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Have you considered the HDF5 data model? Edit for Louis : Why using HDF5 ? As stated in the HFDF short description page : HDF5 is a unique technology suite that makes possible the management of extremely large and complex data collections. HDF5 is a suitable solution when dealing with very large datasets and you need performance. Again, as stated ...


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In conventional parlance, fundamental values would refer to data about the stock's financials, i.e. the data contained in the financial statements. Examples could be the cash flow, assets, liabilities and so on. Technical values would refer to data obtained from the price history of the stock, for example a 50 day moving average of the price, or the ...


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For Q1 and Q2 I would suggest you should not use a columnar database. The reasons are as follows: A typical write-access for your data-type would need to update several symbols with both timestamp and price together in different tables. Due to the high cardinality of your data (low no. of duplicates), columnar compression techniques would not be able to ...


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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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