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Hadoop and Kerberos
4. Page 4 Â© Hortonworks Inc. 2014 Background: YARNã«ã‚ˆã‚‹Hadoopã®ãƒžãƒ«ãƒãƒ†ãƒŠãƒ³ãƒˆåŒ– â€¢â€¯ Hadoop2ã§å°Žâ¼Šã•ã‚ŒãŸYARNã«ã‚ˆã‚Šã€Hadoopã‚¯ãƒ© ã‚¹ã‚¿ã‚’è¤‡æ•°ã®ã²ã¨ã‚„çµ„ç¹”ã€ãƒ¯ãƒ¼ã‚¯ãƒãƒ¼ãƒ‰ã«ã‚ˆã£ã¦ å…±â½¤ã™ã‚‹ã“ã¨ãŒå®¹æ˜“ã«ãªã£ãŸã€‚ â€¢â€¯ å·¨â¼¤ãªHDFSã‚¯ãƒ©ã‚¹ã‚¿ã«æ§˜ã€…ãªãƒ‡ãƒ¼ã‚¿ã‚’æ ¼ç´ã—ã¦ãŠ ãã€ãã®ãƒ‡ãƒ¼ã‚¿ã«å¯¾ã—ã¦æ§˜ã€…ãªå‡¦ç†ã‚’â¾›ã‚‰ã›ã‚‹ã“ ã¨ãŒå¯èƒ½ã«ã€‚ 5. Page 5 Â© Hortonworks Inc. 2014 1 Â° Â° Â° Â° Â° Â° Â° Â° Â° Â° Â° Â° Â° Â° Â° Â° Â° Â° Â° Â° N HDFS Hadoop Distributed File System DATA MANAGEMENT ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸(HDFS)ã¨ã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ ãƒ†ã‚£ãƒ³ã‚°(MapReduce)ãŒçµåˆã—ã¦ ã„ãŸ Before YARN MapReduce ã‚¯ãƒ©ã‚¹ã‚¿å…¨ä½“ã®ãƒª
i43s 2016/02/09
hadoop

kerberos
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40åˆ†ã§ã‚ã‹ã‚‹Hadoopå¾¹åº•å…¥é–€ ï¼ˆCloudera World Tokyo 2014 è¬›æ¼”è³‡æ–™ï¼‰
2. è‡ªå·±ç´¹ä»‹ ï® æ¿±é‡Ž è³¢ä¸€æœ— ï¼ˆã¯ã¾ã® ã‘ã‚“ã„ã¡ã‚ã†ï¼‰ â€“ æ—¥æœ¬Hadoopãƒ¦ãƒ¼ã‚¶ãƒ¼ä¼šã®ãƒ¡ãƒ³ãƒã¨ã—ã¦ã€ ã‚¤ãƒ™ãƒ³ãƒˆ Hadoop Conference Japan ã‚„ å‹‰å¼·ä¼š Hadoopã‚½ãƒ¼ã‚¹ã‚³ãƒ¼ãƒ‰ãƒªãƒ¼ãƒ‡ã‚£ãƒ³ã‚° ã®ä¼ç”»ãƒ»å®Ÿæ–½ã‚’æ‹…å½“ â€“ ç¿”æ³³ç¤¾ ã€ŽHadoopå¾¹åº•å…¥é–€ã€ ç›£ä¿®è€… â€“ NTT DATA åŸºç›¤ã‚·ã‚¹ãƒ†ãƒ äº‹æ¥æœ¬éƒ¨ OSSãƒ—ãƒãƒ•ã‚§ãƒƒã‚·ãƒ§ãƒŠãƒ«ã‚µãƒ¼ãƒ“ã‚¹ ã«æ‰€å±ž â€“ Hadoop é–¢ä¿‚è€…ã§è©±é¡Œã«ãªã£ãŸ ã€ŒçµŒç”£çœã®å ±å‘Šæ›¸ã€ ã®å®Ÿè¨¼äº‹æ¥ã®PM å¹³æˆï¼’ï¼‘å¹´åº¦ ç”£å¦é€£æºã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢å·¥å¦å®Ÿè·µäº‹æ¥å ±å‘Šæ›¸ é«˜ä¿¡é ¼ã‚¯ãƒ©ã‚¦ãƒ‰å®Ÿç¾ç”¨ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢é–‹ç™ºï¼ˆåˆ†æ•£åˆ¶å¾¡å‡¦ç†æŠ€è¡“ç‰ã«ä¿‚ã‚‹ãƒ‡ãƒ¼ã‚¿ã‚»ãƒ³ã‚¿ãƒ¼é«˜ä¿¡é ¼åŒ–ã«å‘ã‘ãŸå®Ÿè¨¼äº‹æ¥ï¼‰ http: //www.meti.go.jp/policy/mono_info_service/joho/downloadfiles/2010software_research/clou_dist
i43s 2014/11/13
hadoop
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Presto - Hadoop Conference Japan 2014
Presto is a distributed SQL query engine that allows for interactive analysis of large datasets across various data sources. It was created at Facebook to enable interactive querying of data in HDFS and Hive, which were too slow for interactive use. Presto addresses probl ems with existing solutions like Hive being too slow, the need to copy data for analysis, and high costs of commercial databases
i43s 2014/07/11
hadoop

