iruIkẹkọ ikoko
Forukọsilẹ

Pe wa

Awọn aaye ti a samisi pẹlu ohun * a ti beere

 

Ilana nla iforukọsilẹ data nla

Big Data Idaabobo Awọn itọnisọna Dajudaju & Ikẹkọ

Akopọ

Ipe & Awọn ẹri

Ilana Akoso

Iṣeto & Owo

iwe eri

Big Data Idaabobo Iwe-aṣẹ Lakopọ Akopọ

O jẹ ilọsiwaju ikẹkọ data ti Hadoop Big Data ti a ṣe nipasẹ awọn amoye ile-iṣẹ lati mọ awọn iṣẹ iṣẹ ile-iṣẹ lọwọlọwọ lati pese imọ-inu-jinlẹ lori data nla ati awọn modulu Hadoop. Eyi jẹ ile-iṣẹ kan ti o ni imọran iwe-ẹkọ imọ-nla ti Big Data ti o jẹ apapo awọn ikẹkọ ikẹkọ ni Olùgbéejáde Hadoop, Olutọju Hadoop, igbeyewo Hadoop, ati awọn atupale. Eyi Cloudera Ikẹkọ Hadoop yoo ṣetan ọ lati nu iwe-aṣẹ nla data.

afojusun

  • Awọn orisun pataki ti Hadoop 2.7 ati YARN ati kọ awọn ohun elo nipa lilo wọn
  • Ṣiṣeto ipade ti nwọle ati Ọpa ti nmu ti opo lori Amazon EC2
  • HDFS Master, MapReduce, Hive, Pig, Oozie, Sqoop, Flume, Zookeeper, HBase
  • Kọ sipaki, Spark RDD, Graphx, Iwe MLlib kikọ Awọn ohun elo turari
  • Awọn iṣẹ iṣakoso Hadoop gẹgẹbi iṣakoso iṣakoso, abojuto, isakoso ati laasigbotitusita
  • Ṣiṣeto awọn ohun elo ETL bi Pentaho / Talend lati ṣiṣẹ pẹlu MapReduce, Hive, Ẹlẹdẹ, ati be be lo
  • Iyeyeye alaye ti Awọn atupọ Big Data
  • Ṣiṣe ayẹwo awọn ohun elo nipa lilo MR Unit ati awọn irinṣẹ irinṣẹ miiran.
  • Ṣiṣe pẹlu awọn ọna kika data Avro
  • Ṣiṣe awọn iṣẹ-ṣiṣe gidi-aye pẹlu lilo Hadoop ati Apagun Spani
  • Jẹ ipese lati ṣafihan awọn ẹri Big Data Hadoop.

ti a ti pinnu jepe

  • Awọn Alagbatọ Awọn Itọsọna ati Awọn Alakoso System
  • Awọn oniṣẹ iṣẹ iriri, Awọn alakoso ise
  • Awọn Aṣekoro nla DataHadoop Awọn ayanfẹ ni itara lati kọ awọn idiwọn miiran gẹgẹbi Igbeyewo, Atupale, Isakoso
  • Awọn akosemoṣe Pataki, Awọn ayaworan ile ati Awọn akosemoye idanwo
  • Imọye Iṣowo, Data warehousing ati awọn Oṣiṣẹ igbasilẹ
  • Awọn ile-iwe giga, awọn akọkọ ti ko ni iwe-ẹkọ ti o ni itara lati kọ ẹkọ titun Imọ-ẹrọ nla data le gba ikẹkọ iṣeduro Ayelujara yii ni Big Data Hadoop certification

Prerequisites

  • Ko si ibere-tẹlẹ lati ya ikẹkọ data nla yii ati lati ṣe olori Hadoop. Ṣugbọn awọn ipilẹ ti UNIX, SQL ati java yoo dara. Ni Intellipaat, a pese itọnisọna unix ati ilana Java pẹlu imọṣẹ iwe-ẹri nla ti wa ni Big Data lati ṣafẹri awọn ogbon ti o nilo lati jẹ ki o dara si ọ Hadoop eko ẹkọ.

