sdsblog.com
Architecting BigData for Real Time Analytics | SDS Blog
https://sdsblog.com/2015/03/17/realtime-bigdata
Fed up with Outdated Arrays? Read About SDS, BigData, and Cloud Storage. Architecting BigData for Real Time Analytics. March 17, 2015. March 17, 2015. BigData is quite new, yet when we examine the common solutions and deployment practices it seems like we are going backwards in time. Manual processes, patches of glue logic and partial solutions, wasted resources and more are we back in the 90’s? Can we build it more efficiently to address real-world business challenges? Only one small group of analysts w...
sdsblog.com
March | 2015 | SDS Blog
https://sdsblog.com/2015/03
Fed up with Outdated Arrays? Read About SDS, BigData, and Cloud Storage. Architecting BigData for Real Time Analytics. March 17, 2015. March 17, 2015. BigData is quite new, yet when we examine the common solutions and deployment practices it seems like we are going backwards in time. Manual processes, patches of glue logic and partial solutions, wasted resources and more are we back in the 90’s? Can we build it more efficiently to address real-world business challenges? Only one small group of analysts w...
sdsblog.com
Architecting BigData for Real Time Analytics | SDS Blog
https://sdsblog.com/2015/03/17/realtime-bigdata/comment-page-1
Fed up with Outdated Arrays? Read About SDS, BigData, and Cloud Storage. Architecting BigData for Real Time Analytics. March 17, 2015. March 17, 2015. BigData is quite new, yet when we examine the common solutions and deployment practices it seems like we are going backwards in time. Manual processes, patches of glue logic and partial solutions, wasted resources and more are we back in the 90’s? Can we build it more efficiently to address real-world business challenges? Only one small group of analysts w...
bionics.it
Random links from the Hadoop NGS Workshop | Bionics IT
http://bionics.it/posts/hadoop-ngs-workshop
Random links from the Hadoop NGS Workshop. Share this on → Twitter. Posted on: 19 Feb '15. Some random links from the Hadoop for Next-Gen Sequencing workshop. Held at KTH in Kista, Stockholm in February 2015. UPDATE: Slides and Videos now available. By Big Data Genomics. Tweet by Frank Nothaft on common workflow def. Part of Global Alliance for . Another link is ga4gh.org. Does support multiple outputs etc. Black-box vs. White-box. Workflow dependency graph can be dynamically built up while you're running.
tech-meetup.com
Tachyon: an open source memory-centric distributed storage system - 湾区同学技术沙龙
http://www.tech-meetup.com/events/20150719
Tachyon: an open source memory-centric distributed storage system. Posted Jun 11, 2015, 11:53 AM. By Hao Xu [ updated Jul 2, 2015, 3:07 PM. 本次活动注册表 http:/ techmeetup-20150719.eventbrite.com. 本次活动详情链接 http:/ www.tech-meetup.com/events/20150719. Http:/ www.tech-meetup.com/wechat. And http:/ www.tech-meetup.com/signup. Time: 1:30PM 3:40PM, 07/19/2015, Sunday. Location: 1320 Ridder Park Dr, San Jose, CA 95131. 1:30pm - 1:50pm: Reception and social time. 1:50pm - 2:30pm: Session 1 by Bin Fan. Is a software en...
tech-meetup.com
Events - 湾区同学技术沙龙
http://www.tech-meetup.com/events
希望参加活动的同学,在参加活动前能事先在免责申明 http:/ tinyurl.com/wtm-waiver. CoreOS rkt, a Container Runtime. Posted Oct 27, 2015, 8:23 PM. By 郭晓峰 [ updated Oct 28, 2015, 8:06 AM. Http:/ tech-meetup-11-08-2015.eventbrite.com. Http:/ www.tech-meetup.com/events/11-08-2015. Http:/ www.tech-meetup.com. Yifan is a maintainer of the rkt project at CoreOS(. 1:30PM 4:00PM, 11/08/2015, [Sunday]. 97 E Brokaw Rd, Ste 210, San Jose, CA 95112. 1:30pm - 2:00pm: Reception and social time. 2:00pm - 3:30pm: Talk and QA. 97 E Brokaw Rd, Ste 2...
ndolgov.blogspot.com
Nikita Dolgov's technical blog: April 2015
http://ndolgov.blogspot.com/2015_04_01_archive.html
Nikita Dolgov's technical blog. Apr 19, 2015. SparkSQL as a foundation for analytics server. Query engine techniques we know and love. As the core computational model. Optimizations biased towards in-memory processing. Column oriented storage and data transfer; lots of compression [2]. Once in memory, data is held in primitive type arrays; no Java auto-boxing ever. Operators implemented to operate on batches of rows; hot loops compiled into byte code [3][4]. The DataFrames API accepts both SQL expression...