So many people have much of their capital in housing which makes it extremely unpopular to try to lower the prices, so neither side is going to make this their top priority.
This makes me think of ‘Why a meritocracy is corrosive to society’ by Philosphize This!
It was very interesting to hear the downsides expressed, and how ingrained it is in society to the point that we don’t even notice.
Perhaps the most important point: how thankful we should be to have the skills to be highly valued in this environment, which will give you some empathy to the people who don’t.
https://open.spotify.com/episode/7ASBhftzNrJnFL0NV3Iqtu?si=c...
Does anyone know how accessible lidar scans are for hobbyists? eg. I would love stick it to a drone to scan my backyard even if it just shows me the rocks underneath
The short version is that if the bounties become too large they'll lose internal talent who can just quit to do the same thing outside the org. Another reason was that they can't offer competitive bounties for zero days because they'll be competing with nation states, effectively a bottomless bank, so price will always go up.
I don't know much about this topic, but surely there are some well structured bounty programs Apple could copy to find a happy middle ground to reward the white hats.
Can you elaborate more on the "roles" of the "new stack"?
To me dbt/dataform and airflow/dagster are quite similar, so why do you need one of each? fivetran/stitch/singer are all new
Great resource, thanks for sharing it! I will dig deeper into the resources linked here as there's a lot I have never seen before. The main topics are more or less exactly what I've found to be key in this space in the last 2 months trying to wrap my head around data engineering in my new job.
What I'm still trying to grasp is first how to assess the big data tools (Spark/Flink/Synapse/Big Query et.al) for my use cases (mostly ETL). It just seems like Spark wins because it's most used, but I have no idea how to differentiate these tools beyond the general streaming/batch/real-time taglines. Secondly, assessing the "pipeline orchestrator" for our use cases, where like Spark, Airflow usually comes out on top because of usage. Would love to read more about this.
Currently I'm reading Designing Data-Intensive Applications by Kleppman, which is great. I hope this will teach me the fundamentals of this space so it becomes easier to reason about different tools.