Couchbase vs Google BigQuery

• Couchbase performance is exceptional both for in-memory and persisted transactions.
• Handling of node failures and cluster rebalancing (high availability).
• Enterprise support from Couchbase themselves
• Good documentation
• Streaming of bucket (database) level mutations via their Database Change Protocol (DCP).
• Replication of datasets between native clients and Couchbase buckets
• Handling of simultaneous writes to the same record with performance penalties

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• Huge plus point if you have idea running SQL scripts.
• The ability to store and manage multiple data warehouses is a big plus point which helps a lot for growing businesses.
• Easy integration with tools like Data Studio and Google Analytics which provides great data warehouse and data management solutions.

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• The N1QL engine performs poorly compared to SQL engines due to the number of interactions needed, so if your use case involves the need for a lot of SQL-like query activity as opposed to the direct fetch of data in the form of a key/value map you may want to consider a RDBMS that has support for json data types so that you can more easily mix the use of relational and non-relational approaches to data access.
• You have to be careful when using multiple capabilities (e.g. transactions with Sync Gateway) as you will typically run into problems where one technology may not operate correctly in combination with another.
• There are quality problems with some newly released features, so be careful with being an early adopter unless you really need the capability. We somewhat desperately adopted the use of transactions, but went through multiple bughunt cycles with Couchbase working the kinks out.

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• One issue with Google Cloud Storage is its price. For one to have that premium Google Cloud Storage, for the purpose of massive storage, he/she must have adequate cash. Otherwise, Google Cloud Storage is a safe and perfect online storage platform.
• The only thing that can come to mind that would be annoying with this software was that sometimes when trying to share files on the Cloud with coworkers, it would just not share at all, or there would be a massive delay in when I shared them and when they received them. Other than that though, everything is perfect with this.

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• Great performance.
• Leading Couchbase Lite capabilities for mobile use.
• Developers' learning curve with replica reads and multi cluster can be long. Needs guidance and nurturing.
• Cluster maintenance during OS patching, etc. has multiple ways to approach. Operational teams may need some guidance.

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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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