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."
Begin by creating an external stage in Snowflake. This stage will point to your Amazon S3 bucket. Configure your Snowflake environment to securely access your S3 bucket by providing the necessary IAM role or access keys. Use the `CREATE STAGE` SQL command with the required S3 URL and credentials.
Use the `COPY INTO` command in Snowflake to export your data to the S3 bucket. Specify the target file format (e.g., CSV, JSON, Parquet) and any necessary options for compression or partitioning. Ensure that the data is correctly formatted and stored in the S3 bucket as specified in your external stage configuration.
Set up AWS Glue by creating an IAM role that has permissions to access both the S3 bucket and other necessary AWS Glue resources. Attach policies such as `AmazonS3FullAccess` and `AWSGlueServiceRole` to the role, allowing Glue to read from and write to your S3 bucket.
In the AWS Glue console, create a Glue Crawler to catalog the data stored in your S3 bucket. Define the data source as the S3 bucket where you exported the Snowflake data. Configure the crawler to create a new database or update an existing one in the Glue Data Catalog.
Execute the Glue Crawler to scan the S3 bucket and populate the Glue Data Catalog with information about the data schema. This step ensures that your data is discoverable and accessible for further processing or querying within AWS.
Set up a Glue ETL job to process the data as needed. This job can transform, clean, or enrich the data based on your requirements. Use the Glue script editor or the Glue Studio visual interface to define the ETL logic. Ensure the job has the appropriate IAM role attached for S3 and Glue access.
Run your configured Glue job to perform the ETL operations on the data within S3. Monitor the job execution through the AWS Glue console to ensure it completes successfully. Once finished, your processed data will be available in the desired format or schema within the S3 bucket, ready for further use.
By following these steps, you can efficiently move data from Snowflake to AWS S3 using AWS Glue, 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.
Snowflake Data Cloud is a cloud-based data warehousing and analytics platform that allows organizations to store, manage, and analyze large amounts of data in a secure and scalable manner. It provides a single, integrated platform for data storage, processing, and analysis, eliminating the need for multiple tools and systems. Snowflake Data Cloud is built on a unique architecture that separates compute and storage, allowing users to scale up or down as needed without affecting performance. It also offers a range of features such as data sharing, data governance, and machine learning capabilities, making it a comprehensive solution for modern data management and analytics.
Snowflake Data Cloud provides access to a wide range of data types, including:
1. Structured Data: This includes data that is organized in a specific format, such as tables, columns, and rows. Examples of structured data include customer information, financial data, and inventory records.
2. Semi-Structured Data: This type of data is partially organized and may not fit into a traditional relational database structure. Examples of semi-structured data include JSON, XML, and CSV files.
3. Unstructured Data: This includes data that does not have a specific format or organization, such as text documents, images, and videos.
4. Time-Series Data: This type of data is organized based on time stamps and is commonly used in industries such as finance, healthcare, and manufacturing.
5. Geospatial Data: This includes data that is related to geographic locations, such as maps, GPS coordinates, and satellite imagery.
6. Machine Learning Data: This type of data is used to train machine learning models and includes features and labels that are used to predict outcomes.
Overall, Snowflake Data Cloud provides access to a wide range of data types, making it a versatile tool for data analysis and management.
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:
