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SQL on Hadoop 比較検証 ã€2014月11æ—¥ã«ãŠã‘る検証レãƒãƒ¼ãƒˆã€‘
Impala Meetup 2014/10/31 @Tokyo 講演資料 ã€æ³¨æ„äº‹é …ã€‘ 本資料ã§ç´¹ä»‹ã—ã¦ã„る検証çµæžœã¯2014年当時ã®ã‚‚ã®ã§ã™ã€‚当該ソフトウェアã¯æˆé•·ã‚„改善ãŒæ—©ãã€ç¾æ™‚点ã®ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã§ã¯å¤§ããç•°ãªã‚‹æ©Ÿèƒ½ã‚„性能ã¨ãªã£ã¦ã„ã¾ã™ã€‚ SQL on Hadoopã®æœ€æ–°æƒ…å ±ã«åŸºã¥ãサービスやシステムインテグレーションã«ã”興味をãŠæŒã¡ã®æ–¹ã¯ã€NTTデータ 基盤システム事æ¥æœ¬éƒ¨ OSSプãƒãƒ•ã‚§ãƒƒã‚·ãƒ§ãƒŠãƒ«ã‚µãƒ¼ãƒ“ス（電åメール： hadoop [AT] kits.nttdata.co.jp） ã«ã”相談ãã ã•ã„。Read less
SQL on Hadoop in Taiwan
This document discusses SQL engines for Hadoop, including Hive, Presto, and Impala. Hive is best for batch jobs due to its stability. Presto provides interactive queries across data sources and is easier to manage than Hive with Tez. Presto's distributed architecture allows queries to run in parallel across nodes. It supports pluggable connectors to access different data stores and has language bi
Cloudera Blog
We are thrilled to announce the general availability of the Cloudera AI Inference service, powered by NVIDIA NIM microservices, part of the NVIDIA AI Enterprise platform, to accelerate generative AI deployments for enterprises. This service supports a range of optimized AI models, enabling seamless and scalable AI inference. Background The generative AI landscape is evolving […] Read blog post
Cloudera Blog
We are thrilled to announce the general availability of the Cloudera AI Inference service, powered by NVIDIA NIM microservices, part of the NVIDIA AI Enterprise platform, to accelerate generative AI deployments for enterprises. This service supports a range of optimized AI models, enabling seamless and scalable AI inference. Background The generative AI landscape is evolving […] Read blog post
War of the Hadoop SQL engines. And the winner is ...? - Sonra
War of the Hadoop SQL engines. And the winner is …? You may have wondered why we were quiet over the last couple of weeks? Well, we locked ourselves into the basement and did some research and a couple of projects and PoCs on Hadoop, Big Data, and distributed processing frameworks in general. We were also looking at Clickstream data and Web Analytics solutions. Over the next couple of weeks we wil
Apache Drill at ApacheCon2014
Lot of workloads exist for Big data, batch, machine learning, search, interactive SQL, Operational/user facing applicationsApache Drill fits into the interactive SQL category Analytics on Semi-Structured/Nested dataUse standard SQL to query Nested data without upfront flattening/modelingExtensions to ANSI SQL to operate on nested dataGeneric architecture for a broad variety of nested data types (e
Yahoo Betting on Apache Hive, Tez, and YARN
Low-latency SQL queries, Business Intelligence (BI), and Data Discovery on Big Data are some of the hottest topics these days in the industry with a range of solutions coming to life lately to address them as either proprietary or open-source implementations on top of Hadoop. Some of the popular ones talked about in the Big Data communities are Hive, Presto, Impala, Shark, and Drill. Yahoo has tr
Apache Drill: Building Highly Flexible, High Performance Query Engines by M.C. Srivas, Co-founder and CTO at MapR
Apache Drill: Building Highly Flexible, High Performance Query Engines by M.C. Srivas, Co-founder and CTO at MapR SQL is one of the most widely used languages to access, analyze, and manipulate structured data. As Hadoop gains traction within enterprise data architectures across industries, the need for SQL for both structured and loosely-structured data on Hadoop is growing rapidly Apache Drill s
Hadoop＋SQL＋インメモリã€ãƒžãƒ«ãƒã‚¯ãƒ©ã‚¦ãƒ‰å¯¾å¿œã®ã€ŒPivotal Oneã€ãƒ—ラットフォーム発表。EMC World 2013
Hadoop＋SQL＋インメモリã€ãƒžãƒ«ãƒã‚¯ãƒ©ã‚¦ãƒ‰å¯¾å¿œã®ã€ŒPivotal Oneã€ãƒ—ラットフォーム発表。EMC World 2013 EMCãŒãƒ©ã‚¹ãƒ™ã‚¬ã‚¹ã§é–‹å‚¬ä¸ã®ã‚¤ãƒ™ãƒ³ãƒˆã€ŒEMC World 2013ã€ã€‚2日目ã®åŸºèª¿è¬›æ¼”ã«ã¯ã€EMCã¨VMwareãŒè¨ç«‹ã—ãŸæ–°ä¼šç¤¾ã€ŒPivotalã€ã®CEO ãƒãƒ¼ãƒ«ãƒ»ãƒžãƒªãƒƒãƒ„（Paul Maritz）æ°ãŒç™»å£‡ã—ã€ã‚¯ãƒ©ã‚¦ãƒ‰æ™‚代ã®ã‚¢ãƒ—リケーション基盤ã¨ãªã‚‹ã€ŒPivotal Oneã€ã‚’発表ã—ã¾ã—ãŸã€‚ Pivotalã¯ã€EMCãŒè²·åŽã—ãŸGreenplumや開発コンサルタントã®Pivotal Labsã€VMwareãŒè²·åŽã—ãŸSpring Sourceã‚„CloudFoundryãªã©ã®ãƒãƒ¼ãƒ を集ã‚ã¦12月ã«ç™ºè¶³ã—ãŸçµ„織。今月ã‹ã‚‰æ£å¼ãªä¼æ¥ã¨ã—ã¦ã®æ´»å‹•ã‚’開始ã—ã¦ã„ã¾ã™ã€‚ Pivotal Oneã¯ã€ãƒ“ッグデータã¨ã‚¯ãƒ©ã‚¦ãƒ‰æ™‚代ã®ã‚¢ãƒ—リケーション基盤ã¨ã—ã¦ã€åŒç¤¾ãŒä»Šå¹´æœ«ã«ãƒªãƒªãƒ¼ã‚¹äºˆå®š
オープンソースã®SQL-in-Hadoopソリューション:我々ã¯ã„ã¾ã©ã“ã«ï¼Ÿ
Spring Bootã«ã‚ˆã‚‹APIãƒãƒƒã‚¯ã‚¨ãƒ³ãƒ‰æ§‹ç¯‰å®Ÿè·µã‚¬ã‚¤ãƒ‰ 第2版 何åƒäººã‚‚ã®é–‹ç™ºè€…ãŒã€InfoQã®ãƒŸãƒ‹ãƒ–ック「Practical Guide to Building an API Back End with Spring Bootã€ã‹ã‚‰ã€Spring Bootを使ã£ãŸREST API構築ã®åŸºç¤Žã‚’å¦ã‚“ã 。ã“ã®æœ¬ã§ã¯ã€å‡ºç‰ˆæ™‚ã«æ–°ã—ãリリースã•ã‚ŒãŸãƒãƒ¼ã‚¸ãƒ§ãƒ³ã§ã‚ã‚‹ Spring Boot 2 を使用ã—ã¦ã„る。ã—ã‹ã—ã€Spring Boot3ãŒæœ€è¿‘リリースã•ã‚Œã€é‡è¦ãªå¤‰...
Teradata Presto | Product Details | Open Source
Teradata Blogs When big data becomes vast, what's your data dropping strategy? Read more Support Teradata at Your Service (TAYS) Simple, secure customer access to products, services, education, and support function information. Read more Certifications Teradata Certified Professional Program (TCPP) Management, development, and oversight of the premiere Teradata Certification Program. Read more Con
