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Thinking about how to set up a flexible query library I wonder whether Google Colab might be a good way to organize, document, and run queries on BigQuery. I have never used notebooks before but the fact that they allow for a structured approach could be really beneficial. I assume (but would like to have verified all the same) that I can simply define all kinds of subqueries, CTE's, functions, etc, in different sections of the notebook and then lastly define the actual master query taking all of those in?
Not sure if I could reuse certain notebooks that specifically hold more often used CTE's and functions either. That would surely be a bonus and a nice way to create the fundaments of such a library.
Any insights would be highly appreciated!
drftr
You can use Google Cloud Client Library to interact with BQ in a pythonic way wthin Google colab. please check Link.
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Program A is good at collecting data while Program B, in another language, is good at creating REST APIs. Is it possible to connect these two with a single database that A and B will read and write to? Performance for database operations is not really an issue in my case.
Sure this is possible. Databases typically can handle multiple connections from different programs/clients. A database does not really care which language the tool that is making the connection is written in.
Short edit:
Also most databases support "transactions". Which are used to cover that different connected clients do not break consistency of your application data while reading and writing in parallel.
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I'm just starting to get into web development, and I am planning a website.
This website will have users that can edit data. Think of it like a tree:
Theres the organisation (company), then under the organisation there are users. Each user can have multiple "clients", and the user can edit data about the "client" and share that data. The type of data are numbers and text mostly, and possibly some images.
What database paradigm would be best suited to this? I was thinking documents or relational. I want low-cost, but also lots of room for horizontal (and possible vertical) scaling.
Thanks :)
Considering your requirement, Google Cloud SQL will be the best option for you. It provides data manipulation option and horizontal scaling.
Google Cloud SQL is a fully-managed database service that offers high performance, scalability, and convenience. Hosted on Google Cloud Platform, Cloud SQL provides a database infrastructure for applications running anywhere.
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I've already read a lot about it, but I'm particularly interested for Laravel and its price difference.
We run a web application based on Laravel and this already with the Google Cloud App Engine. For our web application we need a solid database. Here the question arose, which one?
What are the main differences between Firebase and GCP SQL? How is this reflected in the price?
I'd recommend heading into this GCP Databases page which has a cool matrix on what are the differences between solutions on GCP. It really depends on what kind of data you have and how scalable the solution needs to be.
There is also a nice decision diagram on this blog post which can be seen below. It feels like instead of using Firebase Realtime DB or Cloud Datastore, one should now be using the Firestore which is also integrated into the GCP UI experience.
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I want to build spell/query correction functionality. I have 49GB of indexed data into Solr. I want to create a mechanism just like Google - "Did you mean".
Example - If any user asks any question/query into search then my system/application gives the correct query and will correct all the misspell word from query/question.
So, Is Solr is best for this functionality? Or Is there any other tools for the same?
Is this beer the best beer of the world?
It depends!
The quality of the results from the solr spell checking functionality depends form your configuration and the language, you like to check.
And.. it depends on the data based on.
I spend a lot of hours to get an working environment for spechecking with solr (language: german). Finally only with the option "only more popular" this features gives an result with an satisfaction-rate of 75% .
What I'm trying to say: to find the best application for spell checking in your case: simply test several way.
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I am documenting a small itcl project. Due to shortcomings in itcl support in doxygen, and the fact that Ruff! does not support itcl, I am left with NaturalDocs and RoboDoc as the leading candidates. However, I don't want to pick an unsupported system, and was wondering which is going to be there in the long term?
What will be there in the long term? Who knows! It depends on how much people use it, really, as with all open source code systems. It should be noted that both the tools you refer to are really slow developing at this point: they do what they do and need little significant change to keep on doing it.
As far as I can see, ROBODoc requires that you do pretty much all the annotation work yourself, whereas NaturalDocs will derive a bit more for you. Not very much though; in particular, you will have to write plenty of annotations on things whichever route you use. (I've no particular experience with either though; I tend to prefer to maintain documentation in a separate file with something like doctools but that's a very different approach. I've also done nasty custom things in the past; you really don't want to use them.)