Exporting Data

After your automation runs and extracts data, you need a way to get that data out of RTILA X and into a format you can use — spreadsheets, databases, APIs, or other tools. RTILA X provides a built-in Export Wizard that makes this process simple, plus automatic export via triggers for hands-free data delivery.

Viewing Extracted Data (Scraped Data Page)

All extracted data lives on the Scraped Data page. This is your central hub for reviewing, filtering, and exporting everything your automations have collected.

Opening the Scraped Data Page

1. Click Scraped Data in the sidebar (the chart icon).
2. The page opens with three dropdown filters at the top.

Filtering Your Data

Before you can see any data, you need to tell RTILA X which data you want to view:

Dropdown	What It Does	Example
Select Project	Choose which project’s data to view	“Amazon Product Scraper”
Select Run	Choose which execution session to view	“Jan 15, 2025, 3:42 PM”
Select Dataset	Choose which dataset to display	“products”, “reviews”

Important: You must select all three dropdowns before data appears. If any dropdown is empty, the table will show “No data found.”

The Data Table

Once you’ve selected a project, run, and dataset, your data appears in a table:

• Column headers match your dataset property names exactly.
• Each row represents one extracted record.
• Cells show the extracted value (truncated to 50 characters for display).
• Empty cells show “(Empty Required)” in red if the property is marked as required.
• Image columns show a small thumbnail preview with a download button.

Success Banner

After a successful run, a green banner appears at the top of the data table:

✨ Magic Complete! You successfully extracted X records.

If the dataset contains image properties with the Download option enabled, an additional notice appears:

🖼️ Images Detected — RTILA automatically downloaded these images to your local machine to prevent hotlinking issues.

Export Formats

RTILA X supports three export formats directly from the Scraped Data page:

Format	Extension	Best For	Opens In
CSV	.csv	Spreadsheets, data analysis	Excel, Google Sheets, LibreOffice
JSON	.json	Developers, APIs, data pipelines	Any text editor, programming languages
SQL	.sql	Database import	MySQL, PostgreSQL, SQLite

Note: Additional formats like XLSX and Parquet are available through File Format Triggers, which run automatically after each project execution.

CSV Format

CSV (Comma-Separated Values) is the most common format for spreadsheet data. Each row becomes a line in the file, with values separated by commas.

Example output:

title,price,image_url
"Wireless Headphones",29.99,"https://example.com/img1.jpg"
"Bluetooth Speaker",49.99,"https://example.com/img2.jpg"

CSV-specific options:

• Delimiter: Choose the character that separates values. Default is comma (,). You can also use semicolon (;), tab (\t), or any custom character.
• Overwrite vs. Append: Choose whether to replace the existing file or add new data to the end.

JSON Format

JSON (JavaScript Object Notation) is the standard format for data exchange between applications. Each record becomes a JSON object, and all records are wrapped in an array.

Example output:

[
  {
    "title": "Wireless Headphones",
    "price": "29.99",
    "image_url": "https://example.com/img1.jpg"
  },
  {
    "title": "Bluetooth Speaker",
    "price": "49.99",
    "image_url": "https://example.com/img2.jpg"
  }
]

SQL Format

SQL format generates CREATE TABLE and INSERT statements that you can run directly in a database.

Example output:

CREATE TABLE IF NOT EXISTS `abc123_products` (
  `id` VARCHAR(255) PRIMARY KEY,
  `title` TEXT,
  `price` TEXT,
  `image_url` TEXT
);

INSERT INTO `abc123_products` (`id`, `title`, `price`, `image_url`) VALUES
('rec_001', 'Wireless Headphones', '29.99', 'https://example.com/img1.jpg'),
('rec_002', 'Bluetooth Speaker', '49.99', 'https://example.com/img2.jpg');

Tip: The SQL table name is automatically generated from your project ID and dataset name, so you don’t need to worry about naming conflicts.

The Export Wizard

The Export Wizard is a step-by-step dialog that guides you through the export process.

Opening the Export Wizard

1. On the Scraped Data page, select your Project, Run, and Dataset.
2. Click the Export button.
3. The Export Wizard modal opens.

Step 1: Choose Scope

If your project has multiple datasets, you’ll first choose what to export:

Option	Description
Current Dataset	Export only the dataset you currently have selected.
All Datasets	Export every dataset in the project at once.

