An argument is an independent input in a formula that is used to determine the value of the formula output.
Pipelines equip your onboarding teams to send data from a Source Connector to a Destination Connector. You can also cleanup and restructure the data before it is sent to the Destination Connector using training examples, column mapping, and formulas. Pipelines can be set up on a recurring schedule or manually triggered to run at any time.
A Destination Connector is used to send data to a specific system. You can connect to various systems using Osmos, such as Amazon S3, BigQuery, Google Drive and more. Once you create a Destination Connector, you can connect it to one of your Source Connectors using a Pipeline, and send data from that Source Connector to the Destination Connector. You can also connect it to one of your Uploaders, and allow your end-users to upload clean data directly into the Destination Connector.
Uploader enables product teams to quickly build upload buttons and embed them into any web property. These smart uploaders come pre-integrated with Destination Connectors, including databases, SaaS applications, and filestores. Any data uploaded by the end user is saved directly into the destination system, post cleanup. The uploader also comes with an intuitive interface for your end-users to quickly clean the data using training examples, column mapping, and formulas.
Formulas are used to quickly transform and map your source data to columns in the output table. We have several predefined formulas (such as CONCAT, DATE, ADD, etc.) that can be used together to complete complex transformations. You can also use simple formulas to directly map source columns to output columns.
A Source Connector is used to access source data within a specific system. You can connect to various systems using Osmos, such as Amazon S3, BigQuery, Google Drive and more. Once you create a Source Connector, you can connect it to one of your Destination Connectors using a Pipeline, and send data from the Source Connector to the Destination Connector.
Training examples are used to quickly transform and map your source data to the columns in the output table. It involves providing examples of the desired output to teach our AI to detect a pattern and create a program that auto-populates the remaining cells for that column with the transformed data. You can provide several training examples for a given column to increase the complexity of the program learned by our AI.