momoTe whakangungu i te akomanga
RĒHITA
he nui te tohu tiwhikete raraunga raraunga

Nga Tohu Whakaaturanga Hatoop Raraunga Nui

Overview

Te Kairongo me nga Whakaritenga

Whakaritenga Whakangungu

Apiti me nga utu

Tiwhikete

Rarangi Whakaatu Tiwhikete Hatoop Nui

He kaupapa whakangungu Hadoop Big Data kua hangaia e nga tohunga ahumahi e whai whakaaro ana ki nga whakaritenga mahi ahumahi o naianei ki te whakarato i nga akoako-nui ki nga raraunga nui me te Hadoop Modules. Koinei te ahumahi e mohiotia ana ko te akoranga whakangungu tiwhikete Raraunga Nui kei roto i nga akoranga whakangungu i te kaiwhakawhanake Hadoop, te kaiwhakahaere Hadoop, te whakamatautau Hadoop, me nga taapataka. Tenei Cloudera Ka whakangungu a Hadoop ki a koe ki te whakakore i te tiwhikete raraunga nui.

whāinga

  • Kaupapa Matua o Hadoop 2.7 me YARN me te tuhituhi i nga tono e whakamahi ana ia ratou
  • Te whakatu i te Node Kore me te huinga huinga maha i runga i Amazon EC2
  • Kaupapa HDFS, MapReduce, Hive, Pig, Oozie, Sqoop, Flume, Zookeeper, HBase
  • Akohia te Whakawhanawhara, Te Whakapae RDD, Graphx, Te tuhituhi MLlib Whakamahia te tono
  • Ko nga mahi whakahaere a te Kaiwhakahaere Kaupapa Hadoop me te whakahaeretanga, te aro turuki, te whakahaere me te raruraru
  • Ko te whakatikatika i nga taputapu ETL rite Pentaho / Talend ki te mahi ma MapReduce, Hive, Pig, me etahi atu
  • Te māramatanga taipitopito o nga tātaritanga Raraunga Nui
  • Nga tono whakamatautau Hadoop me te MR Unit me etahi atu taputapu automation.
  • Mahi me nga ahuatanga raraunga Avro
  • Whakamahia nga kaupapa moemoeke me te whakamahi i Hadoop me Apache Spark
  • Kia rite ki te whakakore i nga Tiwhikete Hatoop Tika Nui.

tikanga Audience

  • Kaiwhakawhanake Whakahohea me nga Kaiwhakahaere Pūnaha
  • He tohunga ngaio mahi, Kaiwhakahaere kaupapa
  • Nga Kaihanga Raraunga Raraunga e hiahia ana ki te ako i etahi atu poutoko me te Titiro, Nga Tatauranga, Whakahaere
  • Nga Kaihauturu Tumuaki, Nga Kaitohutohu me nga Tohu Whakaaturanga
  • Pakihi Pakihi, Whakamahere Raraunga, me nga Kaitohu Tauanga
  • Ka taea e nga kaitohutohu, kaore e hiahia ana ki te ako i te hangarau hou o te Raraunga Nui, ka taea te whakangungu tuihono tuihono mo tenei rarangi Raraunga Hatoop

hiahiatanga

  • Kaore he mea tika ki te tango i tenei whakangungu raraunga Big me te rangatira Hadoop. Engari ko nga kaupapa o UNIX, SQL me java he pai.Na Intellipaat, ka whakaratohia e mätou te ahurei whakahirahira me te akoranga Java me to tatou whakangungu tohu Tiwhikete Raraunga nui hei whakapoke i nga pukenga e hiahiatia ana, kia pai ai koe ki a koe i te ara ako ako.

Course Outline Duration: 2 Days

Te Whakataki ki te Raraunga Nui me te Hadoop me tona Koiora, Mahere Whakaiti me HDFS

He aha te Big Data, Kei hea a Hadoop i roto i, Hadoop Kōnae Kōnae Whakaratohia - Ngā Whakaaro, Poraka Pouaka, Namenode Tuarua, Te Whakaritea Nui, Te Maamaa IAKA - ResourceManager, NodeManager, Te rerekētanga i waenga i te 1.x me te 2.x

Te Whakataunga Hadoop & te tatūnga

Hadoop 2.x Cluster Architecture, Federation me te High Availability, Ko te Whakatūranga Tohu Whakaoho Tae, Ngā Hatoop Cluster Modes, Ngā Whakaaetanga Hokiop Tae, Hadoop 2.x Kōnae Whakangungu, Cloudera Whakauru Node kotahi

