ituaigaAoaoga a le potuaoga
Tusi Resitala

faʻasalalauga faʻamaumauga faʻamaumauga tetele

Big Data Hadoop Certification Course & Training

lagona

Tagata o loʻo faʻalogo ma faʻamuamua

Taʻiala o le Vasega

Faamatalaga & Totogifuapauina

faʻamaoniga

Big Data Hadoop Certification Course Overview

O se faʻataʻitaʻiga atoa a le Hadoop Big Data Training Training ua fuafuaina e le au atamamai tau alamanuia e tusa ai ma manaoga o galuega faʻapisinisi i le taimi nei e tuʻuina atu aʻoaʻoga loloto i luga o faʻamatalaga tetele ma Hadoop Modules. O se faʻalapotopotoga faʻapitoa lenei o aʻoaʻoga o aʻoaʻoga a le Big Data, o se tuufaatasiga o aʻoaʻoga i Hadoop developer, Pule o le Hadoop, faʻataʻitaʻiga faʻatoʻa, ma auiliiliga. Lenei Cloudera O aʻoaʻoga faʻapitoa o le a saunia ai oe e faʻamaonia le faʻamaonia tele o faʻamaumauga.

sini

  • Faʻatonu autu o Hadoop 2.7 ma YARN ma tusi tusi e faaaoga ai
  • Faʻatulagaina o le Node namu ma le telefoni fusi i le Amazon EC2
  • Master HDFS, MapReduce, Hive, Pig, Oozie, Sqoop, Flume, Tagata Tausia, HBase
  • Aoao Spark, Spark RDD, Graphx, Tusitusi MLlib Lisi talosaga
  • Faiaoga o le Pule Hadoop e pei o le puleaina o le tautoga, mataʻituina, pulega ma folasia
  • Faʻapipiʻiina o meafaigaluega e pei o Pentaho / Talend e galulue ma MapReduce, Hive, Pig, ma isi
  • Malamalamaga auiliiliga o faʻamaumauga o Ata tetele
  • Suʻega faʻataʻitaʻiga faʻataʻitaʻiga e faʻaaoga ai le MR Unit ma isi masini masini.
  • Galue ma faʻamatalaga faʻamatalaga Avro
  • Faʻataʻitaʻi faʻataʻitaʻiga galuega faʻapitoa e faʻaoga ai Hadoop ma Apache Spark
  • Ia saunia e faʻamaonia le faʻamaumauga Big Data Hadoop.

Faapotopotoga faamoemoe

  • Polokalame Atinaʻeina o Polokalama ma Pule Faʻatonu
  • Malamalama i tagata faigaluega galuega, Pule o galuega faatino
  • Big DataHadoop Developers e naunau e aʻoaʻo isi tapuni e pei o Suʻega, Suʻega, Pulega
  • Mainframe Tomai Faapitoa, Faʻatekonolosi & Tomai Faapitoa
  • Pisinisi Faʻamatalaga, Faʻamaumauga o Faʻamatalaga ma Faʻamatalaga Faʻamatalaga
  • Tamaiti aʻoga, o loʻo aʻoaʻoina le aʻoga sili ona naunau e aʻoaʻo le televise o le Big Data e mafai ona latou faʻaaogaina lenei faʻataʻitaʻiga Big Data Hadoop Certification i le initaneti

Prerequisites

  • E leai se mea muamua e manaʻomia e ave ai lenei aʻoaʻoga faʻamatalaga tetele ma ia faʻamaoni Hadoop. Ae o mea autu o UNIX, SQL ma java o le a lelei.At Intellipaat, matou te tuuina atu le faʻafetai faʻapitoa ma le Java ma le faʻataʻitaʻiga o le faʻataʻitaʻiga o le Big Data mo le faʻaleleia o tomai manaʻomia ina ia e lelei i luga o oe.

