How to load data from Iterable to Convex

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

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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.
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Airbyte connections are:
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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 Convex 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 Convex 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.

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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.

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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.

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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.

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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.

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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.

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Tech Lead at Symend

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Chase Zieman

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"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."

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How to Sync to Manually

Step 1: Export Data from Iterable

First, log into your Iterable account and navigate to the ‘Export Data’ section. Select the data you want to export, such as user lists, event data, or campaign results. Choose the CSV format for your export, as this is a widely compatible format. Make sure to include all necessary fields to avoid missing critical data.

Open the exported CSV files in a spreadsheet application like Microsoft Excel or Google Sheets. Review the data to ensure all necessary fields are included and data types are consistent. Clean up any discrepancies or unnecessary columns that are not needed in Convex. Make sure the data is formatted correctly for easy import into Convex.

Before importing, set up the necessary database tables and fields within Convex to accommodate the data structure from Iterable. Log in to your Convex account and create tables that correspond to the data you exported, ensuring field names and data types match those in your CSVs.

If there are any fields in your CSV files that do not match the data types or structures supported by Convex, you will need to transform these. Use your spreadsheet application to adjust these fields, such as changing date formats, normalizing text fields, or splitting combined fields into separate columns.

Utilize Convex’s import functionality to bring your prepared CSV files into the database. Navigate to the data import section of Convex, select the prepared CSV files, and follow the prompts to map CSV columns to database fields. Ensure that the import settings match the data structure you set up in Convex.

After importing, review the data in Convex to ensure that all records have been accurately transferred. Check for discrepancies, such as missing records or incorrect data types. Use queries and reports within Convex to validate that the data is complete and functional as intended.

Finally, update any workflows or processes that depend on this data within Convex. This could include setting up triggers, notifications, or analytics dashboards that utilize the newly imported data. Ensure that the data is being utilized effectively to achieve your objectives within the Convex platform.

By following these steps, you can successfully move data from Iterable to Convex without relying on third-party connectors or integrations.