

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
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
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say

Andre Exner

"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."

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."
First, ensure that your IBM Db2 database is up and running. Verify that you have the necessary permissions to access and export the data. You will need to know the database connection details such as the hostname, port, database name, username, and password.
If you haven't already, install the Db2 Command Line Processor (CLP) or any Db2 client tool on your machine. This will allow you to execute SQL queries and export data from your Db2 database. You can download the Db2 client from IBM's website.
Use the Db2 CLP to export the data you need. You can use the `EXPORT` command to output data into a CSV or another text-based format. For example:
```
EXPORT TO '/path/to/data.csv' OF DEL MODIFIED BY NOCHARDEL
SELECT * FROM your_table;
```
This command will export the entire table to a CSV file at the specified path.
Weaviate requires data to be in a specific JSON format. Write a script (using Python, Node.js, etc.) to convert the CSV data into JSON objects. Ensure that each object aligns with the schema you plan to use in Weaviate. Include necessary fields and data types.
Set up your Weaviate instance. This can be done locally using Docker or on a cloud provider. Make sure you have the necessary API keys and access tokens if your instance is secured. Define your schema in Weaviate according to the data structure you are importing.
Use Weaviate's RESTful API to import data. Write a script to post each JSON object to Weaviate. Here's a simplified example using Python and the `requests` library:
```python
import requests
import json
url = "http://localhost:8080/v1/objects"
headers = {"Content-Type": "application/json"}
with open('/path/to/data.json', 'r') as file:
data = json.load(file)
for item in data:
response = requests.post(url, headers=headers, json=item)
if response.status_code != 200:
print("Failed to import:", response.text)
```
Adjust the URL and port according to your Weaviate setup.
After importing the data, verify that all entries are correctly loaded into Weaviate. Use the Weaviate console or API to query the data and ensure that it matches what was exported from Db2. Check for any errors or discrepancies and repeat the import process if necessary.
By following these steps, you can successfully migrate your data from IBM Db2 to Weaviate without relying on third-party connectors or integrations.
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.
Specializing in the development and maintenance of Android, iOS, and Web applications, DB2’s AI technology offers fast insights, flexible data management, and secure data movement to businesses globally through its IBM Cloud Pak for Data platform. Companies rely on DB2’s AI-powered insights and secure platform and save money with its multimodal capability, which eliminates the need for unnecessary replication and migration of data. Additionally, DB2 is convenient and will run on any cloud vendor.
IBM Db2 provides access to a wide range of data types, including:
1. Relational data: This includes tables, views, and indexes that are organized in a relational database management system (RDBMS).
2. Non-relational data: This includes data that is not organized in a traditional RDBMS, such as NoSQL databases, JSON documents, and XML files.
3. Time-series data: This includes data that is collected over time and is typically used for analysis and forecasting, such as sensor data, financial data, and weather data.
4. Geospatial data: This includes data that is related to geographic locations, such as maps, satellite imagery, and GPS coordinates.
5. Graph data: This includes data that is organized in a graph structure, such as social networks, recommendation engines, and knowledge graphs.
6. Machine learning data: This includes data that is used to train machine learning models, such as labeled datasets, feature vectors, and model parameters.
Overall, IBM Db2's API provides access to a diverse range of data types, making it a powerful tool for data management and analysis.
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: