You don't even have to give us your email address. I basically just needed to drag and drop the files onto the file area, add the "transformations" and that was it. I had estimated the whole day for the implementation.
I've done projects like this a hundred times before. Just yesterday I had this client with a complex list reorganization: a couple dozen lists with several thousand data sets each, in all forms of CSV - with different delimiters and encodings - and Excel files had to be stacked, deduped, sorted, columns to be merged and removed and new columns to be added in order to create new improved lists. It has become my go-to tool to prepare both small and massive data sets for both ad-hoc analysis and cleaning data before putting it into a database or BI tool. I have saved hundreds of hours by quickly be able to merge, analyze and unify data. When it comes to quickly prep and unify data sets, even with millions of rows, nothing beats this tool. It's just amazingly quick and intuitive to build up some quite complex transforms. Easy Data Transform is intuitive, fast, and simply does the job. A script that initially took us days to write and test in our existing solution. It took us less than an hour to transfer a script that filters and merges different datasets to Easy Data Transform. It processed our ~100,000 rows of data in just seconds, where our existing solution needed minutes. None of the tests should report fail.We were surprised how fast Easy Data Transform is. There are both sequential and parallel regression tests. If installing locally, you will find the compiler under To build the compiler, use the following command. See the README.md file if you would like to build against a custom MLRISC library. The standard Unix installation of SML/NJ is mostly automated via a installation shell script and installer written in SML. To install SML/NJ on Windows using the installer, the Windows installation instructions. This command links the compiler against the MLRISC libraries provided with the SML/NJ installation. To install SML/NJ on macOS using the installer package, see the macOS installation instructions.
Then, to begin building, initialize the configuration script with the following commands while If you would like to explore other branches of the compiler, clone from the main repository instead: Note that SML/NJ 110.82's version of MLRISC is currently incompatible with ourĭownloading the Source Code Our GitHub mirror contains the latest stable release: Installation should include the MLRISC library. Version of SML/NJ (version 110.81) installed. Manticore is implemented in a mix of C and SML code. Manticore currently only supports the x86-64 (a.k.a.
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