ụdịỌzụzụ klas
Deba aha

nnukwu data amachi akwụkwọ ntinye akwụkwọ

Big data Hadoop Certification Course & Training

Overview

Ndị na-ege ntị na ihe ndị dị mkpa

Ihe ndepụta

Nhazi & Ụgwọ

Asambodo

Nnukwu Data Archive Certification N'ezie Overview

Ọ bụ ọzụzụ zuru ezu Hadoop Big Data nke ndị ọkachamara ụlọ ọrụ na-ahụ maka ọrụ ụlọ ọrụ ugbu a chọrọ iji nye ihe ọmụma miri emi banyere nnukwu data na Hadoop Modules. Nke a bụ ụlọ ọrụ kwadoro ọzụzụ nkuzi nke nnukwu Data nke bụ nchịkọta nke ọzụzụ ọzụzụ na Hadoop onye Mmepụta, onye na-elekọta Hadoop, nyocha ule, na nchịkọta. Nke a Na-emepụta ihe Ọzụzụ ịzụlite ga-akwadebe gị iji kpochapụ nnukwu asambodo data.

ebumnobi

  • Nnabata ukwu nke Hadoop 2.7 na YARN ma dee ederede iji ha
  • Ịtọ ụda ọnụ na ọnụ ọgụgụ dị iche iche na Amazon EC2
  • Master HDFS, MapReduce, Hive, Pig, Oozie, Sqoop, Flume, Zookeeper, HBase
  • Mụta ihe na-emepụta, Gbasaa windo RDD, Graphx, ederede MLlib Akwalite ngwa
  • Ọrụ Nnabata Hadoop dị ka njikwa njikwa, nlekota, nchịkwa na nhazi nsogbu
  • Ịhazi ngwaọrụ ETL dịka Pentaho / Talend na-arụ ọrụ na MapReduce, Hive, ezi, wdg
  • Nkọwa zuru ezu nke nchịkọta data Big Data
  • Ngwa ule na-eji MR Unit na ngwaọrụ ndị ọzọ.
  • Na-arụ ọrụ na usoro Avro data
  • Gbalịa rụọ ọrụ na-eji Hadoop na Apache Spark
  • Kwadebe ka ị kpochapụ Ndepụta Data Big Data.

bu n'obi ege Ntị

  • Ndị mmepe mmepe na ndị nchịkwa System
  • Ndị ọkachamara na-arụ ọrụ ọkachamara, ndị ọrụ Ụlọ ọrụ
  • Ndị Na-emepụta Nchịkwa Data Na-agụsi agụụ ike ịmụta akụkụ ndị ọzọ dị ka Ule, Nchịkọta, Nchịkwa
  • Ndị isi ụlọ ọrụ, ndị na-agba akwụkwọ & ndị ọkachamara n'ule
  • Business Intelligence, Data warehousing na ọkachamara nchịkọta
  • Ndị gụsịrị akwụkwọ, undergraduates na-achọsi ike ịmụta ihe ọhụrụ Data technology nwere ike iji nnukwu Data Hadoop certification online ọzụzụ

Prerequisites

  • Enweghị ihe ọ bụla dị mkpa ị ga - eji nweta ọzụzụ data nnukwu a na nna nna Hadoop. Ma isi ihe nke UNIX, SQL na java ga-adị mma. Na Intellipaat, anyị na-enye nkwado unix na Java na ntinye akwụkwọ ntinye akwụkwọ data anyị dị ukwuu iji mee ka ọkpụkpụ a chọrọ iji mee ka ị dị mma n'ebe ị nọ.

Oge Nhazi Oge: 2 Days

Okwu Mmalite nke Data Ukwu na Hadoop na Ebumnobi ya, Map Reduce na HDFS

Kedu ihe bụ Data Big, Ebee ka Hadoop ruru na, Hadoop Distributed File System - Nkọwapụta, Ogwe Ubi, Namenode nke Abụọ, Elu Nweta, Nghọta ỊNỌ - ResourceManager, NodeManager, Ụdị dị n'etiti 1.x na 2.x

Ndozi elu na nhazi

Hadoop 2.x Cluster Architecture, Federation and High Disponibility, Nhazi Ụdị Nhazi ụyọkọ, Ụdị Ụyọkọ Mpịakọta, Ụdị Ngwá Ọrụ Gburugburu, Hadoop 2.x Nhazi faịlụ, Cloudera Single node cluster

