Warp allows you to convert and analyze (very) large databases with ease at the speed of light. In Warp, you work on a small subset of the data, after which Warp repeats your actions on the entire dataset. Unlike most data-analysis apps, you do not have to type any codes in Warp.
Read data from files (e.g., CSVs), databases (MySQL, PostgreSQL, or SQLite) or even big data warehouses (RethinkDB and Facebook Presto)
Effortlessly juggle around data between files and databases by simply dragging-and-dropping! Load CSV files into MySQL or transfer a PostgreSQL table to a RethinkDB table by just dragging one to the other.
Efficiently analyze large datasets: Warp works closely together with databases to deliver the best performance.
Work faster by creating your analysis on a small subset of the data, then run it on all data with only a single click
Use the same formulas and techniques (such as pivot tables) you already know from Microsoft Excel
Easily re-run an analysis on different data, possibly from different sources
Easy-to-use, drag and drop interface, but the pro features are never more than a click away
You can now rank rows as well as calculate running aggregates (e.g., running average)
You can set a minimum cell size for aggregations (e.g., in pivot table) which helps preserve statistical anonymity
Warp can now read JSON files
The formula syntax for referencing columns has been simplified; simply use the column name if it contains only alphanumeric characters and starts with an alphabetic character; otherwise just use “[colum_name]” instead of “[@column_name]”
Likewise, the syntax for referencing foreign columns has been simplified to “#column” and “#[column_name]”
Presto performance has been greatly improved, as Warp now pushes down operations to Presto when multiple tables from the same server are involved
Warp now supports blob values
A native data type for lists has been introduced
Empty (NULL) values are now sorted consistently when sorting