Combining OLAP without OLTP (large aggregate queries + lots of real time updates) is the holy grail that Cassandra, for example, has addressed rather nicely.
Compressed bitmap indexes are awesome. Like most indexes, the updating random rows problem is best addressed using a log structured merge tree and amortizing your index updates. Just have an in-memory buffer of recently updated rows.
If you are doing mostly sums & counts type work and can deal with some level of inaccuracy, you can consider HyperLogLog...
Compressed bitmap indexes are awesome. Like most indexes, the updating random rows problem is best addressed using a log structured merge tree and amortizing your index updates. Just have an in-memory buffer of recently updated rows.
If you are doing mostly sums & counts type work and can deal with some level of inaccuracy, you can consider HyperLogLog...