LinkedIn's company blog posts product announcements, open-source releases like Kafka and Voldemort, diversity data, and security breach disclosures.
HN commenters link it for the password breach, gender diversity measurements, and job title trends, and note its Kafka and Voldemort work.
I wouldn't discount that last reason. There's some research suggesting that friendships between coworkers can greatly increase worker engagement, productivity, and satisfaction.[0][1][2] And a self-report study from LinkedIn suggests that workers perceive workplace friendships as beneficial for both themselves and their work, for what that's worth.[3] Of course, it can also blow up in your face and do the opposite, occasionally slowing down more productive workers as they match their speed to those of their less productive friends when it comes to individual work.[4] That study looked at fruitpickers, but it could be relevant to the tech industry as well.
Paradoxically, at least in the US, we're seeing a trend towards fewer workplace friendships and greater detachment.[5] I/O (industrial and organizational) psychologists have a lot of interesting interpretations for those trends. Almost across the board, employment is far more transitory today than in past decades, so there's less pressure to invest time in meaningful relationships at work. And we're so connected nowadays that it's easier than ever to preserve relationships in some form (regardless of whether they're preserved, or we just believe they are), so we don't feel the need to generate replacements as much.
Having done a lot of freelance work myself for a couple of years, if I'm being entirely honest with myself, this is one of the disadvantages at times. I enjoy getting to work on different projects, but there's…
LinkedIn - Mountain View, CA
I'm hiring a team to help develop a new user-facing Data Product built on top of LinkedIn's massive dataset of 65 million member profiles, connection graph, recommendations, company database, and other rich information sources - both internal and external. I described a bit about what it is like working on our team earlier this month on the LinkedIn blog: http://blog.linkedin.com/2010/04/20/linkedin-data-scientist-skomoroch/
To accelerate the application process, feel free to ping me directly (info in my HN profile).
* Senior Software Engineer - Analytics Applications
http://www.linkedin.com/jobs?viewJob=&jobId=937403
Front end engineer working directly with me & the Data team. You will be wiring up an application and UI that serves millions of users, pulling data from sources like Voldemort and relational databases. Looking for solid developers who can quickly crank out a solid web app in Java, Rails, or Django. Should be someone who iterates quickly and thinks about scale. Interest in information design / interaction is a plus.
* Site Operations Engineer (Ruby on Rails)
http://www.linkedin.com/jobs?viewJob=&jobId=902262
Another role that might be of interest to HN readers: You will work closely with the Light Engineering Development team to configure, deploy, and monitor production-ready applications using any or many of: apache, nginx, memcached, mongrel, thin, Passenger Phusion, rabbitmq, redis, sphinx, and anything else it takes to…
>Thanks to such pressures and hard work, many Asian-Americans do end up in top universities—but not as many as their high-school performance would seem to merit. Some Asians allege that the Ivy Leagues have put an implicit limit on the number of Asians they will admit. They point to Asians’ soaring academic achievements and to the work of Thomas Espenshade and Alexandria Walton Radford of Princeton, who looked at the data on admissions and concluded that Asian-Americans need 140 SAT points out of 1,600 more than whites to get a place at a private university, and that blacks need 310 fewer points. Yet in California, where public universities are allowed to use economic but not racial criteria in admissions, 41% of Berkeley’s enrolments in 2014 were Asian-Americans and at the California Institute of Technology 44% were (see chart).
The picture painted by this article of the difficulties (or lack thereof) faced by whites in securing admission to elite private universities like Harvard, Yale, or Columbia is in stark contrast to the one painted by Ron Unz three years ago in his thorough analysis of university admissions policies.
While Unz did indeed find ample evidence of quotas restricting the admission of Asians, he also found evidence of what appears to be affirmative action favoring Jews:
>Most of the other Ivy League schools appear to follow a fairly similar trajectory. Between 1980 and 2011, the official figures indicate that non-Jewish white enrollment dropped by 63…
One of the most fascinating aspects of this has been watching LinkedIn's response. The latest blog post is an example of 'reassurance through obscurity' where a large number of words say very little.
http://blog.linkedin.com/2012/06/09/an-update-on-taking-steps-to-protect-our-members/
"First, it’s important to know that compromised passwords were not published with corresponding email logins. At the time they were initially published, the vast majority of those passwords remained hashed, i.e. encoded, but unfortunately a subset of the passwords was decoded. Again, we are not aware of any member information being published at any time in connection with the list of stolen passwords. The only information published was the passwords themselves."
Look, either LinkedIn knows that rest of the information was stolen or they don't. If it was stolen then people need to be told, not fobbed off with this 'only the passwords were published'. The publishing of the passwords is irrelevant. What was taken? The other possibility is that LinkedIn doesn't know what was taken.
"Under this team’s leadership, one of our major initiatives was the transition from a password database system that hashed passwords, i.e. provided one layer of encoding, to a system that both hashed and salted the passwords, i.e. provided an extra layer of protection that is a widely recognized best practice within the industry."
That doesn't address the issue of which algorithm is being used. At worst it…
We seem to be oscillating between "key value stores are the way!" and "relational databases are the way!"
Both have their places. If my notes were read as seeming to advocate the end of relational databases, please excuse me because that was certainly not my intent.
Of course all of those companies use relational databases. Often they're the best tools for the job and in an IT landscape as large as their respective setups, of course they're the right tool for several jobs. But they've also got problem domains where they've decided RDBMSes are not the best tools for the job, which was my point to begin with. This thread started off with the assertion, basically, that if you weren't using a RDBMS to store everything, you probably didn't know anything about databases and I feel like we've (including the original poster) been slowly narrowing in on something closer to reality.
As for the examples on the non-RDBMS side, BigTable, SimpleDB, Voldemort (+) and, well, the guy that I met with last week that wrote eBay Germany's search system and told me they had their own non-RDBMS storage system.
+ http://blog.linkedin.com/2009/03/20/project-voldemort-scaling-simple-storage-at-linkedin/
http://blog.linkedin.com/2015/06/17/measuring-gender-diversity-with-data-from-linkedin/?sf10026881=1
> Technology companies are generally considered to be some of the most sought-after employers in the world. We have an inherent “home field advantage” when it comes to recruiting the largest and richest talent pools of our most critical position: software engineers. However, despite this advantage, our data indicates that software engineering teams in tech have proportionally fewer women than several non-tech industries; namely healthcare, retail, government, education, and nonprofits. For example, a typical software engineering team in a healthcare company is likely to be 32% women, compared to 20% in technology. While these non-tech industries employ significantly fewer software engineers versus technology, their teams tend to exhibit greater gender parity.
You don't end up with 50% women in other industries with basically the same role without some sort of reason.