@Scale is a series of technical conference for engineers who work for scaling of data ie. large-scale storage systems and analytics. These events held throughout the year after some time interval. Engineers across the globle participate in this conference to make a strong scalable system, who works for million or billions of data. This community brings technical people together to openly discuss the challenges which they faced during development.
It includes following topics:
- Data
- Dev Tools & OPS
- Mobile
- Networking
- Performance
- Security
- Spamfighting
- Sustainability
- Video
- Web
Next @Scale conference starts on 31 August 2016. You can register athttps://atscaleconference.com. Engineers from various gaint companies participate in this event. They are DataBricks, Dropbox, Facebook, Flipkart, Google, Instagram, LinkedIn, NVIDIA, Pinterest, Uber, and more.
Last conference was on 02 June 2016. It has speakers from well known companies like Dropbox, Facebook, Google, Microsoft, Qumulo, Tableau, Twitter, and the University of Wisconsin-Madison. They spoke about visual data analysis, exabyte storage system, data processing and many more. Video of event is posted in facebook pagehttps://www.facebook.com/atscaleevents/
Sharing you few examples:
Dropbox, James Cowling talks about their product Exabyte Storage System. It is described in his blog. It has capacity to store large user data in petabytes.
Facebook, Jay Tang introduces new storage system, Raptor, which is being built to run interactive queries over trillions of rows within seconds. It stores data on flash which natively fits popular open source distributed SQL engine Presto.
Microsoft, Sriram Rao spokes about optimization of system resources for large scale computer cluster. He tells about Apache YARN. The system is self-configuring and tolerates failures of subcomponents while providing continued availability. This work has been contributed back to Apache YARN and ships with various Hadoop distributions.
Google, Frances Perry spokes about Apache Beam. It can handle both batch and streaming cases. It allows pipeline to be portable across multiple runtime environments.
Many engineers from top companies share their experiences
Everyone can post questions, comments and follow-ups on the @Scale Facebook page. You can get details at https://code.facebook.com/posts/253562281667886/data-scale-june-2016-recap/
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