

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.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- 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
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say


"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"


“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.”


“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria. The value of being able to scale and execute at a high level by maximizing resources is immense”
Ensure that your JSON file is properly formatted and accessible. For this example, let's assume the file is named `data.json` and contains an array of objects.
You'll need the MySQL connector to interact with your MySQL database from Python. Install it using pip:
```bash
pip install mysql-connector-python
```
Load and parse your JSON file using Python's built-in `json` module.
```python
import json
# Load and parse the JSON file
with open('data.json', 'r') as file:
data = json.load(file)
```
Use the MySQL connector to establish a connection to your MySQL server.
```python
import mysql.connector
# Connect to the MySQL database
db_connection = mysql.connector.connect(
host='localhost',
user='your_username',
password='your_password',
database='your_database'
)
cursor = db_connection.cursor()
```
Replace `'your_username'`, `'your_password'`, and `'your_database'` with your actual MySQL username, password, and database name.
Create a MySQL table that matches the structure of your JSON data. For this example, let's assume each object in the JSON array has `id`, `name`, and `age` fields.
```sql
CREATE TABLE IF NOT EXISTS people (
id INT PRIMARY KEY,
name VARCHAR(255),
age INT
);
```
You can execute this SQL command using the cursor object:
```python
cursor.execute("""
CREATE TABLE IF NOT EXISTS people (
id INT PRIMARY KEY,
name VARCHAR(255),
age INT
)
""")
```
#Step 6: Insert JSON Data into the MySQL Table
Iterate over the JSON data and insert each record into the MySQL table.
```python
# Insert JSON data into MySQL table
for record in data:
sql_query = "INSERT INTO people (id, name, age) VALUES (%s, %s, %s)"
values = (record['id'], record['name'], record['age'])
cursor.execute(sql_query, values)
# Commit the changes
db_connection.commit()
```
Make sure that the keys in `record` (i.e., `'id'`, `'name'`, and `'age'`) match the fields in your JSON objects.
Use try-except blocks to handle any exceptions and ensure that the database connection is closed properly.
```python
try:
# (Insert JSON data into MySQL table - see Step 6)
# ...
except mysql.connector.Error as e:
print(f"Error: {e}")
finally:
if db_connection.is_connected():
cursor.close()
db_connection.close()
print("MySQL connection is closed")
```
After running your Python script, verify that the data has been successfully transferred to the MySQL database. You can do this by querying the MySQL database using a MySQL client or a database management tool.
```sql
SELECT * FROM people;
```
This command will display the contents of the `people` table, and you should see the data from your JSON file.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
JSON (JavaScript Object Notation) is a lightweight data interchange format that is easy for humans to read and write and easy for machines to parse and generate. It is a text format that is used to transmit data between a server and a web application as an alternative to XML. JSON files consist of key-value pairs, where the key is a string and the value can be a string, number, boolean, null, array, or another JSON object. JSON is widely used in web development and is supported by most programming languages. It is also used for storing configuration data, logging, and data exchange between different systems.
JSON File provides access to a wide range of data types, including:
- User data: This includes information about individual users, such as their name, email address, and account preferences.
- Product data: This includes information about the products or services offered by a company, such as their name, description, price, and availability.
- Order data: This includes information about customer orders, such as the products ordered, the order status, and the shipping address.
- Inventory data: This includes information about the stock levels of products, as well as any backorders or out-of-stock items.
- Analytics data: This includes information about website traffic, user behavior, and other metrics that can help businesses optimize their online presence.
- Marketing data: This includes information about marketing campaigns, such as email open rates, click-through rates, and conversion rates.
- Financial data: This includes information about revenue, expenses, and other financial metrics that can help businesses track their performance and make informed decisions.
Overall, JSON File provides a comprehensive set of data that can help businesses better understand their customers, products, and performance.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: