Analisis Big Data | Slims Group 2

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Analisis Big Data

Dr. Joseph Teguh Santoso, S.Kom., M.Kom - Nama Orang;

Buku Analisis Big Data membahas konsep dasar, karakteristik, dan perkembangan teknologi Big Data serta proses pengolahan dan analisis data dalam jumlah besar. Pembaca diperkenalkan pada berbagai aspek pengelolaan data, mulai dari pengumpulan, penyimpanan, pengolahan, hingga pemanfaatan data untuk menghasilkan informasi yang berguna dalam pengambilan keputusan.\\r\\n\\r\\nBuku ini memberikan pemahaman mengenai pentingnya analisis Big Data di tengah perkembangan teknologi informasi dan meningkatnya jumlah data yang dihasilkan. Cocok bagi mahasiswa, pelajar, maupun pembaca yang ingin mempelajari konsep dan penerapan Big Data dalam berbagai bidang.\\r\\n\\r\\nKata Pengantar ............................................................................................................. iv\\r\\nDaftar Isi ........................................................................................................................ v\\r\\nBAGIAN I \\r\\nKonsep Analisis Big Data ............................................................................................. 11\\r\\nBab 1 Pengantar Analisis Big Data .......................................................................... 12\\r\\n1.1 Pengertian Analisis ................................................................................ 12\\r\\n1.1.1 Analisis Deskriptik ................................................................... 14\\r\\n1.1.2 Analisis Diagnostik .................................................................. 14\\r\\n1.1.3 Analisis Prediktif ...................................................................... 15\\r\\n1.1.4 Analisis Perspektif .................................................................... 15\\r\\n1.2 Definisi Big Data ................................................................................... 16\\r\\n1.3 Karakteristik Big Data ........................................................................... 18\\r\\n1.3.1 Volume ...................................................................................... 18\\r\\n1.3.2 Kecepatan ................................................................................. 18\\r\\n1.3.3 Variasi ....................................................................................... 19\\r\\n1.3.4 Veracity (Keakuratan) ............................................................... 19\\r\\n1.3.5 Value (Nilai) ............................................................................. 19\\r\\n1.4 Contoh Spesifik Domain dari Big data ................................................. 19\\r\\n1.4.1 Web ........................................................................................... 19\\r\\n1.4.2 Bidang Keuangan ..................................................................... 22\\r\\n1.4.3 Bidang Kesehatan ..................................................................... 23\\r\\n1.4.4 Internet of Thing (IoT) ............................................................ 25\\r\\n1.4.5 Bidang Lingkungan .................................................................. 26\\r\\n1.4.6 Bidang Logistik dan Transportasi ............................................. 28\\r\\n1.4.7 Bidang Industri ......................................................................... 30\\r\\n1.4.8 Bidang Retail ............................................................................ 31\\r\\n1.5 Alur Analisis Big Data .......................................................................... 32\\r\\n1.5.1 Pengumpulan Data ................................................................... 33\\r\\n1.5.2 Persiapan Data ......................................................................... 33\\r\\n1.5.3 Tipe Analisis ............................................................................ 34\\r\\n1.5.4 Visualisasi ................................................................................ 35\\r\\n1.6 Big Data Stack ...................................................................................... 35\\r\\n1.6.1 Sumber Raw Data .................................................................... 36\\r\\n1.6.2 Konektor Akses Data ............................................................... 37\\r\\n1.6.3 Data Storage (Penyimpanan Data) .......................................... 38\\r\\n1.6.4 Analisis Batch .......................................................................... 39\\r\\n1.6.5 Analisis Real-time ................................................................... 40\\r\\n1.6.6 Kueri Interaktif ........................................................................ 40\\r\\n1.6.7 Ukuran Database, Web & Kerangka Visualisasi ..................... 41 Analisis Big Data vi\\r\\n1.7 Memetakan Alur Analisis Tumpukan Data dalam Big Data (Big \\r\\n Data Stack) ......................................................................................... 42\\r\\nStudi Kasus : Analisis Data Genome ............................................................. 44\\r\\nStudi Kasus : Data Analisis Cuaca ................................................................. 48\\r\\n1.8 Pola Analisis ......................................................................................... 51\\r\\nBab 2 Penyiapan Big Data Stack ............................................................................ 56\\r\\n2.1 Platform Data Hortonworks (HDP) ...................................................... 56\\r\\n2.2 Launching Instan AWS EC2 ................................................................. 57\\r\\n2.3 Pengaturan Apache Ambari .................................................................. 60\\r\\n2.4 Menyiapkan HDP Stack dengan Ambari ............................................. 62\\r\\n2.5 Cloudera CDH Stack ............................................................................ 68\\r\\n2.6 Amazon Elastis MapReduce (EMR) .................................................... 75\\r\\n2.7 Azure HDInsight .................................................................................. 79\\r\\nBab 3 Pola Big Data ................................................................................................. 80\\r\\n3.1 Analisis Komponen Arsitektur & Gaya Desain ................................... 81\\r\\n3.1.1 Load Leveling dengan Queues ................................................ 81\\r\\n3.1.2 Load Balancing dengan Banyak Konsumen ........................... 81\\r\\n3.1.3 Pemilihan Pemimpin ............................................................... 82\\r\\n3.1.4 Sharding .................................................................................. 83\\r\\n3.1.5 Teorama Consistency, Availability & Partition \\r\\nTolerance (CAP) ..................................................................... 84\\r\\n3.1.6 Bloom Filter ............................................................................ 85\\r\\n3.1.7 Materialized Views ................................................................ 87\\r\\n3.1.8 Arsitektur Lambda .................................................................. 87\\r\\n3.1.9 Scheduler-Agent-Supervisor ................................................... 89\\r\\n3.1.10 Pipes & Filter .......................................................................... 90\\r\\n3.1.11 Web Service ............................................................................ 91\\r\\n3.1.12 Konsensus dalam Sistem Distribusi ........................................ 92\\r\\n3.2 Pola MapReduce .................................................................................. 95\\r\\n3.2.1 Peringkasan Numerik .............................................................. 97\\r\\n3.2.2 Top N ....................................................................................... 101\\r\\n3.2.3 Filter ........................................................................................ 103\\r\\n3.2.4 Distinct .................................................................................... 105\\r\\n3.2.5 Binning .................................................................................... 106\\r\\n3.2.6 Indeks Terbalik ........................................................................ 107\\r\\n3.2.7 Sorting ..................................................................................... 109\\r\\n3.2.8 Join .......................................................................................... 110\\r\\nBab 4 NoSQL ........................................................................................................... 116\\r\\n4.1 Database Key-Value ............................................................................ 116\\r\\n4.1.1 Amazon DinamoDB ............................................................... 117\\r\\n4.2 Database Dokumen ............................................................................. 122 vii Analisis Big Data\\r\\n4.2.1 MongoDB ............................................................................... 122\\r\\n4.3 Family Database .................................................................................. 126\\r\\n4.3.1 Hbase ...................................................................................... 126\\r\\n4.3.2 Contoh Penggunaan HBase .................................................... 131\\r\\n4.4 Database Grafik .................................................................................. 136\\r\\n4.4.1 Neo4j ....................................................................................... 136\\r\\nBAGIAN II\\r\\nImplementasi Analisis Big Data .................................................................................. 143\\r\\nBab 5 Akuisisi Data .................................................................................................. 144\\r\\n5.1 Pertimbangan Akuisisi Data ................................................................ 144\\r\\n5.1.1 Jenis Sumber ............................................................................ 144\\r\\n5.1.2 Velocity .................................................................................... 145\\r\\n5.1.3 Mekanisme Ingestion ............................................................... 145\\r\\n5.2 Kerangka Kerja Publish-Subscribe Messaging .................................... 146\\r\\n5.2.1 Apache Kafka .......................................................................... 146\\r\\n5.2.2 Amazon Kinesis ....................................................................... 153\\r\\n5.3 Sistem Pengumpulan Big Data ............................................................. 155\\r\\n5.3.1 Apache Flume .......................................................................... 155\\r\\n5.3.2 Apache Sqoop .......................................................................... 171\\r\\n5.3.3 Mengimpor Data dengan Sqoop .............................................. 171\\r\\n5.3.4 Memilih Data ntuk Diimpor .................................................. 172\\r\\n5.3.5 Konektor Khusus ..................................................................... 173\\r\\n5.3.6 Mengimpor Data ke Hive ........................................................ 173\\r\\n5.3.7 Mengimpor Data ke Hbase ...................................................... 174\\r\\n5.3.8 Incremental Imporrt ................................................................. 174\\r\\n5.3.9 Mengimpor Semua Tabel ......................................................... 175\\r\\n5.3.10 Mengekspor Data dengan Sqoop ............................................. 175\\r\\n5.4 Antrian Perpesanan ............................................................................... 175\\r\\n5.4.1 RabbitMQ ................................................................................ 175\\r\\n5.4.2 ZeroMQ ................................................................................... 178\\r\\n5.4.3 RestMQ .................................................................................... 179\\r\\n5.4.4 Amazon SQS ............................................................................ 181\\r\\n5.5 Konektor Kustom .................................................................................. 183\\r\\n5.5.1 Konektor Berbasis REST ......................................................... 184\\r\\n5.5.2 Konektor berbasis WebSocket ................................................. 187\\r\\n5.5.3 Konektor berbasis MQTT ........................................................ 188\\r\\n5.5.4 Amazon IoT ............................................................................. 190\\r\\n5.5.5 Azure IoT Hub ......................................................................... 199\\r\\nBab 6 Big Data Storage ............................................................................................ 207\\r\\n6.1 HDFS .................................................................................................... 207\\r\\n6.1.1 Arsitektur HDFS ...................................................................... 208\\r\\n6.1.2.1 Namenode ................................................................... 208\\r\\n6.1.2.2 Namenode Sekunder ................................................... 209 Analisis Big Data viii\\r\\n6.1.2.3 Datanode ..................................................................... 209\\r\\n6.1.2.4 Pemblokiran & Replikasi Data ................................... 209\\r\\n6.1.2.5 Jalur Baca HDFS ......................................................... 210\\r\\n6.1.2.6 Jalur Tulis HDFS ........................................................ 211\\r\\n6.1.3 Contoh Penggunaan HDFS ..................................................... 212\\r\\n6.1.3.2 Mengakses HDFS dengan Python .............................. 212\\r\\n6.1.3.3 Interface Web HDFS ................................................... 213\\r\\nBab 7 Analisis Batch ................................................................................................ 215\\r\\n7.1 Hadoop dan MapReduce ...................................................................... 215\\r\\n7.1.1 Model MapReduce Programming ............................................ 215\\r\\n7.2 Hadoop YARN ...................................................................................... 216\\r\\n7.3 Penjadwalan Hadoop ............................................................................ 220\\r\\n7.3.1 FIFO ......................................................................................... 222\\r\\n7.3.2 Penjadwalan Kapasitas ............................................................ 223\\r\\n7.4 Contoh Hadoop - Map Reduce ............................................................. 223\\r\\n7.4.1 Analisis Batch pada Sensor Data ............................................. 223\\r\\n7.4.2 Analisis Batch pada N-Gram Dataset ..................................... 226\\r\\n7.4.3 Temukan kata-kata teratas dengan MapReduce ....................... 228\\r\\n7.5 Pig ......................................................................................................... 229\\r\\n7.5.1 Loading Data ............................................................................ 230\\r\\n7.5.2 Tipe data pada Pig .................................................................... 230\\r\\n7.5.3 Analisis dan Data Filtering ...................................................... 231\\r\\n7.5.4 Storing Result ........................................................................... 233\\r\\n7.5.5 Debugging Operator ................................................................. 233\\r\\n7.5.6 Contoh Pig ............................................................................... 235\\r\\nStudi Kasus : Analisis Batch pada Artikel Berika .......................................... 236\\r\\n7.6 Apache Oozie ....................................................................................... 241\\r\\n7.6.1 Workflow Oozie untuk Analisis Data ...................................... 241\\r\\n7.7 Apache Spark ........................................................................................ 249\\r\\n7.7.1 Spark Operation ....................................................................... 252\\r\\n7.8 Search ................................................................................................... 257\\r\\n7.8.1 Apache Solr ............................................................................. 257\\r\\nBab 8 Analisis Real-Time ......................................................................................... 269\\r\\n8.1 Stream Processing ................................................................................. 269\\r\\n8.1.1 Apache Storm ........................................................................... 269\\r\\n8.1.1.1 Konsep ......................................................................... 269\\r\\n8.1.1.2 Stream Groupings ........................................................ 271\\r\\n8.1.1.3 Perancangan ................................................................. 272\\r\\n8.1.1.4 Reliable Processing ...................................................... 274\\r\\n8.2 Studi Kasus Storm ................................................................................ 274\\r\\n8.2.1 Analisis Sentimen Twitter Real-Time ..................................... 274\\r\\n8.2.2 Analisis Data Cuaca Real-Time .............................................. 285\\r\\n8.3 Pemprosesan Dalam Memori ............................................................... 294 ix Analisis Big Data\\r\\n8.3.1 Apache Spark .......................................................................... 294\\r\\n8.4 Studi Kasus Spark ................................................................................ 299\\r\\n8.4.1 Analisis Data Sensor Rela-Time ............................................. 299\\r\\n8.4.2 Analisis Data Sensor Parkir Real-Time untuk Sistem\\r\\n Smart Parking .......................................................................... 301\\r\\n8.4.3 Analisis Sentimen Twitter Real-Time ..................................... 307\\r\\n8.4.4 Analisis Window pada Tweets ................................................. 312\\r\\nBab 9 Interactive Query .......................................................................................... 315\\r\\n9.1 Spark SQL ............................................................................................ 315\\r\\nStudi Kasus : Kueri Interaktif pada Data Cuaca\\r\\n9.2 Hive ...................................................................................................... 324\\r\\n9.3 Amazon Redshift .................................................................................. 330\\r\\n9.4 Google BigQuery ................................................................................. 339\\r\\nBab 10 Serving Database & WebFramework .......................................................... 349\\r\\n10.1 Database Relasional (SQL) ................................................................... 349\\r\\n10.1.1 MySQL ..................................................................................... 351\\r\\n10.2 Database Non-Relasional ...................................................................... 354\\r\\n10.2.1 Amazon DinamoDB ................................................................. 355\\r\\n10.2.2 Cassandra .................................................................................. 361\\r\\n10.2.3 MongoDB ................................................................................. 364\\r\\n10.3 Python Web Application Framework – Django .................................... 367\\r\\n10.3.1 Rancangan Django .................................................................... 367\\r\\n10.3.2 Memulai pengembangan dengan Django ................................. 368\\r\\nBAGIAN III \\r\\nTopik Lanjutan .............................................................................................................. 392\\r\\nBab 11 Analisis Algoritma ........................................................................................... 393\\r\\n11.1 Framework .............................................................................................. 393\\r\\n11.1.1 Spark Mlib ................................................................................. 393\\r\\n11.1.2 H2O ........................................................................................... 394\\r\\n11.2 Clustering ............................................................................................... 397\\r\\n11.2.1 K-Means .................................................................................... 398\\r\\nStudi Kasus ...................................................................................................... 405\\r\\n11.3 Klasifikasi dan Regresi .......................................................................... 412\\r\\n11.3.1 Metrik Evaluasi Kinerja ............................................................ 413\\r\\n11.3.2 Naive Bayes .............................................................................. 415\\r\\n11.3.3 Model Linier Umum ................................................................ 427\\r\\n11.3.4 Pohon Keputusan ..................................................................... 442\\r\\n11.3.5 Random Forest ......................................................................... 445\\r\\n11.3.6 Gradient Boosting Machine ..................................................... 454\\r\\n11.3.7 Support Vector Machine ........................................................... 463\\r\\n11.3.8 Deep Learning .......................................................................... 466 Analisis Big Data x\\r\\n11.4 Studi Kasus : Mengklasifikasikan Digit Tulisan tangan ....................... 476\\r\\n11.4.1 Menklasifikasikan Digit dengan H2O ...................................... 477\\r\\n11.4.2 Mengklasifikasikan Digit dengan Spark .................................. 479\\r\\n11.5 Studi Kasus : Analisis Data Genome (Implementasi) .......................... 481\\r\\n11.6 Sistem Rekomendasi ............................................................................ 485\\r\\n11.6.1 Alternating Least Square (ALS) .............................................. 486\\r\\n11.6.2 Singular Value decomposition (SVD) ..................................... 491\\r\\n11.7 Studi Kasus : Sistem Rekomendasi film .............................................. 491\\r\\nBab 12 Visualisasi Data .............................................................................................. 505\\r\\n12.1 Framework dan Library ........................................................................ 505\\r\\n12.1.1 Lightning ................................................................................... 505\\r\\n12.1.2 Pygal ......................................................................................... 506\\r\\n12.1.3 Seaborn ..................................................................................... 506\\r\\n12.2 Contoh Visualisasi ................................................................................. 506\\r\\n12.2.1 Diagram Garis ........................................................................... 506\\r\\n12.2.2 Scatter Plot (Plot Sebar) ........................................................... 508\\r\\n12.2.3 Diagram Batang ....................................................................... 512\\r\\n12.2.4 Box Plot ................................................................................... 515\\r\\n12.2.5 Diagram Lingkaran .................................................................. 517\\r\\n12.2.6 Diagram Titik ........................................................................... 518\\r\\n12.2.7 Diagram Map ........................................................................... 519\\r\\n12.2.8 Diagram Pengukur ................................................................... 521\\r\\n12.2.9 Diagram Radar ......................................................................... 522\\r\\n12.2.10 Diagram Matriks ...................................................................... 523\\r\\n12.2.11 Grafik Force-Directed .............................................................. 525\\r\\n12.2.12 Grafik Spasial .......................................................................... 527\\r\\n12.2.13 Plot Distribusi .......................................................................... 528\\r\\n12.2.14 Plot Kernel Density Estimate (KDE) ...................................... 528\\r\\n12.2.15 Plot Regresi ............................................................................. 529\\r\\n12.2.16 Plot Residual ........................................................................... 531\\r\\n12.2.17 Plot Interaktif .......................................................................... 532\\r\\n12.2.18 Plot Violin ............................................................................... 532\\r\\n12.2.19 Plot Strip (Jalur) ..................................................................... 533\\r\\n12.2.20 Plot Point ................................................................................ 534\\r\\n12.2.21 Plot Count ............................................................................... 535\\r\\n12.2.22 Heatmap .................................................................................. 536\\r\\n12.2.23 Clustered Heatmap ................................................................. 537\\r\\n12.2.24 Joint Plot ................................................................................. 538\\r\\n12.2.25 Pair Grid .................................................................................. 540\\r\\n12.2.26 Facet Grid ............................................................................... 541\\r\\nBibliografi .................................................................................................................... 543

Ketersediaan

My Library 005.74 Jos a

L260001JOS

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My Library 005.74 Jos a

L260002JOS

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Informasi Detail

Judul Seri: -
No. Panggil: 005.74 Jos a
Penerbit: Semarang : YAYASAN PRIMA AGUS TEKNIK., 2020
Deskripsi Fisik: 546 hlm
Bahasa: Indonesia
ISBN/ISSN: 9786239504243
Klasifikasi: 005.74
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: Big Data
Info Detail Spesifik: -
Pernyataan Tanggungjawab: Dr. Joseph Teguh Santoso, S.Kom., M.Kom

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