> For the complete documentation index, see [llms.txt](https://docs.rootcause.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rootcause.ai/user-guide/connecting-data.md).

# 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.

***

## The Import Manager

Open the **Sources** section in the top navigation bar and click **Import data**. 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**. Alongside the external connectors, the list also offers **From Organization** — reuse datasets that already exist in your organization — and **Web Research**, which builds a table from AI-driven web research.

<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>

***

## File Upload

File upload is the quickest way to get started. RootCause accepts tabular files (CSV, TSV, Parquet, JSON, Excel, ODS), documents (PDF, Word, PowerPoint, Markdown, plain text), images, and video 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>

Upload progress is tracked in the Import Manager's **Current Imports** tab. When all files show "Completed", ontology processing begins automatically in the background.

<figure><img src="/files/H2eA9lNLuIvBevOu1nD3" alt="The Import Manager&#x27;s Current Imports tab showing both CSV files completed, with row and column counts"><figcaption><p>Current Imports. Each file reports its size, rows, and columns once the upload completes; <strong>View</strong> opens the imported source.</p></figcaption></figure>

***

## 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 Table](/user-guide/data-views.md))

When processing finishes, the **Sources** page lists your datasets with row counts, sizes, and an analysis status; the recommended join appears on the Ontology page's **Recommended Datasets** panel.

<figure><img src="/files/B1gupZ4gJGpdEgD52Xjv" alt="The Sources page after upload showing both datasets with 4,998 rows each, sizes, and analysis status dots"><figcaption><p>Sources after processing: both datasets imported and analyzed. The journey card in the corner offers the next steps — understand why, build a report, or build an app.</p></figcaption></figure>

***

## 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)
* [Google Cloud Storage](/data-connectors/google-cloud-storage.md) *(Beta)*

**APIs & Services**

* [REST API](/data-connectors/rest-api.md)

**Coming soon:** BigQuery, Databricks, Azure Synapse, Redshift, Salesforce, Hubspot, NetSuite, Kafka

***

## Live Data Sync

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

***

## 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 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.