Whakawhanaweti i te Mahere Mahere

Me pehea te Mahere Mahere, pehea nga mahi whakaiti, pehea te mahi a te kaitohutohu, nga kaihautū, nga kaitautoko, nga puka whakaurunga, nga putanga putanga, te whakatere me te tohaina, te mahere mahere, te whakaiti i te taha taha, te MRUnit, te kaatai ​​i whakawhiwhia

Nga mahi a Lab:

Te mahi tahi me te HDFS, te tuhi i te WordCount Program, Te tuhi i te waitohu ritenga, te Mahere me te Kaihautū, Mahere Mahere, Whakaitihia Nga Taeha, Whakaritea Nga Mahere Whakahaere, Whakahaere Mahere i roto i te Rohe LocalJobRunner

Te Whakarite Rautaki Kauwhata

He aha te Kauwhata, Te Whakaaturanga Kauwhata, Te Painga Rapu Rapu Algorithm, Te Whakamahereiro Mahere o te Mahere Whakaiti, Me pehea te Mahinga Kaupapa Algorithm, Tauira o te Mahere Matapihi Whakaiti,

    Mahi 1: Mahi 2: Mahi 3:

Te māramatanga taipitopito o te Pig

A. Whakataki ki te Pig

Te mohio ki te Apache Pig, nga ahuatanga, nga whakamahinga me nga ako ki te taunekeneke me te Pig

B. Te tango i te Pig mo te tautuhinga raraunga

Ko te whakariterite o te Pig Latin, nga whakamaanga rerekē, te momo raraunga me te tautuhinga, nga momo raraunga, te whakamahi i te Pig mo ETL, te utaina raraunga, te tiro angamahi, nga tautuhinga mara, nga mahi e whakamahia ana.

C. Pig mo te tukatuka raraunga matatini

He maha nga momo raraunga tae atu ki te whakauru me te uaua, te raraunga tukatuka me te Pig, te whakariterite raraunga, te mahi mahi

D. Performing multi-dataset operations

Whakauruhia te raraunga, te wehewehe raraunga, nga tikanga maha mo te huinga raraunga e honohono ana, e whakarite ana i nga mahi, e mahi ana-ringa

E. Ko te Pig Tae

Te māramatanga ki nga mahi tautuhinga a te kaiwhakamahi, te mahi tukatuka raraunga me etahi atu reo, kawemai me nga tonotono, te whakamahi i te rerema me te UDF hei whakawhānui i te Pig, nga mahi mahi

F. Pig Jobs

Te mahi tahi me nga raupapa raraunga tuuturu e whai ana i Walmart me nga Arts Electronic hei tauira ako

Te māramatanga taipitopito o Hive

A. Hive Whakataki

Te Marama ki te Hive, te whakarite raraunga tuku iho me te Hive, te Pig me te Hive whakatau, te kohikohi i nga raraunga i roto i te hoahoa Hive me te Hive, te honohono a Hive me nga momo whakamahinga o Hive

B. Hive mo te tohatoha raraunga whakawhitinga

Te maatau ki te HiveQL, te tuhonohono taketake, nga ripanga me nga papaunga raraunga, nga momo raraunga, te tautuhinga raraunga, te whakauru i nga mahi hangarau, te whakamahi i nga uiui i nga tuhinga, i te anga me te Hue.

C. Te whakahaere raraunga me Hive

Te maha o nga raraunga, te hanganga o nga raraunga, ngaa raraunga raraunga i te Hive, te whakatauira raraunga, Teapu Whakaritea, Teapuranga Whaiaro, te utaina raraunga, te whakarereketanga o nga raraunga me nga Ripanga, te whakaemi rapanga me nga Tirohanga, te pupuri i nga uiuinga, te mana uru raraunga, te whakahaere i nga raraunga ki a Hive, Hive Metastore me te Tūmau Whakatau.

D. Te Whakaritea o te Hive

Te mahi akoranga o te uiuinga, te tohuranga raraunga, te wehewehe me te whakatere

E. Te Hiko Atu

Te whakamahi i nga mahi tautuhinga kaiwhakamahi mo te whakawhānui i te Hive

F. Ngā ringa i runga i ngā Mahi - mahi me nga raupapa raraunga nui me te uiui whānui

Te Hoko Hive mo nga pukapuka nui o nga huinga raraunga me te nui o te uiui

G. UDF, uiuinga uiui

Te mahi nui me nga Uiui Kua Tautuhia e te Kaiwhakamahi, te ako ki te whakapai ake i nga uiuinga, nga tikanga maha hei mahi i nga mahi.