Akokọ Akoko Iye: Ọjọ 2

Ifihan si Awọn Alaye nla ati Idagbasoke ati awọn Ekoro-ori rẹ, Map Din ati HDFS

Kini Data nla, Nibo ni Hadoop ti yẹ si, Hadoop Pipin System File - Awọn ilojade, Iwọn Block, Namenode ti ile-iwe, Ipilẹ to gaju, Imọye YARN - ResourceManager, NodeManager, Iyatọ laarin 1.x ati 2.x

Fifi sori Hadoop & setup

Hadoop 2.x Cluster Architecture, Federation and High Highlight, Awọn ilana Itoju Oro Ipilẹ, Awọn Ipapọ Ipapọ Awọn Iwọn, Awọn Ipapọ Iwọn Awọn Igbẹhin Ipa, Hadoop 2.x Awọn iṣeto ni Awọn faili, Cloudera Nilu ipade kan ṣoṣo

Dive Dive ni Mapreduce

Bawo ni Ṣiṣẹlẹ Mapẹ, Bawo Awọn iṣẹ irẹwẹsi, Bawo ni Ṣiṣẹ Awakọ, Awọn apẹrẹ, Awọn oludari, Awọn ọna Input, Awọn Apẹrẹ Ṣiṣejade, Ṣiṣẹpọ ati Lẹtọ, Awọn Mapide Ṣepọ, Din Agbegbe Wọpọ, MRUnit, Kaṣe Pipin

Awọn iṣẹ-inu ile:

Ṣiṣẹ pẹlu HDFS, Eto kikọ WordCount, Ṣiṣẹ onisẹ aṣa, Ṣafihan pẹlu Olugbepo, Bọtini Map Daapọ, Din ẹgbẹ wa, Ẹrọ Agbegbe Ibẹrẹ, Ṣiṣe Ilẹ-ilẹ ni Ipo LocalJobRunner

Ṣiṣe Isoro Isoro

Kini Eya, Asoju Aworan, Akaraka akọkọ Ṣawari Algorithm, Aṣoju Aworan ti Map Dinku, Bawo ni lati ṣe Algorithm aworan, Apẹẹrẹ ti Awọn aworan Map Ṣiwọn,

    Idaraya 1: 2 Idaraya: Idaraya 3:

Iyeyeye alaye ti Ẹlẹdẹ

A. Ifihan si Ẹlẹdẹ

Mimọ Pig ti o ti npa, awọn ẹya ara ẹrọ, awọn oriṣiriṣi awọn ipawo ati ẹkọ lati ṣe alabapin pẹlu Ẹlẹdẹ

B. Ti n ṣafẹjẹ Ẹlẹdẹ fun onínọmbà data

Sisọpọ ti Latin Pig, awọn asọye ti o yatọ, tito data ati idanimọ, awọn iru data, gbigbe Pig fun ETL, ikojọpọ data, wiwo wiwo simi, awọn itumọ aaye, awọn iṣẹ ti a lo.

K. Ẹlẹdẹ fun itọju data pataki

Orisirisi awọn iru data pẹlu ohun ti o wa ni idasilo ati idiyele, data ṣiṣe pẹlu Ẹlẹdẹ, ṣajọpọ itọnisọna data, idaraya ṣiṣe

D. Ṣiṣe awọn iṣiro-ọpọlọ awọn iṣẹ

Ṣiṣeto data ṣeto didopọ, pinpin data, awọn ọna oriṣiriṣi fun ṣeto data ṣeto, awọn iṣẹ ṣeto, iṣẹ idaraya

E. Ẹlẹdẹ to pọ

Agbọye ti awọn olumulo ṣe awọn iṣẹ, ṣiṣe awọn data data pẹlu awọn ede miiran, awọn ikọja ati awọn macros, lilo sisanwọle ati awọn UDF lati fa Pig, awọn adaṣe ṣiṣe

F. Iṣẹ Ẹlẹdẹ

Ṣiṣẹ pẹlu awọn data gangan ti o wa pẹlu Walmart ati Ẹrọ Itanna gẹgẹbi iwadi iwadi

Iyeyeye alaye ti Hive

A. Hive Ifihan

Imọye Hive, data ipilẹ ti aṣa pẹlu Ipa, Ẹlẹdẹ ati Hive lafiwe, titoju awọn data ni Hive ati Hma schema, Ibaramu ibaraẹnisọrọ ati awọn lilo orisirisi lilo ti Hive

B. Hive fun iyasọtọ data ibatan

Miiye HiveQL, ipilẹ agbekalẹ, awọn tabili oriṣiriṣi ati awọn apoti isura infomesonu, awọn oniru data, ṣeto awọn data, awọn iṣẹ-ṣiṣe ti o yatọ, ti n ṣawari awọn ibeere lori awọn iwe afọwọkọ, ikarahun ati Hue.

K. Isakoso data pẹlu Hive

Awọn apoti ipamọ data orisirisi, ẹda ti awọn apoti isura infomesonu, awọn ọna kika data ni Hive, awoṣe data, Awọn tabili iṣakoso, awọn iṣakoso ti iṣakoso ara, awọn ikojọpọ data, iyipada awọn apoti isura data ati Awọn tabili, simplification ìbéèrè pẹlu Awọn iwo, iṣeduro titoju ti awọn ibeere, iṣakoso wiwọle data, pẹlu Hive, Hive Metastore ati olupin Thrift.