Course Outline Duration: 2 Days

Folasaga i Big Data & Hadoop ma lana Ecosystem, Map Reduce and HDFS

O le a le Big Data, O fea e talafeagai i ai Hadoop, Polokalame Faʻasalalau Tuʻufaʻatasi - Faʻamatalaga, Faʻasalaga, Igoa Igoa Maualuga, Maualuga Maualuga, Malamalama IOA - ResourceManager, NodeManager, Eseesega i le 1.x ma le 2.x

Polokalame faʻapipiʻi & seti

Hadoop 2.x Cluster Architecture, Faʻatasiga ma Maualuga Maualuga, Faʻatulagaina o le Faʻatulagaina o le Fusi Faʻapitoa, Faʻaogaina o Faiga Faʻapitoa, Talosaga Faʻatasi o Polokalame Faʻavae, Hadoop 2.x Fetuunaiga o Faʻamaumauga, Cloudera Fusi faʻapipi tasi

Vaʻavave lemu i Mapreduce

E faʻafefea ona faʻapipiʻiina le faʻafanua, pe faʻafefea ona faʻaititia, pe faʻafefea ona faʻaaogaina le avetaavale, faʻapipiʻi, faʻasalalau, pepa faʻapipiʻi, faʻataʻitaʻiga, faʻataʻitaʻiga, faʻasolosolo, faʻafanua, faʻasolosolo itu,

Galuega faatino:

Galulue faʻatasi ma HDFS, Tusitusi WordCount Program, Tusitalaina o le tagata masani, Mapreduce ma le Combiner, Faʻafanua Faʻafeiloaʻi, Faʻaitiitia le Itu Faʻatasi, Suʻega o Suʻega o Suʻe, Faʻataʻatia le Mapreduce i le LocalJobRunner Mode

Faʻataʻitaʻiina Faʻailoga o Faʻafitauli

O le a le ata, ata o le ata, o le falaoa Muamua Suʻe le Algorithm, Faʻatusaina o Ata o Faʻafanua Faʻaitiitia, Faʻapefea ona faia le Ata Algorithm, Faataʻitaʻiga o le Ata Faʻafanua Faʻaitiiti,

    Talositino 1: Faʻafiafiaga 2: Faʻafiafiaga 3:

Malamalamaga auiliili o le puaa

A. Faatomuaga i le Pig

Malamalama i le Apache Pig, o foliga vaaia, eseese faʻaaoga ma aʻoaʻoga e fegalegaleai ai ma Pig

E. Faʻaaogaina o Pig mo suʻesuʻega faʻamatalaga

O le syntax o le Pig Latina, faʻamatalaga eseese, faʻasologa o fuainumera ma le faamamaina, ituaiga o faʻamatalaga, faʻaaogaina o le Pig mo ETL, faʻamaumauga o faʻamatalaga, mataʻituina ata, faʻamatalaga o fanua, galuega masani masani ona faʻaaogaina.

C. Pig mo le faigata o le faagasologa o faamatalaga

Eseese faʻamatalaga faʻamaumauga e aofia ai mea faʻapitoa ma faigata, faʻamaumauga faʻasoasoa ma Pig, faʻapipiʻiina o faʻamatalaga o faʻamatalaga, faʻamalositino faatino

D. Performing multi-dataset operations

Faʻamaumauga o tuʻufaʻatasia, faʻasologa o faʻamaumauga, auala eseese mo le tuʻufaʻatasia o faʻamaumauga, tuʻuina o faʻatinoga, faʻaaogaina o lima

E. Pupu Faʻatele

Malamalama i galuega faʻatulagaina a le tagata, faʻatinoina faʻamatalaga faʻamatalaga i isi gagana, faʻaulufale mai ma masini, faʻaaogaina le faʻafefe ma UDF e faalautele le Pig, faatinoga faʻatino

F. Pig Jobs

Galulue ma faʻamaumauga faʻamatalaga moni e aofia ai Walmart ma Faʻamatalaga Faʻatekonolosi e fai ma suʻesuʻega

Malamalamaga auiliiliga o Hive

A. Folasaga Faatomuaga

Malamalama i le Hive, faʻamaumauga faʻamaumauga faʻapitoa faʻatasi ma le Hive, Pig ma Hive faʻatusatusaga, teuina o faʻamatalaga i le Hive ma Hive schema, faʻatalatalanoaga ma le faʻaogaina o mea faʻaaogaina o Hive

B. Hive mo suʻesuʻega faʻamatalaga faʻatasi

Malamalama i le HiveQL, syntax autu, o laulau eseese ma nofoaga o faʻamaumauga, faʻamaumauga o faʻamatalaga, tuʻufaʻatasia o faʻamaumauga, maualuluga galuega e fausia, faʻaaogaina o fesili i luga o tusitusiga, atigi ma Hue.

