这是为什么呢？与此 SO 站点中的 OLAP 问题相比，与 OLTP 相关的问题的问题频率（数量）高出 2 个订单。
这是显而易见的。 OLAP 很容易。 OLTP 很难。
C# 也是最难的编程语言。同样明显。
How does it relate to the cost of corresponding (by size and level of of data of) OLTP database implementation?
The question makes no sense. You might as well ask how OLAP implementations compare with buying a new Bentley Continental Automobile. Or ask how OLAP implementations compare with an SAP ERP implementation. Or ask how OLAP implementations compare with a vacation in the South of France.
There's nothing comparable between OLAP and OLTP except that they both use a database.
Is it higher/lower? how many times?
Yes. It can be higher, lower or the same. It depends on the scope of work, not the database architecture.
Why are the OLAP solutions rather very rare in comparison with OLTP ones?
According to whom? Everyone who implements a "reporting" system that is attached to their transactional system is doing OLAP. Many, many applications are in two parts: the core transactional part and some reporting add-ons.
I wouldn't call using Business Objects or Cognos "very rare".
Does the laboriousness and costs of OLAP seem too prohibitive?
OLAP costs depend on the scope of work. There's nothing inherently prohibitive. If you install BO to do some reporting, the cost is very small. If you create a large enterprise-wide warehouse, the cost is large.
Why are the costs so elevated?
Compared with what? Companies can spend $80M US implementing SAP. That's higher. But not comparable. Companies can spend nearly $0 (under $100K) implementing a free open-source component. That's lower. But not comparable.
Why is it? OLTP related questions has 2 orders more frequency(number) of questions compared to OLAP ones in this SO site.
That's obvious. OLAP is easy. OLTP is difficult.
Also C# is the most difficult programming language. Equally obvious.
Why is it? OLTP related questions has 2 orders more frequency(number) of questions compared to OLAP ones in this SO site.
Could it be that generally experienced database developers do OLAP while OLTP is often done by application programmers with little or no database knowledge? So there is less need to ask questions and the questions they have tend to be too complex for a forum.
Could there be fewer OLAP databases because they generally need one or more OLTP databases to pull data from and that OLAP implementations often consolidate data from many OLTP databases? Could it also be that small databases and databases on some subjects don't generally need data warehousing solutions at all? Generally there is no need to develop a data warehouse if the reporting needs perform adequately aginst the OLTP database, so it is generally only as the amount of data gets very large and the reporting gets very complex that these types of projects are initiated.
I can also assure you that our Enterprise OLTP databases cost considerably more than 3 million to develop. By comparison our OLAP databases were much cheaper. We have spent less that 5% of the development time on OLAP than on OLTP. Maybe even less than 1%.
There's very little point in comparing the costs of two vastly different sets of requirements. Anyway, there is no such thing as an "average" project. OLTP refers to one type of database workload whereas OLAP refers to a set of technologies used for decision support applications. They are literally incomparable. Nor do I understand on what you are basing your idea that OLAP is "rare". OLAP is only one type of solution, inevitably the universe of all other database solutions is much bigger than just OLAP.
EDIT: Maybe you might rephrase the question this way: "Why is there less money spent on decision support database applications than there is on other types of database applications?". It depends how you define "decision support" of course. However, if you look at the proportion of most organisations' resources dedicated to "decision support" versus the resources used on doing other stuff then I think you will see the reason.
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这个问题没有意义。您不妨询问 OLAP 实施与购买新宾利大陆汽车相比如何。或者询问 OLAP 实施与 SAP ERP 实施相比如何。或者询问 OLAP 实施与在法国南部度假相比如何。
OLAP 和 OLTP 之间没有什么可比性，只是它们都使用数据库。
是的。它可以更高、更低或相同。这取决于工作的范围，而不是数据库架构。
根据谁的说法？每个实现附加到其事务系统的“报告”系统的人都在进行 OLAP。很多很多应用程序分为两部分：核心事务部分和一些报告附加组件。
我不会说使用 Business Objects 或 Cognos“非常罕见”。
OLAP 成本取决于工作范围。没有什么本质上是禁止的。如果你安装BO来做一些报告，成本是非常小的。如果创建一个大型的企业级仓库，成本就很大。
与什么相比？公司可以花费 8000 万美元实施 SAP。那更高。但没有可比性。公司可以花费近 0 美元（低于 10 万美元）来实施免费的开源组件。那更低。但没有可比性。
这是显而易见的。 OLAP 很容易。 OLTP 很难。
C# 也是最难的编程语言。同样明显。
The question makes no sense. You might as well ask how OLAP implementations compare with buying a new Bentley Continental Automobile. Or ask how OLAP implementations compare with an SAP ERP implementation. Or ask how OLAP implementations compare with a vacation in the South of France.
