# Preparing your datasets
Once you’ve created or imported a dataset (through **Connect**), you can refine, transform, and organize it under the **Prepare** module. The left-hand panel lists available datasets and folders, while the central workspace offers specialized tabs: **Data**, **Metadata**, **Scripting**, **Data Fusion**, and **Schedule**. Each tab serves a unique purpose in preparing your data for analytics.
Understanding the Prepare page
1. **Datasets**: A list of datasets created. This left pane allows you to quickly search and sort datasets or folders by name.
* Datasets can be organized into folders.
* The icon of each dataset indicates the datasource type (e.g., Snowflake vs. CSV). Live datasets are indicated with a green dot.
* The archived folder at the end contains older or deprecated datasets. You can still access them but they’re separated for clarity.
* Click a folder to expand or collapse its contents.
* Select a dataset to open it in the central **Prepare** workspace.
* **Select dataset author(s):** Superusers can filter data assets by who created them using the **Author** filter. This filter is available across the **Dataset**, **Prepare**, and **Business View** tabs within the **Data** module. When you click the **Author** dropdown, it displays a list of all users who have created assets in that tab. Select one or more authors to filter the list to only show assets created by those users. Click **Reset** to clear the filter and return to the full list.
2. Displays the following options:
* **Create a new dataset:** On clicking this button, you will be redirected to **Data → Connect.** For more details, please check out [this](https://help.tellius.com/data/create-new-datasource) section.
* **Import dataset:** Click on this button to import the required dataset. (Only .zip files are allowed to import)
* **Create a new folder:** Creates a new folder to categorize the datasets. Provide a relevant name and add the required datasets from the available list.
New folder creation for datasets
3. **Actions performed on a dataset:** Click on the three-dot kebab menu and the following menu will be displayed. For more details, please check out [this](https://app.gitbook.com/o/S3VKMrzMgXbC36NqGRj8/s/JHwf1QFuv1BRPzfSnL2Z/~/changes/154/data/actions-that-can-be-done-on-a-dataset) page.
4. **Data tab:** Displays all the datasets, allowing you to add or modify transformation nodes (SQL, Python, type changes) and perform data preparation actions.
5. **Metadata tab:** Here, you can
* Add user-friendly display names (e.g., “Booking Date” instead of `BOOKED_DATE`), synonyms, and descriptions to columns.
{% hint style="success" %}
If you use Kaiya feature (where enabled), you can auto-generate synonyms, display names, and desciptions for large sets of columns.
{% endhint %}
* Choose relevant data types (string, numeric, date/time) to ensure proper aggregations. For example, if `BOOKED_DATE` is incorrectly typed as string, you can’t do date-based filtering or time-series analysis properly.
* Assign measures (numeric fields), dimensions (for grouping or filtering), and date columns. For more details about the distinction, check out [this](https://help.tellius.com/vizpads-explore/measures-dimensions-date-columns) page.
6. **Scripting tab:** After verifying metadata, you might need advanced transformations that exceed basic pipeline nodes. For example, you want to:
* Add custom columns with SQL or Python.
* Join multiple datasets (often more than two) based on business rules.
* Aggregate or filter big data beyond what’s feasible in a single pipeline step.
7. **Data Fusion tab:** Data fusion is intended for simpler merges, typically merging exactly two datasets in a point-and-click fashion, without writing SQL.
8. **Schedule:** Use the **Schedule** option to refresh and keep your data in sync with the most up-to-date information available. A user can choose from a set of refresh modes and have flexibility in setting the refresh schedule.
9. **Export (Writeback):** Think of this as “saving” the cleaned or transformed dataset outside of Tellius. Depending on which connector you pick, you’ll either generate a local file (e.g., CSV) or publish it to an external system (e.g., HDFS, Snowflake etc.). For more details, check out [this](https://app.gitbook.com/o/S3VKMrzMgXbC36NqGRj8/s/JHwf1QFuv1BRPzfSnL2Z/~/changes/155/data/preparing-your-datasets/writeback-window) page.
10. **Edit:** Transforms the page into **Edit** mode where you can edit and apply transformations to the selected dataset. For more details, check out [this](https://app.gitbook.com/o/S3VKMrzMgXbC36NqGRj8/s/JHwf1QFuv1BRPzfSnL2Z/~/changes/155/data/editing-prepare-data) page.
11. **Pipeline:** Visual representation of transformations or nodes that have been applied to the dataset.
If you click **Edit**, this area will show your pipeline steps (e.g., an SQL node, Python node, or partitioning settings).
12. **Search Columns:** Quickly filters the displayed columns by typing part of the name or label.
13. **Column headers:** Displays each column in the selected dataset where you can sort the columns or apply filters on the fly. For more details on editing the dataset, check out [this](https://app.gitbook.com/o/S3VKMrzMgXbC36NqGRj8/s/JHwf1QFuv1BRPzfSnL2Z/~/changes/155/data/editing-prepare-data) page.
14. **Footer:** Displays the datasource name, preview of the dataset rows and columns. Also, it displays the timestamps indicating the last dataset refresh and the dataset creation.
