Couchbase vs Google BigQuery

• Key/value data store. Couchbase is blazing fast at data access and retrieval when you know what keys you want to access.
• Data querying with N1QL. If you have your indexes set up right, searching your unstructured data can be really fast too.
• Management dashboard. The Couchbase platform has a great admin panel that provides tons of insights into how your cluster is performing.

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• How many pros can a person type? This storage program gives workers and students the reality of unlimited storage space. I have never came close to overfilling my google cloud storage because it's huge and the best. I can view anything I save on there from any of my internet devices which is very important.
• Depending on how you have the program set up - either online or through an application that lives on your desktop, dragging and dropping files to and from Cloud Storage couldn't be any more uncomplicated. Plus, new users who meet certain criteria - like updating personal security, or share the program receive additional free online storage.
• The array of tools is very impressive, intuitive to use, and well organized in the sense that you don't have to go looking for individual apps. They're all easily accessed via a single dropdown.

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• Should allow servers to change their roles on the fly - currently you will need to take down the server and re-assign roles
• SDK documentation is not as helpful and should have more examples
• No pre-compiled procedures - unlike in SQL Server, Couchbase does not have pre-compiled stored procedures therefore we have to run various test to optimize our application

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• Though it is SQL some syntax are different but they are getting used to after you use for some time.
• The legacy SQL is in beta state but can be used and you can run the query with simple SQL.
• More documentation is needed for using User-defined functions in Big Query.

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• We can implement the mobile app using community release.
• The peer to peer syncing technology saves our development time.
• The CRUD process will be stopped when index and view are growing rapidly, we have to delete index files and reindex again.

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• Google BigQuery has had enormous impact in terms of ROI to our business, as it has allowed us to ease our dependence on our physical servers, which we pay for monthly from another hosting service. We have been able to run multiple enterprise scale data processing applications with almost no investment
• Since our business is highly client focused, Google Cloud Platform, and BigQuery specifically, has allowed us to get very granular in how our usage should be attributed to different projects, clients, and teams.
• Plain and simple, I believe the meager investments that we have made in Google BigQuery have paid themselves back hundreds of times over.

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