.output.compress”, true) Conf.set (“mapreduce.output.fileoutputformat.compress”, incorrect) Q183) A Reducer explain different methods. Answer: Various methods of a Reducer include: System () – It is used to configure various parameters such as input data size. Syntax: general vacuum system (environment) Cleaning () – It is used to clean all temporary files at the end of the task. Syntax: General Vacuum Cleanup (Eco) Reduce () – This method is known in the heart of Rezar. This is used once A key to the underlying work involved. Syntax: reduce general void (key, value, environment) Q184) How can you configure the response factor in the HDFL? For the configuration of HDFS, the hdfssite.xml file is used. Change the default value The reflection factor for all the files stored in HDFS is transferred to the following asset hdfssite.xml dfs.replication Q185) What is the use of the “jps” command? The “Jps” command is used to verify that the Hadoop daemons state is running. TheList all hadoop domains running in the command line. Namenode, nodemanager, resource manager, data node etc Q186) What is the next step after Mapper or Mumpask?: The output of the map is sorted and the partitions for the output will be created. The number of partitions depends on the number of disadvantages. Q187) How do we go to the main control for a certain reduction? Any Reducer can control the keys (through which posts) by activating the custom partition. Q188) What is the use of the coordinator? It can be specified by Job.setCombinerClass (ClassName) to make local integration with a custom component or class, and intermediate outputs, which helps reduce the size of the transfers from the Mapper to Reducer. Q189) How many maps are there in specific jobs? The number of maps is usually driven by total inputs, that is, the total volume of input files. Usually it has a node for 10-100 maps. The work system takes some time, so it is best to take at least a minute to run maps. If you expect 10TB input data and have a 128MB volume, you will end up with 82,000 maps, which you can control the volume of the mapreduce.job.maps parameter (this only provides a note structure). In the end, the number of tasks are limited by the number of divisions returned by the InputFormat.getSplits () over time (you can overwrite). Q190) What is the use of defect? Reducer reduces the set of intermediate values, which shares one key (usually smaller) values. The number of job cuts is set by Job.setNumReduceTasks (int). Q191) Explain Core modalities of deficiency? The Reducer API is similar to a Mapper, a run () method, which modes the structure of the work and the reconfiguration of the reconfiguration framework from reuse. Run () method once (), minimize each key associated with the task to reduce (once), and finally clean up the system. Each of these methods can be accessed using the context structure of the task using Context.getConfiguration (). As for the mapper type, these methods may be violated with any or all custom processes. If none of these methods are violated, the default reduction action is a symbolic function; Values go further without processing. Reducer heart is its reduction (method). This is called a one-time one; The second argument is Iterable, which provides all the key related values. Q192) What are the early stages of deficiency? Shake, sort and lower. 193) Shuffle’s explanation? Reducer is a sorted output of input mappers. At this point, the configuration receives a partition associated with the output of all the mappers via HTTP. 194) Explain the Reducer’s Line Stage? Structured groups at this point are Reducer entries with the keys (because different movers may have the same key output). Mixed and sequence phases occur simultaneously; They are combined when drawing graphic outputs (which are similar to the one-sequence). 195) Explain Criticism? At this point the reduction (MapOutKeyType, Iterable, environment) method is grouped into groups for each group. Reduction work output is typically written to FileSystem via Context.write (ReduceOutKeyType, ReduceOutValType). Applications can use application progress status, set up application level status messages, counters can update, or mark their existence. Reducer output is not sorted. Big Data Questions and Answers Pdf Download Read the full article

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Data Integration QA/ETL

Position Title : Data Integration QA/ETL Analyst Location: Ft. Myers, FL Duration: 12+ months  Term Contract Skype hire The Data Integration QA Analyst is mainly responsible for integration testing of data throughout the big data platform implementation. This includes, but is not limited to, ?end-to-end? testing of data from feeds through various databases, creating test feeds and test data for entire QA team, data feed ingestion, data integration testing, data analysis, and case creation logic testing. Additionally, functional testing and associated activities are required of this position. Candidate must be experienced with Hadoop filesystems   This position will require interaction with database operations, as well as coordination of test data integration between internal QA, Development and other technical and non-technical personnel  Requirements 5+ Years of Quality Assurance Experience Must have independently created complete test strategies, test cases, and defect reports. Bachelor?s degree, preferably in the technical area Strong understanding of data models, structures, and ETL/ELT tools 3+ Years of working experience and knowledge of SQL / Oracle / Big Data platform Experienced writing queries in both Oracle and Microsoft SQL Experienced in MapR or Hortonworks or Cloudera Big Data frameworks Experienced with Hadoop filesystems Some Big Data tool knowledge (I.E. HDFS, MapReduce, YARN, Sqoop, Flume, Hive-Beeline, Impala, Tez, Pig, Zookeeper, Oozie, Solr, Sentry, Kerberos, HBASE, Centrify DC, Falcon, Hue, Kafka, sqoop, Drill, and Storm) is a plus. Ability to navigate in a Linux environment. Ability to script in Linux is a plus. Testing experience in a data warehouse, data analysis, ETL/ELT or combination Experience in creating comprehensive, end-to-end, integration test cases based on requirements. Experience in testing ETL/Batch jobs for data ingestion and transformation.  Functional testing experience with visualization tools (Tableau, Power BI, TOAD, SQL Developer etc.) Retail experience is a plus     -- Reference : Data Integration QA/ETL jobs Source: http://jobrealtime.com/jobs/technology/data-integration-qaetl_i6440