> For the complete documentation index, see [llms.txt](https://docs.cloudfabrix.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cloudfabrix.io/rda/introduction-to-rda/rda-terminology.md).

# RDA - Terminology and Artifacts

## RDA Terminology

![Terminology Reference](/files/-Ma8sxLnNlqXvB_0KeuV)

**Solution Packages**: Solution packages are  shareable bundles with data, pipelines, and configurations. 

**Pipelines**: Pipelines are data operation workflows or Directed Acyclic Graphs(DAGs) 

**Bots**: A bot deals with a particular task or a data function. There are three types of pre-fixes to a bot's name.

       **#: Bot is source filtered**. It means the data queries and filters are applied while querying the data from datasource.

      **\*: Bot is destination filtered**. It means the data queries and filters are applied after retrieving the complete data from the datasource. It is not an efficient approach when dealing with a large amount of data, however, it can be used if the datasource does not support filtering capability while querying the data.

      **@: API Endpoint.** This dataset is a wrapper for an API offered by a datasource.

**Dataset**: A dataset holds the data.

**Configurations**: A configuration stores information like credentials, which systems to connect, parameters, etc.

**Plugins**: A plugin defines which system to connect to and its credentials.

## RDA Artifacts:

| Artifact                  | Description                                                                                                                                                            |
| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Pipelines                 | A pipeline performs a specific sequence of tasks such as data ingestion,  analysing the data, data sanitization, data transformation including applying ML algorithms. |
| Datasets                  | A dataset is like a dataframe, a saved tabular data. It contains the data.                                                                                             |
| Dictionary Dataset        | A dictionary dataset is a dataset used for enrichment of other datasets.                                                                                               |
| Models                    | A model is a Machine Learning model produced as a result of ML training in a pipeline.                                                                                 |
| Bot Source Configurations | A bot source configuration captures the configuration (if any) and enlists a specific set of bots for automation.                                                      |
| Solution Package          | A solution package is a bundle of pipelines, datasets, formatting templates, ML models, Bot source configurations etc to accomplish a specific outcome.                |
| Data Streams              | A data stream is used to notify other pipelines or for exchange of data. Pipelines interacts with other pipelines using datastream.                                    |
| Traces                    | Traces can be used to track how the data moves through the pipeline. This captures how a pipeline interacts with defined systems, artefacts, etc.                      |