Mmiri miri emi na Mapreduce

Kedu ka Mapreduce na-arụ ọrụ, Olee otú ọrụ dị ala si dị, Olee otú ọkwọ ụgbọala na-arụ ọrụ, ndị na-arụ ọrụ, ndị nchịkọta, ụzọ ntinye, usoro mmepụta, gbanwee na ụdị, mapide na-abata, ibelata akụkụ na-abanye, MRUnit,

Ihe eji eme ụlọ:

Na - arụ ọrụ na HDFS, Edekọ Okwu WordCount, Onye na - ede ederede ọdịnala, Mapreduce na Combiner, Map Side Jikọọ, Ibelata Akụkụ Na - Ejikọta, Ule Ule Mapreduce, Na-agba ọsọ Mapreduce na LocalJobRunner Mode

Ndozi nsogbu nsogbu

Kedu ihe bụ eserese, Nnọchiterese eserese, Achịcha mbụ Chọọ Algorithm, Eserese Ngosipụta nke Map Gbasaa, Esi mee Graph Algorithm, Ihe Nlereanya nke Foto Map Kwụsị,

    Nwee 1 mmega: Ngosipụta 2: Nwee 3 mmega:

Nkọwa zuru ezu banyere ezi

A. Okwu Mmalite nke Ezi

Ịghọta Apache Pig, atụmatụ, ụzọ dị iche iche na ịmụ iji soro Pig na-emekọ ihe

B. Na-eri nri ezi maka nyocha data

Nkọwa nke Latin Pig, nkọwa dịgasị iche, ụdị data na nza, ụdị data, na-eji Pig maka ETL, ntinye data, nlele nke atụmatụ, nkọwa nke ubi, ọrụ ndị a na-ejikarị eme ihe.

C. Pig maka nhazi data

Ụdị data dị iche iche gụnyere nested na mgbagwoju anya, nhazi data na ezi, nchịkọta oge nchịkọta data, mmega ahụ

D. Ịrụ ọrụ arụmọrụ multi-dataset

Ntọala data ejikọta, ịkọwa data, ụzọ dịgasị iche iche maka ijikọ data, ịtọ ntọala, mmega aka

E. Anwụrụ na-agbatị

Ịghọta njirimara ọrụ ndị ọrụ, ime nhazi data na asụsụ ndị ọzọ, mbubata na macros, na-eji gụgharia na UDF iji gbasaa ezi, mmezi omume

F. Pig Ọrụ

Na-arụ ọrụ na ihe ndekọ data dị na Walmart na Electronic Arts dị ka ihe ọmụmụ

Nkowa zuru oke banyere Hive

A. Ihe Ntuziaka

Ịghọta Hive, ihe ntanetị ntanetị na ntinye aka, Ntube na Hive, na ịchekwa data na Hive na Hive schema, Mkparịta ụka na ụdị dị iche iche nke Hive

B. Hive maka nyocha data njikọ

Ịghọta HiveQL, isi okwu dị iche iche, tebụl dị iche iche na ọdụ data, ụdị data, ntinye data, ọrụ dị iche iche a wuru na ya, na-etinye Hed queries na scripts, shell and Hue.

C. Njikwa data na Hive

Ọdịdị data dị iche iche, mmepụta nke ọdụ data, usoro data na Hive, ntinye data, Tebụl jikwaa, nchịkọta onwe ya tebụl, ntinye data, na-agbanwe data data na tebụl, nchịkọta ajụjụ na Echiche, na-eweta ịchekwa ajụjụ, nchịkwa nchịkwa data, ijikwa data na Hive, Hive Metastore na Thrift server.

D. Ọdịdị kachasị mma

Ịmụ ihe nke njụ-ajụjụ, ntinye aha data, nkewa na bucketing

E. Na-agafe

Ịkọ ọrụ onye ọrụ kọwaa ọrụ maka ịgbasa Hive

F. Aka na mmega - na-arụ ọrụ na nnukwu data data na nnukwu ajụjụ

Na-etinye Hive maka nnukwu ọnụọgụ nke data na nnukwu ajụjụ ịjụ

G. UDF, njikarịcha ajụjụ

Na-arụ ọrụ dị ukwuu site na Ntuziaka Onye Ntuziaka, na-amụta otú e si emelite ajụjụ, ụzọ dịgasị iche iche iji rụọ arụmọrụ.