Impala

A. Whakataki ki Impala

He aha te Impala? Nahea te Impala e puta mai ana i te Hive me te Pig, pehea te Impala Differs mai i nga Rapu Whakamaunga Rawa, Nga Whakataunga me te Whakaritea a Meake Nei, Te Whakamahia o te Iwi Impala

B. Te whiriwhiri i te pai (Hive, Pig, Impala)

C. Whakatauira me te Whakahaere Raraunga ki te Impala me te Hive

Te Raraunga Raraunga Raraunga, Te Whakaritea i nga Raraunga me nga Ripanga, Raraunga Mahi ki nga Ripanga, HCatalog, Impala Metadata Caching

D. Rauemi Raraunga

Paapakotanga Panui, Te wehewehe i te Impala me te Hive

(AVRO) Raraunga Raraunga

Te Whakaritea o te Hōputu Kōnae, Te Tautoko Rauemi mō ngā Pukapuka Kōnae, Mahere Avro, Whakamahia a Avro me Hive me Sqoop, Evro Schema Evolution, Compression

Whakataki ki te hoahoa Hbase

He aha te Hbase, kei hea te pai, He aha te NOSQL

Apache Spark

A. He aha te mea kakara? Te mahi me te Pakihi Whakawhiti me te Hadoop Whakawhiti Kōnae

He aha te Spark, Whakataurite i waenga i te Spark me te Hadoop, Ngā Waehu o te Waro

B. Ngā Taputapu Taputapu, Ngā Algorithms Whakawhiti Korero-Nga Whakaaetanga Aromatawai, Te Whakamaherehere Kauwhata, Te Akoranga Miihini

Apache Spark- Whakatakotoranga, Whakaritea, Tuhituhi, Paarua, Whakanuihia te Whakanui o te Tae, Nga Taputapu Whakaaturanga, Te tauira whakaari, te mahout, te ngaru, te kauwhata

C. Whakahaerehia te Kohu i runga i te Cluster, Te Tuhituhi Tuhituhi Nga Whakamahinga Python, Java, Scala

Te whakamārama i te tauira python, Whakaatuhia te whakauru i te hiko, Te whakamārama i te hōtaka arataki, Te whakamārama i te horopaki whakaari me te tauira, Te tautuhi i te rerekētanga o te tautuhi ngoikore, te whakawhitiwhiti i te scala me te java seamlessly, te whakamārama me te whakawhitinga., Whakamāramahia he aha te ahua, Whakamāramahia te mahi teitei ake me te tauira, Tautuhi i te OFI he kaiwhakarato maatauranga, He painga o te hiku, tauira o te Lamda e whakamahi ana i te hiku, Whakamāramahia te Maherehere me te tauira

Ko te Tatūnga Hokooputu Cluster me te Whakahaere Mahere Whakaiti i Ngā Mahi

Whakanoho Tae Kotahi Tauranga ma te whakamahi i te Amazon ec2 - Te waihanga 4 kōpuku huinga huinga, Mahere Whakatere Whakaitihia Ngā Mahi i runga i te Cluster

Kaupapa Nui - Whakanui katoa ana me te Hononga Tohu

Te mahi tahi me te honohono i nga Tohu, Mahi me nga Rapu raraunga nui, Nga waahanga e uru ana ki te whakamohio i nga raraunga nui

Ko te hononga tahi me te Hikoopioropiha Hadoop

Me pehea nga taputapu ETL i roto i te Big Data Industry, Te hono ki HDFS mai i te taputapu ETL me te whakawhiti raraunga mai i te pūnaha Paetata ki te HDFS, te whakawhiti i te Raraunga mai i DBMS ki HDFS, Mahi me te Hive me te ETL, Te Whakarite Mahere Whakaiti i te mahi i te taputapu ETL, Whakamutu ki te Whakamutu ETL Ko te PoC e whakaatu ana i te whakauru raraunga nui me te taputapu ETL.

