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kursus sertifikasi hadoop besar

Kursus Sertifikasi Big Data Hadoop & Training

Ringkesan

Penonton & Prasyarat

Course Outline

Jadwal & Bayaran

Certification

Gambaran Umum Sertifikasi Big Data Hadoop

Iku kursus pelatihan Hadoop Big Data komprehensif sing dirancang dening ahli industri considering syarat-syarat proyek industri saiki kanggo nyedhiyani sinau jero ing data amba lan Modul Hadoop. Iki minangka industri pelatihan sertifikasi Big Data sing diakoni minangka gabungan saka kursus latihan ing pangembang Hadoop, administrator Hadoop, pengujian Hadoop, lan analytics. Iki Cloudera Latihan Hadoop bakal nyiyapake sampeyan mbusak sertifikasi data gedhe.

dislametaké

  • Dasar fundamental Hadoop 2.7 dan YARN dan menulis aplikasi menggunakannya
  • Nyetel pseudo node lan Multi node cluster ing Amazon EC2
  • Master HDFS, MapReduce, Hive, Babi, Oozie, Sqoop, Flume, Zookeeper, HBase
  • Sinau Spark, Spark RDD, Graphx, Aplikasi nulis MLL Spark
  • Aktivitas administrasi Master Hadoop kaya klompok ngatur, ngawasi, ngatur lan ngatasi masalah
  • Configuring ETL tools kaya Pentaho / Talend kanggo nggarap MapReduce, Hive, Babi, dsb
  • Pemahaman sing rinci babagan analytics Big Data
  • Aplikasi testing Hadoop nggunakake MR Unit lan piranti otomatis liyane.
  • Bisa nganggo format data Avro
  • Praktik proyek nyata nggunakake Hadoop lan Apache Spark
  • Dilengkapi kanggo mbusak Big Data Hadoop Certification.

dimaksudaké pirsawan

  • Pangembang Program lan Sistem Administrator
  • Profesional digunakake pengalaman, Manajer proyek
  • Big DataHadoop Developers semangat kanggo sinau vertikal liyane kaya Testing, Analytics, Administrasi
  • Mainframe Profesional, Arsitek & Profesional Penguji
  • Business Intelligence, Data Warehousing lan Profesional Analytics
  • Sarjana, sarjana sing kepéngin sinau teknologi Big Data paling anyar sing bisa njupuk latihan online Big Certification Hadoop iki

Prasyarat

  • Ora ana pre-requisite kanggo njupuk latihan data Big iki lan kanggo master Hadoop. Nanging, basis UNIX, SQL, lan jawa bakal apik. Ing Intellipaat, kita nyedhiyakake unix lan jurusan Jawa kanthi latihan sertifikasi Big Data kanggo nyediakake skills sing dibutuhake supaya sampeyan apik ing dalan pembelajaran Hadoop.

Course Outline Duration: 2 Days

Pambuka kanggo Big Data & Hadoop lan Ekosistem, Peta Ngurangi lan HDFS

Apa Big Data, Endi Hadoop cocok, Sistem File Hadoop - Replikasi, Ukuran Blok, Namenode Sekunder, Ketersediaan Dhuwur, Ngerti YARN - ResourceManager, NodeManager, Beda antarane 1.x lan 2.x

Instalasi & persediaan Hadoop

Hadoop 2.x Arsitektur Cluster, Federasi lan Ketersediaan Dhuwur, Persiyapan Cluster Produksi Khas, Modul Cluster Hadoop, Perintah Shell Hadoop Umum, Hadoop 2.x Configuration Files, Cloudera Single cluster node

Deep Dive in Mapreduce

Cara Nggawe Mapreduce, Cara Ngurangi Cara, Cara Nggawe Driver, Gabungan, Partisi, Format Input, Format Output, Shuffle lan Urut, Ngombe Sisir, Ngurangi Sisi Ngombe, MRUnit, Didistribusikan Cache

Latihan Lab:

Nggarap HDFS, Nggawe WordCount Program, Nggawe partisi khusus, Nggawe bareng Combbiner, Nggabungake Peta Peta, Ngurangi Sisi Nggabung, Unit Pangujian Mapreduce, Mundhut Mlumpat ing LocalJobRunner Mode

Pemecahan Masalah Graph

Apa Graph, Representasi Graf, Breadth Algoritma Panelusuran, Graph Representasi Peta Ngurangi, Carane Nggawe Algoritma Graf, Conto Peta Grafik Ngurangi,

    Latihan 1: Ngleksanani 2: Ngleksanani 3:

Pemahaman sing rinci babi

A. Pambuka Babi

Ngerteni Apache Pig, fitur, macem-macem migunakake lan sinau kanggo sesambungan karo Babi

B. Deploying Babi kanggo analisis data

Sintaks saka Pig Latin, macem-macem definisi, sort lan filter data, tipe data, deploying Pig kanggo ETL, data ngemot, tampilan skema, definisi lapangan, fungsi sing umum digunakake.

