This is in many ways a smart way to understand the problem, but it doesn't mean that microsoft contracts mean you're stuck with bad software. There are several verticals where Microsoft and Azure actually were smart and chose a better software product to sell on their platform than what they had in house.
One example is when they stopped trying to develop a inferior product to EMR and Dataproc, and essentially just outsourced the whole effort to a deal made between them and Databricks. Because of this I assume many enterprise azure customers have better running data solutions in that space than they wouldve had they gone with just AWS or GCP.
On the other hand, having worked for Microsoft on an Azure team, there are plenty of areas that critically need a rewrite (for dozens of different reasons), and such a solution is never found (or they just release some different product and tell those with different needs to migrate to that), where they keep on building what can only really be described as hot-fixes to meet urgent customer demands that make it harder to eventually do said critical rewrite.
The Databricks thing was a ploy. They then pushed Azure Synapse Analytics and forced all internal teams to stop using Azure Databricks. Synapse was half baked and then they are now pushing Microsoft Fabric which is even less baked.
About a year ago the whole situation changed and Microsoft started to push everyone to their own Data Engineering solution (Fabric) that back then was really half-baked.
If there's a single section of the entire world where daylight savings makes the most, it's above and below the 45th parallel. This means the earliest sunrise is 9am in the winter what a horrible idea just to give people a little bit more sunlight when they'd still be out at work anyways.
I mean, it's objectively true that they can do this, especially when even mildly filtered down by incoming external data.
It's why you no longer need to speak with a person when reentering your home country in a lot of different places (israel being one of them, but also the EU, trusted travelers in the US through global entry, ect).
There's no need to counter it, the whole point is to hit the social aspect of being on these platforms. If even half the kids can't figure out how to make it work, then a massive part of the problem is solved because a much larger percentage are only using it due to network effects.
I can't speak about this being a current law, but there were laws in multiple US states at various times that prevented you from storing facial data on the server. In turn features like snapchat's face filters were doing all the relevant computation locally on the device (which back then was certainly a complicated achievement).
US tech companies are constantly under FTC audit relating to how they use user data. This is certainly not something that needs to be seriously worried about, certainly less so than say the way in which cameras placed all over cities are used to track all sorts of people or storing GPS locations attached to a specific devices UUID.
Isn't this essentially just trying to reinvent ERP (i.e. what SAP has built a 207 billion dollar company at time of writing on and 90% of fortune 500 companies along with endless other large organizations use).
One can argue that ERP as code is higher value than whatever it is right now, but to act like this is a totally new idea is insane.
I worked in a place where basically everything that happened in the company was implemented as actions within Lotus Notes.
While the choice of implementation and performance were abysmal (Notes was a great/the only choice when the decision was made but 25 years later not so much), the actual idea was amazing and it worked extremely well.
The iOS version of most social media apps is better. IOS simply has better API integration to it's hardware, where with android, many OEMs (hell this was even the case to a certain extent with older pixel phones), do a number of things that make the hardware not as easily accessible as quickly from the OS API for said feature.
This is especially relevant for the camera, but also various other sensors and hardware modules that exist inside these phones.
That said, in recent years there are just a number of other areas that android is much better at such as deeper AI integration, which goes back to even prior to the current LLM craze.
I'm originally from the US, but where I live now, whatsapp functionally replaced email for a lot of different types of communication (that would be an email in the US). Recruiters text me on whatsapp about jobs, I can ask for a prescription renewal through it, and I get support from everything ranging from a government agency to customer support for things from businesses, ect.
One thing that is repeatedly underdiscussed about open source is that every time you have a major open source project become successful, be that anything from Linux to Apache Spark, you have private companies who come in, build something that can very reasonably still be called Linux or Apache Spark, but underneath has tons and tons of extra stuff that they never feed back into the open source community.
Hell, I think with the later (since all major cloud providers deploy their own version of spark on their respective data processing cluster services), people don't even know that they aren't in fact using open source software. Hell, eventually you get to a point where companies that choose not to use these third party services eventually just open source their own improvements or abstractions as again separate open source projects that never make it into the upstream project (which are often times heavily influenced by profit making entities).
This has been the model for a very long time, going back to at least the likes of redhat. And certainly will be going forward with countless future projects. Maybe there needs to be new models of open source governance, but I have no clue how successful such a thing would even be.
It depends on what you were trying to with the data. Hadoop would never win, but Spark can allow you to hold all that data in memory across multiple machines and perform various operations on it.
If all you wanted to do was filter the dataset for certain fields, you can likely do something faster programmatically on a single machine.
One example is when they stopped trying to develop a inferior product to EMR and Dataproc, and essentially just outsourced the whole effort to a deal made between them and Databricks. Because of this I assume many enterprise azure customers have better running data solutions in that space than they wouldve had they gone with just AWS or GCP.
On the other hand, having worked for Microsoft on an Azure team, there are plenty of areas that critically need a rewrite (for dozens of different reasons), and such a solution is never found (or they just release some different product and tell those with different needs to migrate to that), where they keep on building what can only really be described as hot-fixes to meet urgent customer demands that make it harder to eventually do said critical rewrite.
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