Whirihoranga Cluster

Tirohanga Whirihoranga me te kōnae whirihoranga nui, Tautuhinga whirihoranga me nga uara, taapiri HDFS MapMahia nga tawhiri, te tautuhinga taiao Hadoop, 'Whakauru' me te 'Tangohia' nga kōnae whirihoranga, Lab: MapReduce Performance Tuning

Whakahaere me te Whakahaere

Ko nga hanganga rehitatanga ingoa Namenode / Datanode, ko nga kōnae, Ko te ahua o te pūnaha kōnae, me te Whakatika i te takiuru, te Ture Whakatau Tirohanga, te korenga Namenode me te tukanga whakaora, Aratau Haumaru, Metadata me te Raraunga Raraunga, Nga raruraru me nga otinga ka taea te rapu, te tango me te tango i nga whaarangi, Whakamahia te Whakahaere Pūnaha Kōnae

Te aroturuki me te raruraru

Nga mahi pai o te aroturuki i te huinga, Te whakamahi i nga raau me te waahi mo te aroturuki me te raruraru, Te whakamahi i nga taputapu tuwhera-ki te aroturuki i te huinga

Job Scheduler: Mahere te whakaheke i te rere o te tukunga mahi

Me pehea te whakarite i nga Mahi i runga i te huinga rite, te FIFO Schedule, Fair Scheduler me tona whirihoranga

Ko te Whakaritea Kaerangi Maha me te Whakahaere Mahere Whakaiti i nga Mahi i te Amazon Ec2

Whakanoho Tae Kotahi Tauranga ma te whakamahi i te Amazon ec2 - Te waihanga 4 kōpuku huinga huinga, Mahere Whakatere Whakaitihia Ngā Mahi i runga i te Cluster

ZOOKEEPER

ZOOKEEPER Introduction, ZOOKEEPER whakamahia nga take, ZOOKEEPER Ratonga, ZOOKEEPER raraunga Model, Znodes me ona momo, Mahi Znodes, Tirohanga Znodes, Znodes pānui me te tuhi, Whakaaetanga Whakaaetanga, Whakahaere Cluster, Pōti Pōti, Poutū Tuku Whakarato, Tohu nui

Haere ki Oozie

He aha te Oozie ?, Te Whakanoho Oozie, Te Whakahaere i te tauira, te Oozie-workflow engine, te tauira M / R mahi, te tauira o te kupu, te tono Taumahi, te tukatuka Maataumahi, nga whakawhiti rerenga o te reremahi, te tukatuka mahi Oozie, te haumarutanga Oozie, He aha te haumarutanga Oozie, te tukunga Job , Te maha o te waitohu me te painga, Te waa o te mahi Oozie, te Kaihautū, te Taeke, te Rangatira o te Abstraction, Te Whakaahuatanga, Te Whakamahia te 1 X: Ko te waa te whakamahi, Whakamahia te 2: nga raraunga me nga waahanga, Whakamahia te 3: te matapihi hurihuri

Tuhinga o mua

Overview o Apache apu, pae tino tohaina puna Raraunga, huri te hanganga o Raraunga, titiro piri, Anatomy o apu, ariā Core, Event, kiritaki, Agents, Pūtake, Hongere, Sinks, Interceptors, Channel kaikōwhiri, totohu pūtukatuka, Raraunga horomitanga, Agent paipa , Te whakawhitinga raraunga tawhito, Te urupare, me te whakautu, He aha nga waaawa, Whakamahia te pakihi- Whakaritea Whakauru, Te Whakaritea o te kaitoi, Te whakahaere i te paamu paari, Raraunga raraunga mo ia kaihoko, Te tauira e whakaatu ana i te whakamahinga o te kohanga kotahi

Tuhinga o mua

HUE whakataki, HUM te hanganga-a-tinana, he aha te HUE, te tirohanga o te Ao, te painga o te HUE, Me pehea te tuku raraunga ki te Pūtirotiro Kōnae ?, Tirohia nga ihirangi, Whakauru i nga kaiwhakamahi, Integrating HDFS, Kaupapa o HUE FRONTEND

Haere ki Impala

IMPALA Ngā tirohanga: Ngā whāinga, te tirohanga a te Kaiwhakamahi o Impala: Te tirohanga, te tirohanga a te Kaiwhakamahi o Impala: SQL, Tirohanga a Impala: Apache HBase, hoahoa Impala, toa taonga a Impala, ratonga pukapuka a Impala, nga waahanga tono uiui, te whakarite i te Impala ki te Hive

Test Test Application

He aha te whakamatautau he mea nui, Whakamātautau Unit, Whakamātautau whakauru, Whakamātautau mahi, Whakamātautau, Whakamātautau QA, Te tohu me te mutunga ki nga whakamatautau whakamutunga, Nga whakamatautau mahi, Nga whakamatautau tiwhikete, Nga whakamatautau Haumaru, Nga whakamatautau Kaiaka, Te Komihana me te Whakatuhoatanga o nga Naturanga Raraunga Raraunga, Nga whakamatautau tika , Nga whakamatautauranga