C. Babi kanggo pangolahan data komplèks

Jinis data macem-macem kalebu data nested lan kompleks, data pangolahan karo Babi, pengulangan data kelompok, latihan praktis

D. Performing multi-dataset operations

Data disambungake, pamisah data, macem-macem cara kanggo ngisi data, ngeset operasi, latihan tangan

E. Ngembangake Babi

Ngerteni fungsi sing wis ditemtokake pengguna, ngolah data nganggo basa liyane, impor lan makro, nggunakake streaming lan UDFs kanggo ngluwihi Babi, latihan praktis

F. Pig Jobs

Nganggo data-data nyata sing nglibatake Walmart lan Electronic Arts minangka studi kasus

Pemahaman sing rinci babagan Hive

A. Pambuka Hive

Understanding Hive, perbandingan basis tradisional karo Hive, Babi lan Hive, nyimpen data ing Hive and Hive schema, Interactive Hive lan macem-macem kasus nggunakake Hive

B. Hive kanggo analisis data hubungan

Ngerteni HiveQL, sintaks dhasar, tabel lan database sing macem-macem, jinis data, kumpulan data sing disambung, macem-macem fungsi sing diwangun, nyithak pitakon Hive ing skrip, shell lan Hue.

C. Manajemen data kanthi Hive

Sawetara basis data, nggawe database, format data ing Hive, pemodelan data, Tabel Hive-managed, Tabel dhata ngatur, ngowahi data, ngganti database lan Tabel, ndandani simplifikasi karo Tampilan, asil nyimpen pitakon, kontrol akses data, ngatur data kanthi Hive, Hive Metastore lan Thrift server.

D. Optimisasi Hive

Kinerja learning query, indeksasi data, partisi lan bucketing

E. Ngembangake Hive

Nganggo fungsi didefinisikan pengguna kanggo ndawakake Hive

F. Tangan ing Pakaryan - nggarap data gedhe lan pitakon lengkap

Deploying Hive kanggo volume data gedhe lan jumlah query sing akeh

G. UDF, optimisasi query

Makarya kanthi cetha karo Pit sing Ditetepake Panganggo, belajar cara ngoptimalake pitakon, macem-macem cara kanggo nindakake tuning kinerja.

Impala

A. Pengantar Impala

Apa Impala ?, Carane Impala Béda saka Hive lan Babi, Carane Impala Béda saka Database, Watesan lan Arah Future, Nggunakake Impala Shell

B. Milih Best (Hive, Babi, Impala)

C. Modeling lan Ngatur Data kanthi Impala lan Hive

Ringkesan Panyimpenan Data, Nggawe Database lan Tabel, Ngunggah Data menyang Tabel, HCatalog, Impala Metadata Caching

D. Pemisahan data

Ikhtisar Pemisahan, Pemisahan ing Impala lan Hive

(AVRO) Format Data

Milih Format Gambar, Alat Dhukungan kanggo Format Gambar, Skema Avro, Nggunakake Avro karo Hive lan Sqoop, Avro Schema Evolution, Komprèsi

Pambangunan arsitektur Hbase

Apa sing dimaksud Hbase, ngendi iku cocok, Apa NOSQL

Apache Spark

A. Apa Spark? Nggarap Sistem Gambar Spark lan Hadoop Didistribusikan

Apa Spark, Perbandhingan antarane Spark lan Hadoop, Komponen Spark

B. Spark Komponen, Algoritma Spark Umum-Algoritma Iteratif, Analisis Grafik, Mesin Belajar

Apache Spark- Pangenalan, Konsistensi, Kasedhiyan, Pemisahan, Spread Stack Unified, Komponen Spark, Conto Scalding, mahout, badai, grafik

