Concepts

Data

There are 2 type of data structured and unstructured data. SEDGE helps you to work with both types of data. Structured data is a set of records created using a predefined (fixed) schema and is organized in a tabular format. SEDGE can handle structured data. Unstructured datasets are not stored in a structured database format and it’s not predefined through data models and data can be in a textual or a non-textual format. SEDGE can handle unstructured data in a textual format.

Projects

In SEDGE the project is the set of dataset(s), that is loaded for the purpose of analysis. The project files could be

• Single CSV / TSV files or

• Multiple CSV files

• Data loaded in a Database

CSV / TSV can be single files or they could be multiple files, which can be joined in together using primary key. In summary, the SQL query builder can be applied to the multiple csv files which are loaded, and they behave similar to the tables of a database. The database which can be read are MYSQL, MSSQL, Oracle, Postgres, MariaDB etc. The tables within database can be loaded and using the SQL query builder, they can be joined, sorted, grouped or filtered. In summer most of the SQL query expression can be applied to the datasets.

Formula Builder

Once the dataset is loaded, the users have to perform data wrangling, which is the art of cleaning and unifying messy and complex data sets for easy access and analysis. 80% of users time to perform analysis ends up in data cleaning, and this has been a laborious process, and SEDGE helps the users to perform these functionalities with ease. More than 100+ functions exist within SEDGE and the users can easily perform these functionalities with minimal / nil python programming knowledge.

The functionalities built into SEDGE includes various transformations, string manipulation, mathematical functions, grouping and filtering functionalities.