Apparently for a while their internal tech org was a number of fiefdoms with huge number of contractors and no central vision. They have changed that post-bankruptcy, bringing in serious operators in the exec team building the org, product and culture from scratch.they are hiring product, data and ml folks. Please dm me if interested in working on this, happy to connect
I dont have the source at hand, but soviets also preffered republicans to the left, since they could always make a dral with republicans while the lwft was too ideological.
Well, a good question is what are you offering the senior applicants, other than (hopefully competitive) money for their time. Are there interesting problems to solve? Is there potential for growth?are they going to have a say in the technical direction and strategy? How about the business side?
Think of better ways to formulate the opportunity, your pitch, and ways to stand out in the market
To further add detail, the un resolution is focused sqarely on kelbajar region, which in any negotiation logic (madrid, kazan, etc) was slated to return to Azerbajan once the status of Karabakh was determined
Judt start doing it in your current role. Be the person who sees the end to end, knows how to deliver it, and show others how to do it. The person writing tickets probably is tired of doing that. Help him out. Make connections with business side. Explain to them the technical nuance in human terms. Work the role. Next time when recruiting, tell your story. The title doesnt matter - principal engineer, tech lead, requirements ninja, all the same
My partner and I capitalized on dropping rents and just moved into a large 1br with amazing views and garage in SF, for a marginal increase in rent. I am hoping that with tech moving out and commercial real estate getting cheaper, the new generation of artists will come to the city, bringing with them the weird culture, the creatives, the experimental. Its not the first boom of this town and after every bust the city heals itself to something even better.
I run a team of data engineers, and over the years there has been a lot of confusion between what is a data scientist and what is a data engineer.
I draw the divide in that data scientists discover the features and the methodology, while data engineers take these insights to production. One can argue that data scientists themselves could do that, but this is constrained by the domain expertise on tools(be that the depth of spark internals or whatever) and the number of hours in the day. It's hard enough to deal with the variance of the models to deal with the variance of the system.
A good data engineer is a unicorn.I define three central competencies for a data engineer:
be a good coder: quality, maintainability, efficiency,
know how to explore the data: SQL, R, just eye the damn data feed,
know enough data science to interface with scientists
For a data engineer it's okay not to know probability theory and stats that much, but its a must for a data scientist( running TensorFlow out of the box with no understanding of the underlying math doesn't make a data scientist, just a common butcher).
that sounds like a non-canonical use of clickhouse. Wouldnt a good RDBMS be a better fit for invoice data? This is on the surface, of course, really interested in what is this invoice data like, and what queries are you trying to run on them.
The article assumes that the noise was added intentionally for obfuscation - however, for realtime size estimation facebook would have to rely on some sort of orobabilistic data structures, as sketches, and its wuite possible that the authors are observing the accuracy loss thats coming from these data structures.
One can argue that FB doesnt need to use probabilistic data structures for estimating the size of a small set of externally provided PII, but they probably need to keep them at hand in case they need to intersect with geo-demo sets.e.g one uploads a large list of emails, and wants to intersect that audience with the set of males in san francisco.
“Data pipelines die quietly” is a more appropriate name for the article based on the example. And its true, all data pipelines need counters monitored continuously. A simple <number of records that didnt parse> metric on grafana would prevent this error.
I remember an article which was saying that a lot of startups are just solving problems for the 'millenials' that their moms used to solve. Laundry, making food, making appointments, cleaning, etc. Now there is a startup that will sign your grade sheet and tell you to do homework.
MapR is great while you are in their ecosystem - once you step outside and try to bring in technology thats not supported by them, you run into little showstopper bugs and complete lack of documentation and community support.
Its still there - storage is still mainly HDFS, but computation layer morphed into Spark on Yarn (Mesos as a scheduler is barely used). Barely anyone runs classic Mapreduce jobs, Spark all around, written in Scala(or java or python).
There was a talk by Steve Blank where he was explaining US investment into radio astronomy(arecibo for example) by military projects aiming to establish the signatures and order of battle of soviet radars. They would measure the reflection of radar signals from the moon.
Looks like another "cherry-pick your customer" fin-tech startup. They do extremely well in the first stages of growth, but exhaust the potential market pretty fast and then go into turmoil when the VC's rightfully demand growth. With CAC in hundreds of dollars, the well dries up fast. I would be delighted to see them break this barrier and flourish.