kdb+ vs. ScyllaDB

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

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Scylla is well suited for high-throughput scenarios where keyed data must be read or written with consistently low latency. It's less appropriate for use cases requiring relational queries, secondary indexes, or more structured data sets.

• 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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• Low-latency reads
• CQL has a familiar syntax
• Parity with Cassandra
• Practical features

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

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• Better documentation for best practices (e.g., how to effectively use connection pooling)

switching costs

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its ok

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Very easy-to-understand syntax--uses CQL (same as Cassandra), which has many similarities to standard SQL. There are some gotchas, however, that must be known during schema development.

We don't use it.

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The Scylla cloud support team is incredibly responsive and proactive.

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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Scylla has a quick learning curve (same as Cassandra) compared to other proprietary solutions like BigTable. It supports higher throughput and lower latency that other NoSQL databases like MongoDB, which sacrifice those features for more flexibility and unique features.

• 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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• Addresses latency requirements of our platform