Impala

A. Ihe omumu nke Impala

Gịnị bụ Impala, Olee otú Impala si esi na Hive na Pig, Olee otú Impala Differs sitere na Relational Databases, Limitations na Future Directions, Iji Impala Shell

B. Ịhọrọ Nke Kasị Mma (Mkpụrụ, Ezi, Impala)

C. Imezi na Ijikwa Data na Impala na Hive

Nchịkọta Nchekwa Data, Ịmepụta Databases na tebụl, Data Gburugburu na Tebụl, HCatalog, Impala Metadata Caching

D. Nwepụta data

Nchikota nke isi, Nwekota na Impala na Hive

(AVRO) Data Formats

Ịhọrọ usoro Nhazi, Nkwado Ngwá Ọrụ maka Ụdị Njikwa, Avro Schemas, Iji Avro na Hive na Sqoop, Evro Schema Evolution, Compression

Okwu Mmalite nke ụlọ Hbase

Kedu ihe bụ Hbase, ebee ka ọ dị, Gịnị bụ NOSQL

Apache Spark

A. Ntak emi ẹkedọhọde? Na-arụ ọrụ na Sistemụ Njikwa na Hadoop Distributed File System

Kedu ihe bụ ụja, Ntụkọrita dị n'etiti etu na ọkụ, ihe dị iche iche nke ọkụ

B. Ihe na-emepụta ihe, Algorithms na-emekarị ihe-Algorithms na-ekpo ọkụ, Nyocha nkọwa, Machine Ịmụ

Apache Spark- Okwu Mmalite, Ịnọgidesi ike, Nweta, Nkọwa, Unified Stack Spark, Na-emepụta ihe, Ihe nṅomi ihe atụ, mahout, storm, graph

C. Na-agba ọsọ na-agba ọsọ na ụyọkọ, na-edepụta ihe eji eji Python, Java, Scala

Kọwaa ihe gbasara ọdịiche, Gosi na ịwụnye ọkụ, Kọwaa ihe ọkwọ ụgbọala, Ịkọwapụta ihe na-eme ka ihe ntụgharị na ihe atụ, Kọwaa njedebe na-adịghị ike, Gwakọta egwu na java seamlessly, Kọwaa nkwenye na nkesa., Kọwaa ihe bụ àgwà, Kọwaa ọrụ dị elu karịa ọrụ, kọwaa OFI nhazi oge, Uru nke ịkụ ọkụ, Ihe atụ nke Lamda na-eji ụja, Kọwaa Mapreduce na ihe atụ

Mbepụta nchịkọta ụyọkọ na-agba ọsọ na-ebelata ọrụ

Nhazi ụyọkọ nke ụyọkọ site na iji Amazon ec2 - Ịmepụta 4 nhazi ụyọkọ ụyọkọ, Map Na-agba ọsọ Ibelata Ọrụ na Ụyọkọ

Nnukwu ọrụ - Na-etinye ya niile na Ijikọta ntụpọ

Na-etinye ya niile na Ijikọta ntụpọ, Na-arụ ọrụ na nnukwu data data, Nzọụkwụ gụnyere itinye nyocha data dị ukwuu

ETL Njikọ na Hadoop Edobe

Olee otú ETL ngwaọrụ na-arụ ọrụ na Big data Industry, Na-ejikọ na HDFS si ETL ngwá ọrụ na na-agbanwe data site na Mpaghara na HDFS, Data ntụgharị site DBMS na HDFS, Na-arụ ọrụ na Hive na ETL Ngwá Ọrụ, Ịmepụta Map Ịbelata ọrụ na ETL ngwá ọrụ, Ọgwụgwụ na Ọgwụgwụ ETL PoC na-egosi nnukwu data njikọta na ETL ngwá ọrụ.