C. Spark mlaku ing Klaster, Ngarang Spark Aplikasi nggunakake Python, Jawa, Scala

Nerangake conto python, Nuduhake instalasi spark, Nerangake program driver, Nerangake konteks spark karo conto, Netepake variabel sing diketik banget, Gabung scala lan java seamlessly, Nerangake concurrency lan distribusi., Nerangake apa sifat, Nerangake fungsi order sing luwih dhuwur karo conto, Tentukan OFI scheduler, Advantages of Spark, Conto Lamda nggunakake percikan, Nerangake Mapreduce nganggo conto

Hadoop Cluster Setup lan Running Peta Ngurangi Proyek

Multi Node Cluster Setup nggunakake Amazon ec2 - Nggawe 4 node cluster setup, Running Map Ngurangi Proyek ing Cluster

Project Major - Putting kabeh bebarengan lan Connecting Dots

Numpukake kabeh lan Nyambungake Dots, Nggarap Set data gedhe, Langkah-langkah sing digunakake kanggo nganalisa data gedhe

ETL Konektivitas karo Ekosistem Hadoop

Piranti ETL bisa dianggo ing Industri data Big, Nyambungake menyang HDFS saka alat ETL lan ngalih data saka Sistem lokal kanggo HDFS, Ngalih Data saka DBMS kanggo HDFS, Nggarap Hive karo Alat ETL, Nggawe Peta Ngurangi proyek ing alat ETL, Pungkasan nganti Akhir ETL PoC nuduhake integrasi data amba karo alat ETL.

Konfigurasi Cluster

Ringkesan konfigurasi lan file konfigurasi penting, Parameter lan nilai konfigurasi, paramèter HDFS MapReduce paramèter, Konfigurasi lingkungan Hadoop, 'Sertakan' lan 'Ngilangi' berkas konfigurasi, Lab: MapReduce Performance Tuning

Administrasi lan Pemeliharaan

Namenode / Datanode struktur lan file direktori, Gambar sistem gambar lan Log pangowahan, Prosedur Checkpoint, Kegagalan Namenode lan prosedur pemulihan, Mode Aman, Metadata lan Data cadangan, Potensi masalah lan solusi / apa sing kudu digoleki, Nambah lan ngilangi simpul, Lab: MapReduce File System Recovery

Pemantauan lan Ngatasi masalah

Praktik paling apik kanggo ngawasi cluster, Nganggo log lan tumpukan jejere kanggo ngawasi lan ngatasi masalah, Nggunakake alat sumber terbuka kanggo ngawasi cluster

Job Scheduler: Peta ngurangi aliran pengajuan proyek

Cara nggawe jadwal ing klompok padha, Jadwal FIFO, Penjadwal Fair lan konfigurasi

Multi Node Cluster Setup lan Running Map Ngurangi Jobs ing Amazon Ec2

Multi Node Cluster Setup nggunakake Amazon ec2 - Nggawe 4 node cluster setup, Running Map Ngurangi Proyek ing Cluster

ZOOKEEPER

ZOOKEEPER Pambuka, ZOOKEEPER nggunakake kasus, ZOOKEEPER Services, ZOOKEEPER Model data, Znodes lan jinisnya, Operasi Znodes, Znodes Watches, Znodes maca lan nyerat, Jaminan Konsistensi, Manajemen Cluster, Pemimpin Pemilihan, Distribusi Eksklusif Kunci, Poin penting

Nggawe Oozie

Apa Oozie ?, Nginstal Oozie, Running conto, Oozie- workflow engine, Conto M / R aksi, Conto count conto, Workflow application, Workflow submission, Workflow state transitions, Oozie job processing, Oozie security, Why Oozie security ?, Job submission , Tenaga kerja lan skalabilitas, Wektu wektu kerja Oozie, Koordinator, Bundel, Lapisan abstraksi, Arsitektur, Gunakake Casi 1: wektu memicu, Gunakake Casi 2: data lan wektu memicu, Gunakake Case 3: rolling window

Urip mlebu

Ringkesan Apache Flume, Sumber data, Ngubah Struktur Data, Nggoleki sing luwih cedhak, Anatomi Flume, Konsep Inti, Acara, Klien, Agen, Sumber, Saluran, Sinks, Interceptor, Selector Channel, Prosesor Sink, Data ingest, pipa Agen , Exchange data transaksional, Routing lan replicating, Apa saluran ?, Gunakake case- Log aggregation, Nambahake agent flume, Ngontrol farm server, Volume data per agent, Conto njlentrehake penyebaran simpul node siji

