[akalitenya]

LookML supports different databases as sources. Sometimes Looker adds its own LookML parameters for database - which makes it difficult to read the documentation.

For analysts who work with SQL queries, any wrapper on a language seems complicated. But in the case of LookML and BlockML, in a couple of days you already begin to understand how this works, and after a week you can not tear yourself away from using the product.

Looker and Mprove are in the middle between fully custom DIY solutions and simple query generators tools.

[segah]

@akalitenya the way I used to explain it to the technical audience is: would you rather program in a low-level language like C or in a high-level language like Java, Python, or Node? Well, if you are writing compilers, you might choose C, but if you are doing almost everything else, you want to choose a language that fits the purpose: python for data science, Node for web backend, etc. Same with LookML - it provides a language that is ideal for doing analytics in-database. SQL is a language for extracting and summarizing data - it is not an ideal language to preserve business logic in just as C would not be an ideal language to preserve backend web dev. work in.

[akalitenya]

@segah Good point. Based on your consulting experience, can you tell if there have been any cases when your clients chose another Business Intelligence tool instead of Looker, what are these tools and what are the reasons?

[segah]

that's the wrong question. People choose different tools for all kinds of reasons. And many of these reasons are totally valid. A tool, Looker or otherwise, is no panacea.