Nhazi nchịkọta

Nhazi nhazi na mkpa nhazi faịlụ, Nhazi nhazi na ụkpụrụ, HDFS parameters MapReduce parameters, Hadoop gburugburu ebe obibi ntọala, 'Gụnyere' na 'wezụga' nhazi faịlụ, Lab: MapReduce arụmọrụ ntinye

Nchịkwa na Nlekọta

Nkọwa ndekọ faịlụ na ndekọ Datanode, faịlụ nchịkwa faịlụ na Dezie log, Usoro nchọpụta, Ndabere ndekọ aha na usoro mgbake, Ọnọdụ Nchekwa, Metadata na Data ndabere, Nsogbu nsogbu na ngwọta / ihe ịchọrọ maka, Na-agbakwụnye ma wepu nha, Ụlọ: FileReduce File usoro Iweghachite

Nlekota na Nchọpụta nsogbu

Omume kachasị nke nlekota ụyọkọ, Iji ederede na nchịkọta na-achọ maka nlekota na nhazi, Iji ihe ntinye oghere iji nyochaa ụyọkọ ụyọkọ

Atụmatụ Job: Map belata nsụgharị nrube ọrụ

Kedu otu esi echekwa oge ọrụ na otu ụyọkọ ahụ, usoro FIFO, ihe ngosi ziri ezi na nhazi ya

Nchịkọta Ụyọkọ Ụyọkọ Dị Iche Iche na Ijere Map Kwụsị Ọrụ na Amazon Ec2

Nhazi ụyọkọ nke ụyọkọ site na iji Amazon ec2 - Ịmepụta 4 nhazi ụyọkọ ụyọkọ, Map Na-agba ọsọ Ibelata Ọrụ na Ụyọkọ

ZOOKEEPER

ZOOKEEPER Okwu Mmalite, ZOOKEEPER na-eji okwu, ọrụ ZOOKEEPER, ZOOKEEPER data Model, Znodes na ụdị ya, arụmọrụ Znodes, Znodes ese, Znodes na-agụ ma na-ede, Njikọ aka, Nchịkọta ụyọkọ, Ntuziaka Onye Ndú, Oghere Na-ekpuchi Nanị, Isi ihe dị mkpa

Gaa n'ihu Oozie

Ihe mere Oozie ?, Ịwụnye Oozie, Na-agba ọsọ ihe atụ, engine ọrụ, Ihe nṅomi M / R, ihe atụ ọnụ ọgụgụ, ngwa ngwa ọrụ, ntinye nrubeisi, nsụgharị nsụgharị ọrụ, nhazi ọrụ Oozie, Oozie security, why Oozie security? , Multi tenancy na scalability, Usoro oge nke Oozie ọrụ, Onye nchịkwa, Ogwe, Ụdị nke abstraction, Nhazi, Jiri Azụmahịa 1: oge ​​na-ebute, Jiri Usoro 2: data na oge na-ebute, Jiri Ụdị 3: window windowing

Ọga n'ihu

Nkọwapụta nke Apache Flume, Nkesa data nke ọma, Ọdịdị mgbanwe nke Data, Anya nke ọma, Ọdịdị nke Flume, Isi ihe, Omume, Ndị ahịa, Ndị ọrụ, Isi, Ọwa, Sinks, Interceptors, Onye ntanetụ, Ntanye sink, Data ingest, , Nkwekọrịta data azụmahịa, Ntugharị ma na-emegharịghachi, Ntak-ọhụụ, Jiri ikpe- Njikọ agwakọta, Na-agbakwụnye onye na-emegharị ọkpụkpọ, Na-edozi ugbo nkesa, Ntanetị data,

Gaa n'ihu

MGBE a na - ebute ihe na - eme ka ị nweta ihe ọ bụla na - eme ka ọ bụrụ na ị na - ebufe data na Njikwa Nchọgharị ?, Gaa ọdịnaya, Njikọ ndị ọrụ, Integrating HDFS, Fundamentals of HUE FRONTEND

Ịga n'ihu Impala

IMPALA Nzuzo: Ebumnuche, Impala onye ọrụ: Isi, Impala nke onye ọrụ: SQL, njirimara nke Impala: Apache HBase, ụlọ obibi impala, ụlọ ahịa Impala, ọrụ ntanetị nke Impala, usoro mmegbu nke ajụjụ, tụnyere Impala ruo Hive

