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").
In the old days it would just be 2 separate applications. Unfortunately, with so much work being done with SaaS that suck in collaborative features (that should ideally be handled on a different layer, outside of the software), it has to be one app, so I guess it should be done in 2 tiers to be useful.
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.
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.
Hand-written SQL is not intrinsically faster than ORM generated SQL. The instances where ORM's have worse performance is typically due to application errors like n+1 queries or forgetting to batch updates. If you actually know what you are doing you can typically do the same optimization on the ORM level.
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)
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?
Even banks can be victims of inefficacy or corruption due to too many middle men. Furthermore, they can also have disputes about ownership of loans, property or money. In my country, there is a bank that is actively embracing and supporting blockchain: https://www.abnamro.com/en/about-abnamro/innovation/blockcha...
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").