# Connect Data

Connecting data is the first step in the seven-step workflow. Add data by uploading files directly or by connecting to an external source. RootCause supports connecting to databases, data warehouses, cloud storage or APIs.

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## The Import Manager

From your workspace Home, click **+ Import Data Sources** on the Data Sources card. The Import Manager opens with two tabs:

* **Current Imports** — data sources already connected to this workspace
* **New Import** — add a new source

Filter by category: **All Sources**, **Databases**, **Cloud Storage**, **File Upload**, and **APIs & Services**.

<figure><img src="/files/Hv6OfYz7QpLEm4kmK7V8" alt="Import Manager showing all available data source connectors"><figcaption><p>The Import Manager lists all available connectors. Sources marked Setup need credentials configured before use.</p></figcaption></figure>

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## File Upload

File upload is the quickest way to get started. RootCause accepts CSV, Excel, Parquet, JSON, PDF, and image files.

1. In the Import Manager, click **Local File Upload**
2. Drag files onto the drop zone, or click to browse

<figure><img src="/files/iqYswMKDW2Km7cfqiScq" alt="File upload drop zone"><figcaption><p>Drop files onto the zone or click to browse. You can add multiple files at once.</p></figcaption></figure>

3. Files appear in a list with names and sizes — remove any with the **×** button
4. Click **Upload (n)** to start the transfer

<figure><img src="/files/SPbCJ4FrE7lI25LTjjXn" alt="Two files staged and ready to upload"><figcaption><p>Files staged and ready. The Upload button shows the number of files queued.</p></figcaption></figure>

An upload progress panel appears in the bottom-right of the screen. When all files show "Completed", ontology processing begins automatically in the background.

<figure><img src="/files/H2eA9lNLuIvBevOu1nD3" alt="Upload progress panel showing files uploading"><figcaption><p>Upload progress panel. A badge on the Progress icon in the left sidebar indicates that ontology processing is still running.</p></figcaption></figure>

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## What happens after upload

RootCause processes imported data in three steps:

1. **Schema detection** — column types are inferred (Number, Text, DateTime, Boolean, Category)
2. **Ontology building** — each column becomes a concept; columns with shared names or matching patterns across datasets are flagged for review
3. **Data Views created** — a default Data View is generated for each dataset; datasets that share an identifier column get a recommended joined view (covered in [Step 3: Build 360 Data Table](/user-guide/data-views.md))

When processing finishes, your workspace Home updates to show your datasets, Data Views, and any recommended joins.

<figure><img src="/files/B1gupZ4gJGpdEgD52Xjv" alt="Workspace Home after upload showing datasets, data views, and a recommended join"><figcaption><p>Workspace Home after processing. Two datasets, two auto-created Data Views, and a recommended linked view based on a shared Customer Id field.</p></figcaption></figure>

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## Data Connectors

For live, continuously synced data, connect directly to your source system. Each connector requires credentials — click **Setup** next to the connector name to configure.

**Databases**

* [PostgreSQL](/data-connectors/postgresql.md)
* [MySQL](/data-connectors/mysql.md)
* [MongoDB](/data-connectors/mongodb.md)

**Data Warehouses**

* [Snowflake](/data-connectors/snowflake.md) *(Beta)*

**Cloud Storage**

* [Amazon S3](/data-connectors/s3.md)
* [Azure Data Lake](/data-connectors/azure-data-lake.md)

**APIs & Analytics**

* [REST API](/data-connectors/rest-api.md)
* [Google Analytics](/data-connectors/google-analytics.md)

**Coming soon:** BigQuery, Azure Synapse, Databricks

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## Live Data Sync

After connecting a source, configure its sync frequency — manual, hourly, daily, or weekly. When a sync runs, all dependent Data Views and analyses update automatically.

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## Next steps

By the time import finishes, RootCause has already built a first-pass ontology. [Step 2: Build Ontology](/user-guide/ontology-concepts.md) covers how to review the auto-detected concepts, merge columns that represent the same thing, and classify identifiers and time fields.

After that, [Step 3: Build 360 Data Table](/user-guide/data-views.md) shows how to turn those datasets into a single joined table ready for causal analysis — using the recommended join RootCause has already prepared.


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