C. Faʻamaumauga faʻamaumauga ma le Hive

O le tele o faʻamaumauga, faʻatulagaina o faʻamaumauga, faʻamaumauga i totonu o le Hive, faʻataʻitaʻiga o faʻamatalaga, Fuafuaga faʻatautaia, faʻaogaina o Laulau, faʻapipiʻiina o faʻamatalaga, suia o faʻamaumauga ma Tables, faʻailoga faigofie i Vaʻaia, maua ai le teuina o fesili, puleaina o faʻamatalaga faʻamatalaga, puleaina o faʻamaumauga ma le Hive, Hive Metastore ma le Thrift server.

D. Faʻatupulaia o le Uiga

Aoaoina o le faʻatinoga o fesili, faʻamaumauga o faʻamaumauga, vaeluaina ma le faʻatautaia

E. Faʻalauteleina o le Mau

Faʻaleleia o galuega faʻatulagaina a le tagata e faʻalauteleina ai le Hive

F. Lima i luga o Faigaluega - galue ma faʻamaumauga tele ma le tele o fesili

Faʻapipiʻiina Hive mo voluma tetele o faʻamaumauga faʻasologa ma le tele o fesili

G. UDF, fesiligia le suʻesuʻega

Galulue faʻatasi ma Faʻamaumauga Faʻamatalaina a le Tagata, aʻoaʻo pe faʻapefea ona faʻatautaia fesili, auala eseese e fai ai le faʻatulagaina o le faʻatulagaga.

Impala

A. Faatomuaga i Impala

O le a le Impala ?, pe faʻafefea ona faʻafefe mai Impala mai le Hive ma le Pig, pe faapefea ona faʻaaogaina e Ipalapala mai Faʻamatalaga o Faʻamaumauga, Faʻamaumauga ma Taʻiala i le Lumanaʻi, Faʻaaogaina o le Impala Shell

B. Filifilia o le Mea Sili (Hive, Pig, Impala)

C. Faʻataʻitaʻiina ma le puleaina o faʻamatalaga ma Impala ma Hive

Faʻamatalaga o Puipuiga o Faʻamaumauga, Fausia o Faʻamaumauga ma Laulau, Faʻamaumauga o Faʻamaumauga i Laulau, HCatalog, Impala Metadata Caching

D. Data Sheet

Vaaiga Vaaiga, Vavaega i Impala ma Hive

(AVRO) Faʻamatalaga Faʻamaumauga

Filifilia o se Faʻasalalauga Filemu, Lagolagoina o Meafaigaluega mo Formats Failautusi, Avro Schemas, Faʻaaogaina o Avro i le Hive ma le Sqoop, Evro Schema Evolution, Compression

Faatomuaga i le faufautua ile Hbase

O le a le Hbase, O fea e talafeagai, O le a le NOSQL

Apache Spark

A. Aiseā e Malamalama ai? Galulue faʻatasi ma le Lulu Faʻaalia ma le Puipuiga Faʻasalalau File System

O le a le Spark, Faʻatusatusaga i le va o le Sipelo ma le Hadoop, Vaega o le Susulu

B. Faʻatauga o Vailaʻau, Algorithms Faʻasalalau masani-Iterative Algorithms, Suʻega Ata, Faʻatinoina o Meafaigaluega

Apache Spark- Faʻatomuaga, Faʻaauau, Maua, Vaevaega, Faʻasagaina Avanoa, Lafoaʻi Vaega, Faataʻitaʻiga faʻataʻitaʻiga, mahout, afa, kalafi