Nuwun

HUE perkenalan, ekosistem HUE, Apa HUE ?, HUE tampilan donya, Keuntungan HUE, Cara ngunggah data ing File Browser ?, Deleng isi, Nggabungake pangguna, Nggabungake HDFS, Dasar-Dasar Hue FRONTEND

Advance Impala

IMPALA Ikhtisar: Sasaran, Tampilan panganggo saka Impala: Ikhtisar, Tampilan panganggo saka Impala: SQL, Tampilan panganggo Impala: Apache HBase, Arsitektur Impala, Impala negara nyimpen, Layanan katalog Impala, Tahap eksekusi query, Nyakup Impala kanggo Hive

Testing Aplikasi Hadoop

Apa testing iku penting, Unit penguji, Pangujian Integrasi, Pengujian Kinerja, Diagnostik, Test QA Nightly, Tes standar lan end to end, Penguji fungsional, Test sertifikasi rilis, Penguji keamanan, Pengujian skalabilitas, Komisi lan Decommissioning Data Nodes Testing, Test reliability , Test testing

Peran lan Tanggung Jawab Hadoop Testing Professional

Pengertian Prasyarat, Preparation of the Estimation Testing, Case Test, Data Test, Creation Bed Test, Execution Test, Reporting Defect, Retest Retest, Delivery Report Status saben dina, Test completion, ETL testing ing saben panggung (HDFS, HIVE, HBASE). ngunggah input (log / file / cathetan etc) nggunakake sqoop / flume sing kalebu nanging ora diwatesi karo verifikasi data, Rekonsiliasi, Panguwasa Panganggo lan Pengesahan otentikasi (Kelompok, Panganggo, Privileges etc), Report cacat kanggo tim pangembangan utawa manajer lan nyetir kanggo nutup, Nggabungake kabeh cacat lan nggawe laporan cacat, Ngesyahke fitur lan masalah anyar ing Core Hadoop.

Framework disebut Unit MR kanggo Pengujian Peta-Ngurangi Program

Laporan cacat kanggo tim pembangunan utawa manajer lan nyopir mau kanggo nutup, Nggabungake kabeh cacat lan nggawe laporan cacat, Tanggung jawab kanggo nggawe testing Framework disebut Unit MR kanggo testing Map-Ngurangi program.

Unit Pengujian

Pengujian otomatis nggunakake OOZIE, Validasi data nggunakake alat surge query.

Test Execution

Rencana uji kanggo peningkatan HDFS, Otomasi test lan hasil

Strategi Rencana Test lan nulis Test Kasus kanggo nguji Aplikasi Hadoop

Carane nyoba nginstal lan ngatur

Dhukungan Proyek lan Sertifikasi

Tips lan Bimbingan Sertifikasi Cloudera lan Mock Interview Preparation, Tips Pembangunan Praktis lan Teknik

Mangga tulis ing info@itstechschool.com & hubungi kita ing + 91-9870480053 kanggo biaya kursus & biaya, jadwal & lokasi

Selehake We a Query

Kursus latihan iki dirancang kanggo mbantu mbusak kabeh Cloudera Spark lan Hadoop Developer Certification (CCA175) ujian lan Cloudera Administrator Sertifikat kanggo Apache Hadoop (CCAH) ujian. Konten kabeh isi kursus adhedhasar program sertifikasi loro iki lan mbantu sampeyan mbusak ujian sertifikasi kasebut kanthi gampang lan entuk proyek paling apik ing MNC ndhuwur.

Minangka bagéan saka latihan iki, sampeyan bakal nggarap proyèk wektu nyata lan tugas sing nduwèni implikasi sing banget ing skenario industri donya nyata saéngga mbantu sampeyan ngetutake karir kanthi cepet kanthi gampang.

Ing pungkasan program latihan iki bakal ana kuis sing sampurna nggambarake jinis pitakonan sing ditakonake ing ujian sertifikasi kasebut lan mbantu sampeyan menehi skor luwih apik ing ujian sertifikasi.

Certificate Course Completion bakal dianugerahi nalika rampungake karya Proyèk (ing review pakar) lan nyatakake paling ora 60% ing kuis. Sertifikat Intellipaat uga dikenal ing ndhuwur 80 + MNCs kayata Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, lan liya-liyane.

Kanggo informasi luwih lengkap Hubungi Kami.

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