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There are many different databases out there, all specialized for different use cases. The main parts you should consider are: Using a time series database, since they can handle timestamped data (e.g. ticks) more efficient than any SQL solution can by using bucketing and other methods. Using a database with a good query language, for example to aggregate ...


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Apache Cassandra would be a good fit. It's a partitioned row store, where rows are organized into table using a partition key. It is common use case to store time series data, you could simply use ticker and a period as partition key. Cassandra is optimized for writes and it's easily scaled, but you need at least 3 servers for it to run.


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Apache Cassandra would be a good fit for storing real-time intraday data. It's a partitioned row store, where rows are organized into table using a partition key. It you use a schema where you store data for one ticker per row with partitioning by day or month (it has a limit of 2B records in a row), the operations in your questions would be very performant....



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