Couchbase vs Qubole

• 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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• From a UI perspective, I find Qubole's closest comparison to Cloudera's HUE; it provides a one-stop shop for all data browsing and querying needs.
• Auto scaling groups and auto-terminating clusters provides cost savings for idle resources.
• Qubole fits itself well into the open-source data science market by providing a choice of tools that aren't tied to a specific cloud vendor.

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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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• Providing an open selection of all cloud provider instance types with no explanation as to their ideal use cases causes too much confusion for new users setting up a new cluster. For example, not everyone knows that Amazon's R or X-series models are memory optimized, while the C and M-series are for general computation.
• I would like to see more ETL tools provided other than DistCP that allow one to move data between Hadoop Filesystems.
• From the cluster administration side, onboarding of new users for large companies seems troublesome, especially when trying to create individual cluster per team within the company. Having the ability to debug and share code/queries between users of other teams / clusters should also be possible.

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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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• We like to say that Qubole has allowed for "data democratization", meaning that each team is responsible for their own set of tooling and use cases rather than being limited by versions established by products such as Hortonworks HDP or Cloudera CDH
• One negative impact is that users have over-provisioned clusters without realizing it, and end up paying for it. When setting up a new cluster, there are too many choices to pick from, and data scientists may not understand the instance types or hardware specs for the datasets they need to operate on.

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