Query customers attributes

I need to create a system where the user creates dynamic filters based on our customer's attributes. There are more or less 30 possible filters and 30 millions of customers, but the number of customer increase every day and the attribute value can change every day too, so we have insert and updates in these set of data every day. Another thing is that I can create a new filter or remove. In this case we can use a relational database like Oracle and create a index for every colunm, but with inserts and updates every day, can I have a problem with performance? Should I use a search engine for this case like Elasticsearch? Or there is a recommended database or architecture for this use case? I need to return a count of customers that match these filters at most in 5 seconds. **EDIT** Some attributes: - Downloaded the app (boolean) - Credit card limit (number) - Last transaction (date) - Status (text) - Last access (date) - How many times used the credit card (number) - City (text) - Average transaction value (number) The user can use

>, =, <=

to filter or use

, like

IN ('New York', 'Seattle')