everything you mentioned can be done easily through database schema.
window functions work well in SQL.
plots are easily done in any BI solution that hooks up to any database.
pandas is just poor man's SQL+BI. pandas stores everything in memory and has many limitations.
in SQL Server I can easily churn through terabyte sized database and get the data I need, because the schema is well designed with partitioned tables, clustered indexes and a well designed SQL takes less than a second to run against >1TB database. It even allows a team of more than 20 people to work with the same SQL and query it simultaneously.
i would love to see how you can analyze 1TB csv file, or a pile of 100 csv files totalling over 10TB where you have yet to discover the schema and how tables join together. and I am doing it with on a simple $700 workstation, not even using any hadoop nonsense
pandas is just poor man's SQL+BI. pandas stores everything in memory and has many limitations.
in SQL Server I can easily churn through terabyte sized database and get the data I need, because the schema is well designed with partitioned tables, clustered indexes and a well designed SQL takes less than a second to run against >1TB database. It even allows a team of more than 20 people to work with the same SQL and query it simultaneously.
i would love to see how you can analyze 1TB csv file, or a pile of 100 csv files totalling over 10TB where you have yet to discover the schema and how tables join together. and I am doing it with on a simple $700 workstation, not even using any hadoop nonsense