Trusted by data-driven companies
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 you have the AWS SDK installed in your development environment. This SDK will allow you to programmatically access and retrieve data from your DynamoDB tables. You can install it using a package manager like pip (`pip install boto3`) for Python or npm (`npm install aws-sdk`) for Node.js. Configure your AWS credentials to access your DynamoDB service.
Use the SDK to initialize a DynamoDB client and query or scan operations to fetch the data you need. For large datasets, use pagination to handle multiple items efficiently. Ensure that your queries are optimized to reduce the amount of data transferred and to avoid timeouts.
DynamoDB data is typically in JSON format, but Weaviate has specific requirements for data structure, including schemas and classes. Transform your DynamoDB JSON data into the Weaviate format by mapping attributes from DynamoDB to the appropriate classes and properties in Weaviate. This may involve reformatting dates, converting data types, or restructuring nested objects.
If you haven't already, set up a Weaviate instance either locally or on a cloud service. Ensure that your Weaviate instance is running and accessible. Familiarize yourself with the REST API that Weaviate provides for data ingestion.
Before importing data, define a schema in Weaviate that reflects the structure of your data. This includes creating classes and properties that match the transformed data from DynamoDB. Use the Weaviate Console or API to create and manage these schemas.
Develop a script using Python, Node.js, or another language to automate the data ingestion process. Use HTTP requests to interact with the Weaviate REST API, sending your transformed data in batches. This script should handle authentication, data formatting, and error checking to ensure data is correctly ingested into Weaviate.
After running your script, monitor the data transfer process to ensure completeness and accuracy. Check Weaviate logs and your application's error handling to confirm that all data was ingested successfully. You may need to adjust and rerun your script if errors occur or if data needs to be re-ingested.
By following these steps, you can efficiently transfer data from DynamoDB to Weaviate without relying on third-party connectors, ensuring a custom and controlled data migration process.
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.
Amazon DynamoDB is a fully managed proprietary NoSQL database service that supports key–value and document data structures and is offered by Amazon.com as part of the Amazon Web Services portfolio. DynamoDB exposes a similar data model to and derives its name from Dynamo, but has a different underlying implementation.
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:
