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データ分析基盤の変遷とデータレイクの作り方
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Ojima Hikaru
April 21, 2018
Technology
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データ分析基盤の変遷とデータレイクの作り方
Battle Conference U30 #2018
Ojima Hikaru
April 21, 2018
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Transcript
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− 8 S3
− 9 S3
− 10 Redshift
− 11
12 Data Lake Architecture
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Data Lake 14 Hive Metastore
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Hive Metastore • Hive Metastore MySQL
• Hive Metastore (HCatalog) server • EMR 5 19
Hive Metastore S3 20
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Hive Metastore • %)" &'&'%)" • &$#
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Hive Metastore 1. Hive Metastore
25
Hive Metastore 1. Hive Metastore
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Hive Metastore 1. Hive Metastore
2. 3. Hive Metastore 27
Hive Metastore 1. Hive Metastore
2. 3. Hive Metastore 4. 28
Hive Metastore ! 1. ),(! $ Hive Metastore # 2.
),($'*, 3. Hive Metastore ! $ 4. ),($ &%+ $ "),($ 29
Hive Metastore 30
Hive Metastore • Hive Redshift "%!$%# • Redshift
COPY "%! csv+gzip • Hive "%! ORC • Redshift csv+gzip Hive ORC ⇒ Redshift Spectrum 31
Redshift Spectrum • Redshift S3(#$+ &%*" • ',)+
Hive Metastore ! Hive ',)+" 32 CREATE EXTERNAL SCHEMA schema_name FROM HIVE METASTORE DATABASE 'database_name’ URI 'hive_metastore_uri’;
Hive Metastore • Redshift Hive 33 INSERT
INTO ‘Redshift ’ SELECT … FROM ‘Hive ’ WHERE y=YYYY AND m=MM AND d=DD;
Hive Metastore • Redshift Spectrum
Hive Metastore • Spark SQL • Presto • Athena • Flink 34
Hive Metastore Hive Metastore S3 Hive,
Redshift Spectrum , Spark 35
36
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Data Lake , !$# 251&*251&%+$#! Hive Metastore , +$# Data Lake , "$#(!6 37
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