How to load data from RSS to MySQL Destination
Learn how to use Airbyte to synchronize your RSS data into MySQL Destination within minutes.


Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
Building in-house pipelines
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes



Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.
Move Large Volumes, Fast
Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.
An Extensible Open-Source Standard
More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.
Full Control & Security
Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.
Fully Featured & Integrated
Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.
Enterprise Support with SLAs
Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.
What our users say

Raman Singh
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

Chase Zieman

“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
How to Sync to Manually
Step 1: Set Up Your Development Environment
Ensure you have a development environment with Python (or another programming language of your choice like PHP or Java) and MySQL installed. This guide will use Python for its simplicity and wide support for both RSS parsing and MySQL operations.
Step 2: Access and Parse the RSS Feed
Use Python's built-in `xml.etree.ElementTree` module or the `feedparser` library to fetch and parse the RSS feed. This involves sending a request to the RSS feed URL and processing the XML data to extract the relevant information like titles, links, publication dates, etc.
```python
import feedparser
rss_url = 'http://example.com/rss'
feed = feedparser.parse(rss_url)
for entry in feed.entries:
title = entry.title
link = entry.link
published = entry.published
# Add other fields as needed
```
Step 3: Design Your MySQL Database Schema
Plan and create a MySQL table schema that matches the data structure of your RSS feed. Use `VARCHAR` for text fields, `DATETIME` for date and time fields, and other appropriate data types for different pieces of information.
```sql
CREATE TABLE rss_feed_data (
id INT AUTO_INCREMENT PRIMARY KEY,
title VARCHAR(255),
link VARCHAR(255),
published DATETIME
-- Add other fields as needed
);
```
Step 4: Establish a Connection to the MySQL Database
Use a MySQL connector library compatible with your chosen programming language to establish a connection to your MySQL database. In Python, you can use `mysql-connector-python`.
```python
import mysql.connector
conn = mysql.connector.connect(
host='localhost',
user='yourusername',
password='yourpassword',
database='yourdatabase'
)
cursor = conn.cursor()
```
Step 5: Transform RSS Data to Match the SQL Table Schema
Prepare the data extracted from the RSS feed for insertion into your MySQL table. This may involve converting date formats or cleaning text fields to ensure they comply with the MySQL table schema.
```python
from datetime import datetime
for entry in feed.entries:
title = entry.title
link = entry.link
published = datetime.strptime(entry.published, '%a, %d %b %Y %H:%M:%S %Z')
# Transform other fields as needed
```
Step 6: Insert Data into MySQL
Use SQL `INSERT` statements to add the parsed and transformed data into your MySQL table. Ensure you handle exceptions and duplicates appropriately, possibly using `INSERT IGNORE` or `ON DUPLICATE KEY UPDATE` clauses.
```python
sql = "INSERT INTO rss_feed_data (title, link, published) VALUES (%s, %s, %s)"
val = (title, link, published)
try:
cursor.execute(sql, val)
conn.commit()
except mysql.connector.Error as err:
print(f"Error: {err}")
```
Step 7: Close the Database Connection
Once all data has been inserted, close the cursor and database connection to free up resources.
```python
cursor.close()
conn.close()
```
By following these steps, you can manually move data from an RSS feed into a MySQL database without relying on any third-party connectors or integrations.