C. Running Spark on a Cluster, Faʻasaga Tusitusi Faʻaaogaina Python, Java, Scala

Faʻamatalaga faʻataʻitaʻiga, Faʻaalia le faʻapipiʻiina o se aloiafi, Faamalamalama le polokalame o le avetaʻavale, Faʻamatalaina le faʻaogaina o le malamalama ma le faʻataʻitaʻiga, Faʻamatalaina le fesuiaiga vaivai o le fesuiaiga, Faʻafesoʻotaʻi le scala ma le java seamlessly, Faamalamalama faʻamaoniga ma tufatufa., Faʻamatala le uiga, faatulaga, Benefits of Spark, Faataitaiga o Lamda e faʻaaogaina ai le aloiafi, Faʻamatalaga Mapreduce ma faʻataʻitaʻiga

Faʻapipiʻi ma le Faʻataʻatiaina o Faʻafanua Faʻaititia galuega

Faʻatasi le faʻapipiʻi faʻapipiʻi o le Node Setete e faʻaaoga ai le Amazon ec2 - Faʻatulagaina o le seti o le setete 4 i le faʻapipiʻiina o le faʻapipiʻiina o le kulupu, Faʻafanua Faʻasao Faʻaitiitia Galuega i luga o le Cluster

Mafuaʻaga Sili - Faʻapipiʻi uma ma Fesoʻotaʻi Taga

Faʻapipiʻi uma ma Fesoʻotaʻi Tagatini, Galulue ma Faʻamaumauga tetele, Laasaga e aofia ai i le suʻeina o faʻamatalaga tetele

ETL Fesoʻotaiga ma le Hadoop Ecosystem

E faapefea ona faʻaaogaina e le ETL meafaigaluega i le Big Data Industry, Fesoʻotai i le HDFS mai le ETL meafaigaluega ma le faʻaliliuina o faʻamaumauga mai le Lotoifale i le HDFS, Faʻaliliuina o faʻamatalaga mai DBMS i HDFS, Galulue ma le Hive ma le ETL, Faʻavaeina o Faʻafanua Faʻaitiitia galuega ile ETL, End to End ETL PoC faʻaalia le tele o faʻamaumauga faʻatasi ma le ETL meafaigaluega.

Fetuunaiga o le kulupu

Aotelega o le folasia ma le faila faatulagaina tele, Faʻamaumauga o faʻasalalauga ma taulaʻa, faʻasologa o le HDFS Map Faʻataʻitaʻiina alafua, Seti faʻasalalauga faʻafanua, 'Include' ma le 'Exclude' configuration files, Lab: MapReduce Performance Tuning

Pulega ma le Tausiga

Faʻamaumauga a le Namenode / Datanode ma faila, Ata o le faila ma le Faʻatulagaga o le log, The Checkpoint Methodata, Faʻamaumauga o le Namenode ma le toe faʻaleleia, Tulaga saogalemu, Metadata ma le Faʻamaumauga o faʻamaumauga, Suʻesuʻega faʻafitauli ma fofo / mea e te suʻeina, Faʻaopopo ma aveese faʻailoga, Lab: MapReduce File System Toe Faʻaleleia

Mataʻituina ma Faʻafitauli

Auala sili ona lelei o le mataituina o se faaputuga vine, Faʻaaogaina o ogalaau ma mea faʻapipiʻi mo le mataʻituina ma le faʻafitauliina, Faʻaaogaina o meafaigaluega matala e mataʻituina ai le kulupu

Job Scheduler: Faʻafanua le faʻaitiitia le gaioiga o le tuʻuina atu o galuega

Auala e faʻatulagaina ai Galuega i luga o le faʻatusatusaga tutusa, FIFO Schedule, Fair Scheduler ma lona faʻatulagaina

Tele Node Cluster Setup ma le Faʻamasinoga Map Faʻitiʻese Galuega i luga o le Amazon Ec2