There's nothing comparable between OLAP and OLTP except that they both use a database.
Yes. It can be higher, lower or the same. It depends on the scope of work, not the database architecture.
According to whom? Everyone who implements a "reporting" system that is attached to their transactional system is doing OLAP. Many, many applications are in two parts: the core transactional part and some reporting add-ons.
I wouldn't call using Business Objects or Cognos "very rare".
OLAP costs depend on the scope of work. There's nothing inherently prohibitive. If you install BO to do some reporting, the cost is very small. If you create a large enterprise-wide warehouse, the cost is large.
Compared with what? Companies can spend $80M US implementing SAP. That's higher. But not comparable. Companies can spend nearly $0 (under $100K) implementing a free open-source component. That's lower. But not comparable.
That's obvious. OLAP is easy. OLTP is difficult.
Also C# is the most difficult programming language. Equally obvious.
难道OLAP是由一般经验丰富的数据库开发人员来完成的，而OLTP通常是由很少或根本没有数据库知识的应用程序程序员来完成的吗？因此，提出问题的需要较少，而且他们提出的问题对于论坛来说往往过于复杂。
是否可以减少 OLAP 数据库的数量，因为它们通常需要一个或多个 OLTP 数据库来提取数据，并且 OLAP 实现通常会合并来自许多 OLTP 数据库的数据？难道小型数据库和某些主题的数据库通常根本不需要数据仓库解决方案？通常，如果报告需求能够充分满足 OLTP 数据库的要求，则无需开发数据仓库，因此通常只有当数据量变得非常大且报告变得非常复杂时，才会启动此类项目。
我还可以向您保证，我们的企业 OLTP 数据库的开发成本远远超过 300 万美元。相比之下，我们的 OLAP 数据库要便宜得多。我们在 OLAP 上花费的开发时间比在 OLTP 上花费的开发时间还不到 5%。甚至可能不到1%。
Could it be that generally experienced database developers do OLAP while OLTP is often done by application programmers with little or no database knowledge? So there is less need to ask questions and the questions they have tend to be too complex for a forum.
Could there be fewer OLAP databases because they generally need one or more OLTP databases to pull data from and that OLAP implementations often consolidate data from many OLTP databases? Could it also be that small databases and databases on some subjects don't generally need data warehousing solutions at all? Generally there is no need to develop a data warehouse if the reporting needs perform adequately aginst the OLTP database, so it is generally only as the amount of data gets very large and the reporting gets very complex that these types of projects are initiated.
I can also assure you that our Enterprise OLTP databases cost considerably more than 3 million to develop. By comparison our OLAP databases were much cheaper. We have spent less that 5% of the development time on OLAP than on OLTP. Maybe even less than 1%.
比较两组截然不同的需求的成本没有什么意义。无论如何，不存在所谓“平均”的项目。 OLTP 是指一种类型的数据库工作负载，而 OLAP 是指用于决策支持应用程序的一组技术。他们确实是无可比拟的。我也不明白您认为 OLAP 是“罕见”的想法的依据是什么。 OLAP 只是解决方案的一种类型，所有其他数据库解决方案的范围不可避免地比 OLAP 要大得多。
编辑：
也许您可以这样重新表述这个问题：“为什么花在决策支持数据库应用程序上的钱比花在其他类型的数据库应用程序上的钱少？”。当然，这取决于您如何定义“决策支持”。然而，如果你看看大多数组织专门用于“决策支持”的资源与用于其他事情的资源的比例，那么我想你就会明白原因。
There's very little point in comparing the costs of two vastly different sets of requirements. Anyway, there is no such thing as an "average" project. OLTP refers to one type of database workload whereas OLAP refers to a set of technologies used for decision support applications. They are literally incomparable. Nor do I understand on what you are basing your idea that OLAP is "rare". OLAP is only one type of solution, inevitably the universe of all other database solutions is much bigger than just OLAP.
EDIT:
Maybe you might rephrase the question this way: "Why is there less money spent on decision support database applications than there is on other types of database applications?". It depends how you define "decision support" of course. However, if you look at the proportion of most organisations' resources dedicated to "decision support" versus the resources used on doing other stuff then I think you will see the reason.