Step 2: Choose Format

Click one of the three format buttons:

• Export as CSV — Opens in Excel or Google Sheets
• Export as JSON — Perfect for developers and APIs
• Export as SQL — Ready to import directly into your database

Step 3: Configure (CSV Only)

If you chose CSV, an additional configuration step appears:

Setting	Description	Default
CSV Delimiter	The character that separates values. Enter , for comma, ; for semicolon, or \t for tab.	,

Step 4: Save

Click Save and choose where to save the file on your computer. The file is saved with a descriptive name like:

rtila_results_amazon_product_scraper_abc123.csv

Overwrite vs. Append (CSV Only)

When exporting CSV, you can choose how to handle an existing file:

Mode	What Happens	When to Use
Overwrite	The existing file is completely replaced with new data.	You want a fresh export each time.
Append	New data is added to the end of the existing file. Column headers are NOT repeated.	You’re building up a dataset over multiple runs.

Important: Append mode only works for CSV. JSON and SQL exports always overwrite.

Warning: When using Append mode, make sure the column structure hasn’t changed between runs. If you’ve added or removed properties, the appended data may not align correctly.

Exporting All Datasets

If your project has multiple datasets (e.g., “products” and “reviews”), you can export them all at once:

1. In the Export Wizard, select All Datasets.
2. Choose your format.
3. Click Save.

What happens for each format:

Format	Behavior
CSV	Each dataset is exported as a separate CSV file. File names include the dataset name (e.g., products.csv, reviews.csv).
JSON	All datasets are combined into a single JSON file, organized by dataset name.
SQL	All datasets are combined into a single SQL file with separate CREATE TABLE and INSERT statements for each dataset.

Automatic Export via Triggers

Instead of manually exporting data every time, you can set up triggers that automatically send your data to a destination after each project run.

How It Works

1. Your automation runs and extracts data.
2. When the run completes, RTILA X checks your project’s triggers.
3. Each trigger executes automatically, sending data to the configured destination.

Available Trigger Destinations

Category	Examples
Databases	PostgreSQL, MySQL, MongoDB
Cloud Storage	AWS S3, Google Cloud Storage, Azure Blob
Spreadsheets	Google Sheets, Excel
APIs	REST API, GraphQL, SOAP
Email	SMTP
Messaging	Slack, Teams, RabbitMQ, Kafka
File Formats	Export to file, Convert format, Compress

See the Triggers documentation for complete setup instructions.

Tip: Automatic export is perfect for scheduled automations. Set up a daily scraper with a database trigger, and your database updates automatically every day without you touching anything.

File Downloads (Download Option)

When a dataset property extracts a URL (like an image src), you can enable the Download option to have RTILA X download the file automatically.

Enabling Download

1. In the Visual Builder, open the Data Extraction tab.
2. Select the property that extracts a URL (e.g., an image src attribute).
3. Check the Download file if value is a URL checkbox.
4. Save your project.

What Happens During Execution

1. The property extracts the URL value (e.g., https://example.com/images/product.jpg).
2. RTILA X detects that the value is a URL.
3. RTILA X downloads the file using the browser’s network context (preserving cookies and session).
4. The file is saved to RTILA X’s output directory.
5. The extracted value in your data becomes the local file path instead of the URL.

Where Downloaded Files Are Saved

Downloaded files are saved to:

~/.rtila-x/output/downloads/

Each file gets a unique name with a timestamp prefix to avoid conflicts:

1705312345678-product.jpg

Download Behavior Details

Scenario	What Happens
Value is a valid URL	File is downloaded and local path is stored
Value is not a URL	Value is kept as-is (no download attempted)
Download fails	Original URL value is kept, error is logged
File already exists	File may be overwritten with a new timestamp prefix

Warning: Downloading many large files can slow down your automation significantly. Only enable Download for properties where you truly need the files locally.

Tip: Downloaded images are especially useful for product catalogs, where you want to save product images alongside the product data. The local file paths in your exported CSV/JSON make it easy to find each image.

Export Tips and Best Practices

Tip	Why It Matters
Export CSV for spreadsheet analysis	CSV opens directly in Excel, Google Sheets, and LibreOffice without any conversion.
Export JSON for developers	JSON is the universal data exchange format. Any programming language can read it.
Export SQL for database import	SQL files can be run directly in MySQL, PostgreSQL, or SQLite without any conversion.
Use Append mode for incremental builds	If you’re scraping daily, Append mode builds up your dataset over time without manual merging.
Export All Datasets for complete backups	One click exports everything, so you never miss a dataset.
Set up triggers for scheduled exports	Automatic export means you never forget to save your data.
Enable Download for image properties	Saves images locally so they’re available even if the source website goes offline.
Check the success banner	It tells you exactly how many records were extracted, so you can verify completeness.
Use descriptive project names	When exporting, the file name includes the project name. Descriptive names make files easier to find.
Test your export before scheduling	Run a manual export first to verify the data looks correct before setting up automatic triggers.