Faʻatasi le faʻapipiʻi faʻapipiʻi o le Node Setete e faʻaaoga ai le Amazon ec2 - Faʻatulagaina o le seti o le setete 4 i le faʻapipiʻiina o le faʻapipiʻiina o le kulupu, Faʻafanua Faʻasao Faʻaitiitia Galuega i luga o le Cluster

ZOOKEEPER

FUAFUAGA FUAFUAGA, ZOOKEEPER faʻaaoga mataupu, ZOOKEEPER Services, ZOOKEEPER data Faʻataʻitaʻiga, Znodes ma ona ituaiga, Faʻamasinoga o le Znoder, Leoleo Znodes, Faitau ma tusitusi, Faʻatusatusa o le Faʻatonu, Puleaina o le kulupu, Taʻitaʻaiga Taʻitaʻifono, Faʻasologa Faʻasologa Faʻasaʻo, Manatu taua

Faʻaauau Oozie

O le a le mea e mafai ai e le Oozie ?, O le faʻaaogaina o le Oozie, le faʻataʻitaʻiina o se faʻataʻitaʻiga, le faʻaaogaina o le Osozie, galuega faataitai M / R, faʻataʻitaʻiga Upu, Faigaluega, , Faʻatulagaga ma le faʻaleleia o le televave, Taimi taimi o le galuega Oozie, Faʻatonu, Faʻasalaga, Lafoaʻi o mea faʻapitoa, Faʻataʻitaʻiga, Faʻaaoga le 1: Faʻamatalaga taimi, Faʻaaoga le 2: faʻamaumauga ma taimi, Faʻaaoga le 3: matala

Avance Flume

Aotelega o Apache Flume, Faʻasalaga tufatufa faʻamaumauga, Suiga o le Faʻamatalaga, Ata e sili atu ona vaʻaia, Anatomy o Flume, Faʻataʻitaʻiga, Faʻasalalauga, Faʻasalalau, Faʻatonu, Filifili auala, Faʻasinifo o le masini, Faʻamatalaga o faamatalaga, Faʻasalalau o le Agent , Faʻamatalaga o fesoʻotaʻiga faʻasoasoa, Faʻataʻitaʻia ma toe faʻapipiʻi, Aisea e fai ai?

Faʻaauau HUE

FAʻAALIGA HUE, HUE faʻaolataga, O le a le HUE, le manatu o le lalolagi moni, Le lelei o le HUE, Faʻapefea ona faʻapipiʻi faʻamaumauga i le Initaneti ?, Tagaʻi i le mea e aofia ai, Faʻapipiʻi tagata, Integrating HDFS, Fundamentals of HUE FRONTEND

Advance Impala

IMPALA Vaʻaiga: Goals, Faʻaaliga a le tagata e uiga i Impala: Vaaiga Aoao, Iloiloga a le tagata e uiga i Impala: SQL, Faʻaaliga a le tagata i Impala: Apache HBase, fale faʻatautaia Impala, falemalo o le Impala, galuega faʻasologa o Impala, faʻasologa o fesili, faʻatusatusa o Impala i Hive

Suʻega Faʻaaoga Tauga

Faʻatinoga o suʻega, Suʻega a le Vasega, Suʻega Faʻatinoga, Suʻega o Suʻega, Suʻega, Suʻega Faʻatonu a le QA, Suʻega ma le Iʻuga i Suʻega Faʻaiʻuga, Suʻega Faʻatino, Suʻeina o Tusi Faamaonia, Suʻega Puipuiga, Suʻega Scalability, Komisiina ma le Faʻasaoina o Nofoaga Faʻamaumauga, Suʻega faʻamaonia , Suʻega faʻasaʻo