Don't use Hadoop - your data isn't that big
"So, how much experience do you have with Big Data and Hadoop?" they asked me. I told them that I use Hadoop all the time, but rarely for jobs larger than a few TB. I'm basically a big data neophite - I know the concepts, I've written code, but never at scale. The next question they asked me. "Could you use Hadoop to do a simple group by and sum?" Of course I could, and I just told them I needed t
Hadoopã®ã‚»ã‚«ãƒ³ãƒ€ãƒªã‚½ãƒ¼ãƒˆã‚’é¿ã‘ã€ã‚ˆã‚Šé«˜é€Ÿã«å€¤ã‚’ソートã™ã‚‹æ–¹æ³•
Hadoopã®Reduceã«æ¸¡ã•ã‚Œã‚‹ã®ã¯ã‚ーã¨å€¤ã®ãƒªã‚¹ãƒˆã ãŒã€ã“ã®ã¨ã値ã®ãƒªã‚¹ãƒˆã«å«ã¾ã‚Œã‚‹å„アイテム（値ãã®ã‚‚ã®ï¼‰ã¯ã‚½ãƒ¼ãƒˆã•ã‚Œã¦ã„ãªã„。ソートã•ã‚Œã¦ã„ã¦æ¬²ã—ã„å ´åˆã«ã¯ã‚»ã‚«ãƒ³ãƒ€ãƒªã‚½ãƒ¼ãƒˆã¨å‘¼ã°ã‚Œã‚‹ãƒ†ã‚¯ãƒ‹ãƒƒã‚¯ã‚’使ã†ã®ãŒå®šçŸ³ã¨ã•ã‚Œã¦ã„ã‚‹ãŒã€ã“ã‚Œã¯å®Ÿè£…ã®é¢ã§ã‚‚概念的ãªé¢ã§ã‚‚ãƒãƒƒãƒ‰ãƒŽã‚¦ãƒã‚¦çš„ãªå´é¢ãŒã‚る。Hadoopã«ã¯ã€Œã‚ーをソートã™ã‚‹ã€æ©Ÿèƒ½ã¯å®Ÿè£…ã•ã‚Œã¦ã„る。ãã“ã§ã€å€¤ã‚’ã‚ーã«å…¥ã‚Œã¦ã—ã¾ã„ã€ã“ã®Hadoopã«å‚™ã‚ã£ã¦ã„る「ã‚ーをソートã™ã‚‹ã€æ©Ÿèƒ½ã«ã‚ˆã£ã¦ã€å®Ÿè³ªçš„ã«å€¤ã‚’ソートã—よã†ã¨ã„ã†ã‚ã‘ã 。 Map/Reduceã¨ã„ã†ã®ã¯ã‚ーã”ã¨ã«ãƒ‡ãƒ¼ã‚¿ã‚’分割ã—ã¦å‡¦ç†ã™ã‚‹æ–¹æ³•ãªã®ã§ã€ã€Œã‚ーã«å€¤ãŒå…¥ã£ãŸã‚‰åˆ†å‰²ãŒãŠã‹ã—ããªã‚‹ã‚“ã˜ã‚ƒï¼Ÿã€ã¨æ€ã†ã®ã¯å½“然ã§ã‚る。ã‚ーã«å€¤ãŒå…¥ã£ã¦ã„ã¦ã‚‚ã€åˆ†å‰²ã«å½±éŸ¿ã—ãªã„よã†ã€Partitioningクラスを自分ã§æ‹¡å¼µã—ã€åˆ†å‰²ã®åŸºæº–ã¨ãªã‚‹å€¤ï¼ˆæœ¬æ¥ã®ã‚ー）ã«ã¯ã€å€¤ã®å½±éŸ¿ãŒå‡ºãªã„よã†ã«ã™ã‚‹ã®ã 。ãã‚Œ
SQL, Pigã®CUBE - wyukawa's diary
SQLã§å°è¨ˆã‚„ç·åˆè¨ˆã‚’求ã‚る時ã«GROUP BYを利用ã™ã‚‹ã“ã¨ãŒå¤šã„ã¨æ€ã„ã¾ã™ãŒã„ã‚ã‚“ãªè»¸ã§é›†è¨ˆã—ãŸã„å ´åˆã«ROLLUP, CUBE, GROUPING SETSを使ã†ã“ã¨ãŒã§ãるよã†ã§ã™ã€‚ 詳ã—ãã¯ã“ã¡ã‚‰å‚ç…§ http://homepage2.nifty.com/sak/w_sak3/doc/sysbrd/sq_kj04_4.htm ROLLUP, CUBE, GROUPING SETSを使ã†ã“ã¨ãŒã§ãã¾ã™ã¨æ–定ã—ã¦ã„ãªã„ã®ã¯åƒ•ãŒè©¦ã—ã¦ãªã„ã‹ã‚‰ã§ã™ï¼ˆæ±— ãªãœè©¦ã—ã¦ã„ãªã„ã‹ã¨ã„ã†ã¨ã“れらã®æ©Ÿèƒ½ã‚’利用ã§ãã‚‹ã®ãŒOracle, SQL Server, DB2ã ã‹ã‚‰ã§ã™ã€‚Oracle XEをダウンãƒãƒ¼ãƒ‰ã—よã†ã‹ã¨æ€ã„ã¾ã—ãŸã‘ã©ãƒ¦ãƒ¼ã‚¶ç™»éŒ²ã«å¿ƒãŒæŠ˜ã‚Œã¾ã—ãŸw　ã¡ãªã¿ã«MySQLã§ã¯ROLLUPã®ã¿ã‚µãƒãƒ¼ãƒˆã—ã¦ã„るらã—ã„ã§ã™ã€‚ 今回ã¯è€ƒãˆã‚‰ã‚Œã‚‹å…¨ã¦ã®çµ„ã¿åˆã‚ã›ã§é›†è¨ˆã™ã‚‹CUBEã«ã¤ã„ã¦æ›¸ã„ã¦ã¿ãŸã„ã¨æ€
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Inside Yellow Pages' SQL-on-Hadoop Journey
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Apache Hadoop Distribution | MapR
商用Hadoopディストãƒã€ŒPivotal HDã€ã¨DBエンジン「HAWQã€ã‚’æ供開始
GitHub - intel-hadoop/project-panthera-ase: Analytical SQL Engine (ASE) for Hadoop under "Project Panthera"
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