presto
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Cloudera Standard ã®ã”æ¡ˆå†… ~ ç„¡å„Ÿç‰ˆ å¤§å¹…æ©Ÿèƒ½å¼·åŒ–ã®ãŠçŸ¥ã‚‰ã› | Cloudera Japan
Cloudera ã¯ã€æœ€å¤§è¦æ¨¡ã®ä¼æ¥ãŒã‚ã‚‰ã‚†ã‚‹å ´æ‰€ã«å˜åœ¨ã™ã‚‹ã™ã¹ã¦ã®ãƒ‡ãƒ¼ã‚¿ã‚’ä¿¡é ¼æ€§ã®é«˜ã„æœ‰æ„ç¾©ãªæ´žå¯Ÿã«å¤‰æ›ã§ãã‚‹ã‚ˆã†æ”¯æ´ã—ã¾ã™ã€‚ Trusted Data Today for Tomorrowâ€™s AI (ä»Šæ—¥ã®ä¿¡é ¼ã§ãã‚‹ãƒ‡ãƒ¼ã‚¿ã‚’ã€æ˜Žæ—¥ã® AI ã«è¨—ã™) ãƒ‡ãƒ¼ã‚¿ã¨ AI ãŒãƒ†ãƒ¼ãƒžã®ãƒ—ãƒ¬ãƒŸã‚¢ã‚«ãƒ³ãƒ•ã‚¡ãƒ¬ãƒ³ã‚¹ã§ã€æ¥ç•Œæœ€å‰ç·šã®æ˜Žç¢ºãªãƒ“ã‚¸ãƒ§ãƒ³ã‚’æŒã£ãŸæ–¹ã€…ã‚„ã‚½ãƒ¼ãƒˆãƒªãƒ¼ãƒ€ãƒ¼ã‹ã‚‰è²´é‡ãªæ´žå¯Ÿã‚’å¾—ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚
i43s 2014/03/07
apache

hadoop

spark
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Big Data with MySQL and Hadoop at MySQL Connect 2013
I will be talking about Big Data with MySQL and Hadoop atÂ MySQL Connect 2013Â (Sept. 21-22) in San Francisco as well as at Percona University at Washington, DC (September 12, 2013). Apache Hadoop is a very popular Big Data solution and we can nowadays easily integrate it with MySQL. I will start with a brief introduction of Apache Hadoop and its components (HFDS, Map/Reduce, Hive, HBase/HCatalog, F
i43s 2013/08/08
mysql

hadoop
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å¤ã‚µãƒŸ2013 Hadoopã‚’ä½¿ã‚ãªã„ç‹¬è‡ªã®åˆ†æ•£å‡¦ç†ç’°å¢ƒã®æ§‹ç¯‰ã¨ãã®é‹ç”¨
Developers Summit 2013 Summer ï¼ˆ2013å¹´8æœˆ1æ—¥æ¸‹è°·ã«ã¦é–‹å‚¬ï¼‰ã®A3ã‚»ãƒƒã‚·ãƒ§ãƒ³ã€ŒHadoopã‚’ä½¿ã‚ãªã„ç‹¬è‡ªã®åˆ†æ•£å‡¦ç†ç’°å¢ƒã®æ§‹ç¯‰ã¨ãã®é‹ç”¨ã€ã®ç™»å£‡è³‡æ–™ã§ã™ã€‚ ã€ã‚»ãƒƒã‚·ãƒ§ãƒ³æ¦‚è¦ã€‘ Intel x86ã‚µãƒ¼ãƒã®æ€§èƒ½å‘ä¸Šã«ä¼´ã„ã€ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã®åºƒå¸¯åŸŸåŒ–ãŒé€²ã¿ã¤ã¤ã‚ã‚‹ãªã‹ã€ ã„ã‚ˆã„ã‚ˆæ™®åŠæœŸã«å…¥ã‚ã†ã¨ã—ã¦ã„ã‚‹10Gigabit Ethernetã®Webã‚µãƒ¼ãƒ“ã‚¹ã«ãŠã‘ã‚‹ åˆ©ç”¨æ³•ã¨ãã®æ€§èƒ½ã¨å…·ä½“çš„ãªåˆ©ç”¨ä¾‹ã«ã¤ã„ã¦ç´¹ä»‹ã—ã¾ã™ã€‚ ã•ã‚‰ã«è¿‘ã„æœªæ¥ã‚’äºˆè¦‹ã•ã›ã‚‹40Gigabit Ethernetã®ä»Šã¨ã“ã‚Œã‹ã‚‰ã«ã¤ã„ã¦ã‚‚ é‹ç”¨æ€§ãƒ»æ€§èƒ½ãƒ»æ³¨æ„ç‚¹ãªã©ã‚’è¦‹ã¦ã„ãã¾ã™ã€‚Read less
i43s 2013/08/02
hadoop
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