Matafaioi ma Tiutetauave o le Tomai Faʻalavelave Toto

Malamalama i le Manaoga, sauniuniga o le Suʻega o Suʻega, Suʻega o Suʻega, Faʻamaumauga o Suʻega, Faʻataʻitaʻiga o le suʻega, Suʻega o Suʻega, Lelei Lipoti, Suʻega Lelei, Aso Faʻamatalaga o Lipoti, Suʻega Suʻega, Suʻega ETL i taimi uma (HDFS, HIVE, HBASE) faʻapipiʻiina o faʻasalalauga (faila / faila / faamaumauga ma isi) faʻaaoga sqoop / flume e aofia ai ae le gata i faʻamaoniga o faʻamaoniaga, Faʻasalaga, Faʻatagaga o le Tagata ma faʻamaoniga faʻamaonia (Groups, Tagata faaaoga, Privileges ma isi), Faʻaletonu lipoti i le atinaʻeina poʻo le pule ma le avetaavale latou faʻamaeʻaina, Faʻamaʻoti uma mea leaga ma faia ni lipoti le lelei, Validating new features and issues in Core Hadoop.

Tausaga e taua o le MR Unit mo le Suʻeina o Faʻafanua-Faʻaitiitia Polokalame

Lipoti le faaletonu i le aufaigaluega po o le pule ma ave i latou i le tapunia, Faʻasoa uma mea leaga ma faia ni lipoti le lelei, Faia mo le faia o se suʻega e suʻeina MR Unit mo le suʻega o Map-Reduce programs.

Suʻega Suʻega

Suʻeina ole Aunoa e faʻaaoga ai le OOZIE, Faʻamaumauga o faʻamatalaga e faʻaaoga ai le meafaigaluega o mea e manaʻomia.

Suʻega Suʻega

Fuafuaga faʻataʻitaʻiga mo le faʻaleleia o HDFS, Tomai suʻesuʻe ma le taunuʻuga

Fuafuaga Fuafuaga o Suʻega ma le tusiaina o Suʻega o Suʻega mo le suʻeina o le Faʻaaogaina Hadoop

E faʻapefea ona suʻeina le faʻapipiʻi ma le fetuunaiga

Iopu ma Tusi Faamaonia Tusi Faamaonia

Cloudera Tusi Faamaonia Tusi Taiala ma Taiala ma le Sauniuniga o Faʻatalanoaga, Faʻamatalaga Faʻatinoga ma Faʻatinoga

Faamolemole tusi mai ia i matou i info@itstechschool.com & faʻafesoʻotaʻi i matou i le 91-9870480053 mo le tau aʻoaʻoga & tusi faamaonia o tau, faʻasologa ma le nofoaga

Tuʻu mai ia i matou se Fesili

O lenei kosi aʻoaʻoga ua mamanuina e fesoasoani ia te oe e faʻamalamalama uma ai Cloudera Spark ma Hadoop Faʻatonu Tusi Faamaonia (CCA175) suʻega ma Cloudera Certified Administrator mo Apache Hadoop (CCAH) suʻega. O le aʻoaʻoga atoa o aʻoaʻoga o loʻo ogatasi ma nei polokalame faʻamaonia e lua ma fesoasoani ia te oe e faʻamaonia nei suʻega tusi faʻamaonia ma maua ai galuega aupito sili i MNC pito i luga.

I le avea ai o se vaega o lenei aʻoaʻoga, o le ae galue ai i le taimi moni galuega faatino ma tofitofiga o loʻo i ai le tele o aʻafiaga i le tulaga moni o alamanuia o le lalolagi, lea e fesoasoani ai ia te oe e vave faʻatautaia au galuega.

I le faaiuga o lenei polokalame aʻoaʻoga o le a iai ni suʻega e atagia lelei ai le ituaiga o fesili o loʻo fesiligia i suʻega tusipasi taʻitasi ma fesoasoani ia te oe e togi lelei faailoga i le suʻega tusipasi.

O le Tusi Faamaonia o le Aʻoaʻoga o le a tuʻuina atu i luga o le maeʻaina o galuega a le Poloketi (i luga o iloiloga faʻapitoa) ma i luga o le sikoa o 60% i le suʻega a itiiti ifo o le 80%. O le faʻamaoniaina o le faʻamaoniaina aloaia e aloaia lelei i luga o XNUMX + MNC pei o le Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.

Mo nisi faʻamatalaga agalelei Faafesootai matou.


iloiloga