D. Ti o dara ju ti Hive

Išẹ ẹkọ ti ìbéèrè, titọka data, ipinpa ati bucketing

E. Afikun ti o ti kọja

Deploying oluṣe aṣaṣe awọn iṣẹ fun sisọ Hive

F. Ọwọ lori Awọn adaṣe - ṣiṣẹ pẹlu awọn ipilẹ data nla ati imọran to pọju

Hive Deproying fun awọn ipele nla ti awọn alaye data ati oye nla ti querying

G. UDF, ibeere ti o dara julọ

Ṣiṣẹ ni pipọ pẹlu Awọn ibeere ti a Ṣawari Awọn Olumulo, imọ bi o ṣe le ṣe awọn ibeere, awọn ọna oriṣiriṣi lati ṣe atunṣe išẹ.

Impala

A. Ifihan to Impala

Kini Impala ?, Bawo ni Impala ṣe npa lati Hive ati Pig, Bawo Impala ṣe ṣafihan lati Awọn Ibuwe Isopọ Iṣọkan, Awọn idiwọn ati Awọn itọnisọna ojo iwaju, Lilo Ipa Impala

B. Yan Ti o dara julọ (Hive, Pig, Impala)

K. Ṣatunṣe ati Ṣiṣakoṣo awọn alaye pẹlu Impala ati Hive

Ibi ipamọ Akopọ Data, Ṣiṣẹda awọn apoti isura infomesonu ati awọn tabili, Data imudani sinu awọn tabili, HCatalog, Impala Metadata Caching

D. Ipilẹ Alaye

Ipilẹ Akopọ, Ipilẹ ni Impala ati Hive

(AVRO) Awọn ọna kika Data

Yiyan faili kika, atilẹyin ọja fun awọn faili Fọọmu, Schemas Afro, Lilo Avro pẹlu Hive ati Sqoop, Evolution Schema Evolution, Compression

Ifihan si ile-iṣẹ Hbase

Kini Hbase, Nibo ni o ṣe yẹ, Kini NOSQL

Agbejade Afun

Idahun. Ṣiṣẹ pẹlu Ọpa Ẹka ati Hadoop Pipin System Oluṣakoso

Kini isokuro, Ifiwejuwe laarin awọn sipaki ati Hadoop, Awọn ohun elo ti sipaki

B. Awọn ohun elo ti a fi han, Awọn Alugoridimu ti o wọpọ-Awọn Alugoridimu Imọ, Awọn Iṣiro Ẹya, Ẹkọ ẹrọ

Afẹkọ Apache- Ifihan, Imọlẹ, Wiwa, Ipinya, Ikọlẹ Atokọ ti Ajọpọ, Awọn ohun-elo Spark, Apẹẹrẹ iboju, mahout, storm, graph

K. Ṣiṣafo Iwoye lori Isupọ, Fifiranṣẹ Awọn Ifilo Awọn ohun elo nipa lilo Python, Java, Scala

Ṣe alaye apẹrẹ apani, Ṣiṣe fifi fifiranṣẹ kan, Ṣagbekale itọsọna awakọ, Ṣafihan ipo itumọ pẹlu apẹẹrẹ, Ṣatunkọ iyipada ti ko ni agbara, Darapọ scala ati java seamlessly, Ṣawari otitọ ati pinpin, Ṣafihan ohun ti o jẹ ẹya, Ṣapejuwe iṣẹ-ṣiṣe ti o ga julọ pẹlu apẹẹrẹ, Ṣeto Ifiranṣẹ scheduler, Awọn anfani ti sipaki, Apẹẹrẹ ti Lamda lilo sipaki, Ṣatunkọ Mapreduce pẹlu apẹẹrẹ

Hadoop Cluster Setup and Running Map Reduce Jobs

Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup, Running Map Reduce Jobs on Cluster

Major Project – Putting it all together and Connecting Dots

Putting it all together and Connecting Dots, Working with Large data sets, Steps involved in analyzing large data

ETL Connectivity with Hadoop Ecosystem

How ETL tools work in Big data Industry, Connecting to HDFS from ETL tool and moving data from Local system to HDFS, Moving Data from DBMS to HDFS, Working with Hive with ETL Tool, Creating Map Reduce job in ETL tool, End to End ETL PoC showing big data integration with ETL tool.

