How to load data from Iterable to Snowflake destination

Learn how to use Airbyte to synchronize your Iterable data into Snowflake destination within minutes.

Try Airbyte CloudManual method (the hard way)

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.

Building in-house pipelines
Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
  • Brittle and inflexible
Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
After Airbyte
Airbyte connections are:
  • Reliable and accurate
  • Extensible and scalable for all your needs
  • Deployed and governed your way
All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Iterable connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Snowflake destination for your extracted Iterable data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Iterable to Snowflake destination in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
Try Airbyte CloudManual method (the hard way)

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

Talk to our team

Setup Complexities simplified!

You don’t need to put hours into figuring out how to use Airbyte to achieve your Data Engineering goals.

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.

Try Airbyte CloudManual method (the hard way)

What sets Airbyte Apart

Modern GenAI Workflows

Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

An Extensible Open-Source Standard

More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

Full Control & Security

Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Raman Singh

Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

Learn more
Chase Zieman headshot

Chase Zieman

Chief Data Officer

“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.”

Learn more

Rupak Patel

Operational Intelligence Manager

"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."

Learn more

How to Sync to Manually

Step 1: Export Data from Iterable

Begin by exporting the data you need from Iterable. You can use Iterable's API to extract data by making HTTP requests to the relevant endpoints. For example, use the `/api/export/data` endpoint to pull data, setting the necessary parameters to filter the data as needed. Ensure you have your API key ready and that you handle pagination if the data set is large.

Once you have retrieved the data from Iterable, transform it into CSV format. CSV is a universal format that Snowflake can easily ingest. Use a script in a language like Python or JavaScript to parse the JSON response from Iterable and write these records into CSV files, ensuring that the data is correctly formatted with the necessary headers.

After formatting the data into CSV files, transfer these files to a secure location where they can be accessed by Snowflake for loading. This could be an AWS S3 bucket, Google Cloud Storage, or an Azure Blob Storage. Ensure that your files are securely stored and that you have the necessary permissions to access them from Snowflake.

In Snowflake, create an external stage pointing to the location where your CSV files are stored. Use the `CREATE STAGE` SQL command, specifying the storage location details and authentication credentials. For instance, if using AWS S3, provide the S3 bucket name, path, and access credentials.

Before loading the data, create a table in Snowflake that matches the structure of your CSV files. Use the `CREATE TABLE` command to define the table schema, ensuring that the columns and data types correspond to those in your CSV files. Consider any necessary transformations that may need to be applied to the data.

With the stage and table prepared, load your data from the CSV files into the Snowflake table. Use the `COPY INTO` command to read from the stage and insert the data into your table. Ensure to handle any potential errors or data conversions during this process, and verify that all data has been imported correctly.

After loading the data into Snowflake, perform a series of checks to ensure that the data has been loaded accurately and completely. Run queries to compare record counts with the source data, check for data integrity, and validate that all fields have been populated as expected. Make any necessary adjustments or rerun the load if discrepancies are found.

By following these steps, you can manually move data from Iterable to Snowflake without relying on third-party connectors or integrations, ensuring a custom and secure data transfer process.