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Gated automated deployment (for MSSQL - dacpac via Azure DevOps, similar things exist for other DBs). Usually can be pre-tested if prod db copy is restored on lower environments. If anything, rollback can be made to previous deployment.

Either way, you need to designate someone who is knowledgeable to oversee the process. Automated deployment just makes this process way faster (thus "reducing bottleneck") where this person only needs to review code to see if anything suspicious isn't there and approve the deployment.

Manual deployments are prone to human error, especially under stress/time pressure. If manual deployment "package" (a set of sql scripts) is poorly written, there's huge incentive to "contact directly", which again could lead to manual errors.

The biggest drawback is culture which is the hardest ("we done this way for n years and we don't want to change").

So 2 tier applications?

I would like to ask if there are any updates for radio addresses. Right now Barrow, AK cannot be connected. It was interesting for me to listen the news they have at corner of earth. Thanks and keep up the good work.

Yes

Look at something like Tower Watson. I would say high barrier of entry.

Sorry, I disagree with you. Let's take forecasting. This is a _very_ hard topic and being applied correctly using actuarial methods it will beat ML. To give you some example, you cannot take sales data for IPhone X and predict its future sales. Reason is simple - market will change, IPhone 11 will come out cannibalizing IPhone X sales. So, you need to have 2 more variables into play. One is the lifetime of a product (approximated) and second is, you need to "put in one basket" several products. Having this, you can predict your sales for some specific product group, no magic ML needed. However this requires you to define "similar products". This is hard because currently it is done semi-automatically using human labor. In fact ML is actually the best one to automagically categorize products into groups, however I have not seen any implementations. To summarize, ML do has value, however not where everybody is pointing out.

Raw SQL queries will perform fast if you write correctly. No ORM will stand close. Stored procedures help a lot. It's close to C/C++ in a sense that is easy to shoot yourself in the foot.

On other hand, ORM helps to write code that can be unit tested and you can test that some data retrieval conforms to certain principles.

There was a thread about it on reddit, please go and find it. The trains do not need LIDAR or image recognition. Going in straight line on rails is simple. You don't need to even brake (useless, stopping distance is way too big). What happens in stations and what preparations are done, that cannot (e.g. cost efficiently) be automated. Metro on other hand is mostly automated (see Europe)

My impression so far is that TSQL, SSIS and .Net keeps world running. Frontend Angular/Bootstrap, but again frontend is not that important.

Let's take first example. As a bank - why it is in your interest to release that authority to blockchain when you are authority and existing infrastructure exists?

What's more. If you see their profile pic, you can image search google to check if that picture matches some social networks. Now you have phone->name identification.

Typescript is going to grow. Just sayin...

One little secret of retail/sales industry is that everyone knows that best salespeople are pathological liars. So I really doubt that they will be out of job, being con artist is just a plus.

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