Cluster Configuration

Configuration overview and important configuration file, Configuration parameters and values, HDFS parameters MapReduce parameters, Hadoop environment setup, ‘Include’ and ‘Exclude’ configuration files, Lab: MapReduce Performance Tuning

Administration and Maintenance

Namenode/Datanode directory structures and files, File system image and Edit log, The Checkpoint Procedure, Namenode failure and recovery procedure, Safe Mode, Metadata and Data backup, Potential problems and solutions / what to look for, Adding and removing nodes, Lab: MapReduce File system Recovery

Monitoring and Troubleshooting

Best practices of monitoring a cluster, Using logs and stack traces for monitoring and troubleshooting, Using open-source tools to monitor the cluster

Job Scheduler: Map reduce job submission flow

How to schedule Jobs on the same cluster, FIFO Schedule, Fair Scheduler and its configuration

Multi Node Cluster Setup and Running Map Reduce Jobs on Amazon Ec2

Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup, Running Map Reduce Jobs on Cluster

ZOOKEEPER

ZOOKEEPER Introduction, ZOOKEEPER use cases, ZOOKEEPER Services, ZOOKEEPER data Model, Znodes and its types, Znodes operations, Znodes watches, Znodes reads and writes, Consistency Guarantees, Cluster management, Leader Election, Distributed Exclusive Lock, Important points

Advance Oozie

Why Oozie?, Installing Oozie, Running an example, Oozie- workflow engine, Example M/R action, Word count example, Workflow application, Workflow submission, Workflow state transitions, Oozie job processing, Oozie security, Why Oozie security?, Job submission, Multi tenancy and scalability, Time line of Oozie job, Coordinator, Bundle, Layers of abstraction, Architecture, Use Case 1: time triggers, Use Case 2: data and time triggers, Use Case 3: rolling window

Advance Flume

Overview of Apache Flume, Physically distributed Data sources, Changing structure of Data, Closer look, Anatomy of Flume, Core concepts, Event, Clients, Agents, Source, Channels, Sinks, Interceptors, Channel selector, Sink processor, Data ingest, Agent pipeline, Transactional data exchange, Routing and replicating, Why channels?, Use case- Log aggregation, Adding flume agent, Handling a server farm, Data volume per agent, Example describing a single node flume deployment

Advance HUE

HUE introduction, HUE ecosystem, What is HUE?, HUE real world view, Advantages of HUE, How to upload data in File Browser?, View the content, Integrating users, Integrating HDFS, Fundamentals of HUE FRONTEND

Advance Impala

IMPALA Overview: Goals, User view of Impala: Overview, User view of Impala: SQL, User view of Impala: Apache HBase, Impala architecture, Impala state store, Impala catalogue service, Query execution phases, Comparing Impala to Hive

Hadoop Application Testing

Why testing is important, Unit testing, Integration testing, Performance testing, Diagnostics, Nightly QA test, Benchmark and end to end tests, Functional testing, Release certification testing, Security testing, Scalability Testing, Commissioning and Decommissioning of Data Nodes Testing, Reliability testing, Release testing

Roles and Responsibilities of Hadoop Testing Professional

Understanding the Requirement, preparation of the Testing Estimation, Test Cases, Test Data, Test bed creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report delivery, Test completion, ETL testing at every stage (HDFS, HIVE, HBASE) while loading the input (logs/files/records etc) using sqoop/flume which includes but not limited to data verification, Reconciliation, User Authorization and Authentication testing (Groups, Users, Privileges etc), Report defects to the development team or manager and driving them to closure, Consolidate all the defects and create defect reports, Validating new feature and issues in Core Hadoop.

Framework called MR Unit for Testing of Map-Reduce Programs

Report defects to the development team or manager and driving them to closure, Consolidate all the defects and create defect reports, Responsible for creating a testing Framework called MR Unit for testing of Map-Reduce programs.

Unit Igbeyewo

Automation testing using the OOZIE, Data validation using the query surge tool.

Igbeyewo idanwo

Test plan for HDFS upgrade, Test automation and result

Test Plan Strategy and writing Test Cases for testing Hadoop Application

How to test install and configure

Job and Certification Support

Cloudera Certification Tips and Guidance and Mock Interview Preparation, Practical Development Tips and Techniques

Jọwọ kọ si wa ni info@itstechschool.com & kan si wa ni + 91-9870480053 fun iye owo iye-owo & iwe eri eri, iṣeto & ipo

Mu Wa Iwadi Kan

This training course is designed to help you clear both Cloudera Spark and Hadoop Developer Certification (CCA175) idanwo ati Cloudera Certified Administrator for Apache Hadoop (CCAH) exam. The entire training course content is in line with these two certification programs and helps you clear these certification exams with ease and get the best jobs in the top MNCs.

As part of this training you will be working on real time projects and assignments that have immense implications in the real world industry scenario thus helping you fast track your career effortlessly.

At the end of this training program there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and helps you score better marks in certification exam.

ITS Course Completion Certificate will be awarded on the completion of Project work (on expert review) and upon scoring of at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.

Fun alaye diẹ ẹ sii daradara Pe wa.


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