Nnwale Uleop Ule

Ihe mere nyocha bụ ihe dị mkpa, Nnwale nke otu, Nlekọta njikọrọ, Nyocha arụ ọrụ, Nyocha, Nyocha ule ehihie, Ihe nyocha na njedebe maka ule nkwụsị, Nlereanya arụ ọrụ, Nlereanya nke nnyefe nkwenye, Nlereanya nche, Nlebanye ule, Nhazi na Decommissioning nke Nodes Nyocha, Nyocha , Nnwale nnwale

Ọrụ na ọrụ nke Hadoop Testing Professional

Ịghọta Ihe Ọchịchọ, nkwadebe maka Atụmatụ Nlereanya, Ule Ule, Ule Nyocha, Ule ihe nyocha, Ule Mkpu, Mmebi Reporting, Defect Retest, Daily Report Report, Nyocha arụcha, ETL ule na ọ bụla ogbo (HDFS, HIVE, HBASE) edebanye ihe ndenye (ndekọ / faịlụ / ederede etc.) iji sqoop / flume na-agụnye ma ọ bụghị njedebe na nkwenye data, nkwekọrịta, ikike njirimara na ule nyocha (otu dị iche iche, ọrụ, ihe nrite, wdg.), ha ka ha mechie, Dụchaa ihe ọ bụla ma mebie nkwarụ, Na-akwado ihe ọhụrụ na mbipụta na Core Hadoop.

Okpokoro a na-akpọ MR Unit maka Ule nke Map-Belata Mmemme

Dezie nsogbu na onye ọrụ mmepe ma ọ bụ njikwa ma na-amanye ha ka ha mechie, Debe nsogbu niile ma mebie nkwarụ, Na-arụ ọrụ maka ịmepụta nyocha nke a na-akpọ MR Unit maka ule nke Mmemme-ebelata.

Nnwale Ule

Ihe nyocha nke nnwale site na iji OOZIE, Nyocha data site na iji ngwa nyocha ajụjụ.

Nnyocha nke ule

Atụle ule maka HDFS nkwalite, Ule ule na nsonaazụ

Nyocha Atụmatụ na edere Ule Elee maka ule Hadoop Ngwa

Otu esi anwale ntinye ma hazie

Job na Nkwado Akaụntụ

Ntuziaka na ntuziaka ndị gbasara imepụta ihe na ntinye aka na ntinye mkparịta ụka, Atụmatụ na Atụmatụ bara uru

Biko dee anyị na info@itstechschool.com & kpọtụrụ anyị na 91-9870480053 maka ọnụahịa ego & akwụkwọ nkwụ ụgwọ, oge na ọnọdụ

Wepụ Anyị Ajụjụ

Ezubere ọzụzụ ọzụzụ a iji nyere gị aka ikpochapụ ma Ngwakọta na-emepụta ihe na Nchekwa Onye Mmepụta (CCA175) nyocha na Onye na-ahụ maka akwụkwọ nlekọta nke Apache Hadoop (CCAH) ule. Ihe omumu ọzụzụ zuru oke na usoro mmemme abuo a ma nyere gi aka ikpochapu ule akwukwo ndi a n 'ike ma nweta oru kachasi elu n'ime MNC.

Dị ka akụkụ nke ọzụzụ a, ị ga-arụ ọrụ na oge ọrụ na ọrụ ndị nwere nnukwu ihe ọ pụtara na ezigbo ụlọ ọrụ ụwa nke dị otú a na-enyere gị aka iji ọsọ na-arụ ọrụ gị.

Ná ngwụsị nke usoro ọzụzụ a, a ga-enwe nyocha nke zuru oke na-egosipụta ụdị ajụjụ ndị a jụrụ na nyocha dịgasị iche iche nke akaebe ma nyere gị akara akara kacha mma na nyocha nyocha.

AKWỤKWỌ ACHỤKWỤKWỤKWỌ ACHỤKỌ NKE A a ga-enye onyinye na mmezu nke ọrụ Ụlọ ọrụ (na nyochaa ndị ọkachamara) na n'elu isi nke ma ọ dịkarịa ala 60% akara na ajụjụ ahụ. A na-enyocha akwụkwọ ntinye aka na 80 + MNC dị ka Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Ụkpụrụ Iwu, TCS, Genpact, Hexaware, wdg.

Maka ozi ndị ọzọ Kpọtụrụ anyị.


Nyocha