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merge intervals, 已经跟据 start sort好的
但是interval 可以是开区间,比如[2,3). leetcode上有类似的,小心点做
过了之后后面要video conference interview。4轮,前面3轮是来自3个组的engineer,最后一个是manager。
问题是要自己从几个大类里选3个组出来,本人有些使用hadoop经验, set up, maintain hadoop, 用hive, spark写过些处理xml的code,用R什么的做过些数据分析。感觉主要是data infrasctructure的工作,求教如何选3个组啊? Data Platform, Content discovery 合适么? 还有INFRASTRUCTURE OPERATIONS 里 DC Service (data center service)。但是INFRASTRUCTURE OPERATIONS 是不是对CS知识要求比较高?。。本人非cs本,os,network没上过。。data devlop/ analytic 的东西做的多些。
求版上大神指教!
CONSUMER PRODUCT & DEVELOPER PLATFORM
DEVELOPER PLATFORM/ These are the teams that build Fabric, Twitter’s platform for mobile developers. They provide a
suite of services including crash reporting, analytics, beta testing, digits (log in with phone number), Tweet syndication, and
a seamless developer integration of all of those features. Our mission is to make this ever-expanding universe of social media
data available via a consistent and reliable architecture so the world can realize the full potential of this amazing stream of
information.
GROWTH /Build Twitter into an indispensable daily habit for people everywhere regardless of device, connection, income
or location. Focus on onboarding, education, and helping users get to healthy states, globally as well as with market-specific
efforts.
CLIENTS / Increase performance, increase quality, and maintain the health and safety of web tools, mobile web client, and
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of the team, you’ll have the opportunity to improve
and extend these massively high-volume, real-time services, adding features and improving their efficiency and scalability.
DISTRIBUTION / We work towards increasing the performance and reliability of the Twitter App around the world, even
in remote locations with unreliable, cellular connections. This the best place to learn about high availability, high volume
distributed systems, mobile and server networking.
DATA PLATFORM / We provide a rich stack of libraries and systems to do asynchronous, large-scale data processing, both
in batch and real-time mode. We work with systems such as hadoop, and our next-generation evolution of Storm. Our work
enables easy-to-write, reliable data processing pipelines thereby enabling products that surface aggregate data (e.g., analytics
on your tweets). Our work also enables data scientists to do rapid, ad-hoc analysis on usage logs, and use the gathered
insights to improve our products.
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