Nga Rohe me nga Tika o Hadoop Testing Professional

He mahara ki te Titauraa, te faaineineraa o te Testing Estimation, Kēhi Test, Test Raraunga, Test hanga moenga, Test Execution, koha Pūrongo, koha Retest, Daily tuku pūrongo Tūnga, Test otinga, ETL whakamātautau i nga atamira (HDFS, taenga, HBASE) ia uta i te tāuru (rangitaki / kōnae / pūkete etc) te whakamahi i sqoop / apu e ngā engari e kore e iti ki whakaū raraunga, Hemihemi, Whakamanatanga Kaiwhakamahi me te Motuhēhēnga whakamātau (Groups, Kaiwhakamahi, Painga etc), hē Report ki te rōpū whanaketanga kaiwhakahaere ranei me te taraiwa ki te whakaoti, Whakakotahitia nga taapanga katoa, me te whakaputa i nga reta kino, Te whakamana i nga ahuatanga hou me nga take i Core Hadoop.

Ko te kaupapa MR mo te Whakaaturanga Mahere-Whakaiti Papatono

Nga korero kino ki te kaiwhakahaere whakawhanake, ki te kaiwhakahaere ranei, me te peia ki te whakamau, Whakaritea nga panga katoa, me te waihanga i nga ripoata pakaru, Ka taea te hanga i tetahi waahanga whakamatautauranga e kiia ana ko te MR Unit mo te whakamatautau i te Mahere-Whakaiti i nga papatono.

Waitohu Uara

Nga whakamatautau aunoa i te whakamahi i te OOZIE, Whakamanatanga Raraunga ma te whakamahi i te taputapu tawhito rapu.

Whakamatauranga

Mahere whakamātautau mo te whakamohoatanga HDFS, te whakamatau me te hua

Te Rautaki Mahere Whakamātautau me te tuhi i nga korero Test mo nga whakamatautauranga Hadoop

Me pehea te whakamatautau i te tāuta me te whirihora

Job me te Tautoko Tiwhikete

Ko nga tohutohu me nga Whakaaetanga Whakamaherehere a Cloudera me te Whakatakoto i te uiuinga uiuinga, nga Tohutohu Whakatikatika Whakangungu me nga Hangarau

Tena tuhia ki a matou i info@itstechschool.com & whakawhiti mai ki a matou i te 91-9870480053 mo te utu akoranga me te utu tiwhikete, te whakatakotoranga me te tauwāhi

Tukua mai he Uiui

Kua hoahoatia tenei akoranga whakangungu hei awhina i a koe kia rua Ko te Whakaaturanga Whakarato a Cloudera me te Hadoop Kairangi Tiwhikete (CCA175) whakamātautau me te Cloudera Kaiwhakahaere Tiwhikete mo Apache Hadoop (CCAH) whakamātautau. Ko te ihirangi akoranga whakangungu katoa i roto i te rārangi ki enei hōtaka tohu e rua, me te āwhina ūkui koutou enei whakamātautau tohu ki te noho humarie, me te tiki i nga mahi pai i roto i te MNCs runga.

Hei waahanga o tenei whakangungu ka mahi koe i nga waahanga o nga waahanga me nga waahanga e tino nui ana te paanga ki te ahumahi ahumahi o te ao nei ka awhina i a koe ki te mahi tere i taau mahi.

I te mutunga o tenei kaupapa whakangungu ka waiho he awangawanga e whakaatu tika ana i te momo o nga patai e ui ana i nga waahanga tiwhikete takitahi, ka awhina ia koe ki te tohu i nga tohu pai i te waahanga tohu.

Ko te Tiwhikete Whakamutunga Whakangungu ka whakawhiwhia ki te whakatutuki i nga mahi a te Kaupapa (i runga i te arotake tohunga) me te whiringa o te 60% tohu i roto i te nama. Ko te tiwhikete Intellipaat kua tino mohiotia i runga ake i te 80 + MNCs rite ki a Ericsson, Cisco, Manaware, Sony, Mu Sigma, Saint-Gobain, Paerewa Paerewa, TCS, Genpact, Hexaware, etc.

Mo te tahi atu korero pai Whakapā mai.

Big Data Hadoop Training in Gurgaon | Big Data Hadoop Training Institute in Gurgaon

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