Summarize
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 identifying and accessing the specific data you need from Flexport. Use Flexport's API to extract the data. This involves sending HTTP requests to the API endpoints that correspond to the data you require. Ensure you have the necessary API credentials and permissions to access this data.
Once you have extracted the data, transform it into a format that Snowflake can ingest, such as CSV or JSON. This may involve cleaning the data, ensuring consistent data types, and removing any unnecessary fields. Use scripting languages like Python or tools that are adept at handling data transformations.
Log into your Snowflake account and ensure that you have a dedicated database, schema, and table(s) ready to receive the data. If not, create these structures using the Snowflake web interface or SQL commands. Define the table schema to match the structure of your transformed data.
Since you can't directly load data from Flexport to Snowflake, use a cloud storage service like AWS S3, Google Cloud Storage, or Azure Blob Storage as an intermediary. Upload your transformed data file(s) to a dedicated bucket or container in your chosen cloud storage service.
Create an external stage in Snowflake that references your cloud storage location. This involves setting up credentials and permissions to allow Snowflake to access the storage service. Use the `CREATE STAGE` command in Snowflake to define this stage, ensuring that it points to the correct bucket or container location.
Use the `COPY INTO` command in Snowflake to load data from the staged files into your Snowflake tables. This command will read the data from the specified stage and insert it into the appropriate tables. Ensure that your data types and formats are correctly aligned to prevent errors during the load process.
After loading the data into Snowflake, perform checks to ensure that all data has been transferred correctly. This includes running queries to validate data integrity, checking for any discrepancies or missing records, and ensuring that the data matches what was extracted from Flexport. Make any necessary adjustments or re-load data if errors are found.
By following these steps, you can manually transfer data from Flexport to Snowflake without the use of 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.
Flexport is a full-service worldwide carriage forwarder and logistics platform using modern software to fix the user experience in worldwide trade and this platform is your supply chain source of truth. It makes managing global logistics as simple, maleable, and programmable as modern business demands. Flexport is completely full-service global freight forwarder and logistics platform using modern software to fix the user experience in global trade. Flexport is a certified freight forwarder that uses people and software to manage the complexity of international trade.
Flexport's API provides access to a wide range of data related to global logistics and supply chain management. The following are the categories of data that can be accessed through Flexport's API:
1. Shipment data: This includes information about the shipment, such as the origin and destination, carrier, mode of transportation, and estimated time of arrival.
2. Customs data: This includes information about customs clearance, such as the customs broker, customs clearance status, and any duties or taxes owed.
3. Inventory data: This includes information about the inventory, such as the quantity, location, and status of goods.
4. Purchase order data: This includes information about purchase orders, such as the supplier, order status, and delivery date.
5. Financial data: This includes information about invoices, payments, and other financial transactions related to the shipment.
6. Analytics data: This includes data related to shipment performance, such as transit times, delivery accuracy, and cost analysis.
Overall, Flexport's API provides a comprehensive set of data that can be used to optimize logistics and supply chain operations.
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:
