Google Cloud BigTable vs. kdb+

It works great for any use case that requires high throughput for reads and writes, for key-value kinda data. This enables you to use it for usecases where you need to retain data for a long term or even short term but then use it in pipelines to enrich data, sync states between backend and frontend or to quickly lookup say a user.

Incentivized

kdb is well suited for real time tick data and time series analytics.

Incentivized

• Analytics: is at Google's heart. No on can beat Google in this space and BigTable is one of its implementation of this. The insights you gain from BigTable are simply usable in your day to day activities and can help you make real difference.
• Speed: Processing TBs and PBs of data under minutes needs real efficient platform which is capable of doing much more than just processing data. All this data cannot be processed by a single machine, but rather huge pairs of machines working in conjuction with each other. BigTable's implementation is one of the finest and allows you achieve great speeds!
• Interface: is great. Google has segregated required task under logically placed buttons which takes no time by users to understand and get habituated.

Incentivized

• Time series analysis. The built-in vector operations are extremely fast. Also with the q language you can code up any customized analytical ideas quickly.
• The database are all file based, very easy to maintain.
• Very solid and fast interface to websocket, so you can interface with javascript easily.

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• Global replication
• Complete SQL support
• Size limit of 1MB

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• Run time error message readability, particularly for new users.
• Backwards compatibility between versions.

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switching costs

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For big IT firms like us, data is very important and it only holds its value if it can make sense to us. Therefore, Bigtable's usability is priceless when it comes to decision making based on data.

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its ok

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Google provides premium support services for BigTable which is absolutely blazing fast similar to Bigtable's performance.

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We don't use it.

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As a hosted solution, that can be managed easily from the Google cloud platform, it enables the teams to think about business use cases vs thinking about the management of the database itself. It is easily to scale it up when you need more throughput or storage. This keeps the developers and the product folks happy.

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Python is very commonly used for large data analysis and in general is much easier to pickup than kdb+. The biggest drawback of kdb+ is the learning curve.

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• Building new features that can leverage storage on the edge
• Realtime decision making
• Data enrichment

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• It perfectly solves most of our real time tick data needs.
• Finding good kdb resources is slightly difficult. Also new people trying to learn kdb experience a relatively longer learning curve.

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