How to load data from Typeform to DynamoDB

Learn how to use Airbyte to synchronize your Typeform data into DynamoDB within minutes.

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 Typeform connector in Airbyte

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

Set up DynamoDB for your extracted Typeform 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 Typeform to DynamoDB 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.

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

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.

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 Typeform

First, log into your Typeform account and navigate to the form data you wish to export. Use the built-in feature to export your response data in a CSV format. This can typically be done by selecting the form, going to the results or responses section, and choosing the export option. Save the CSV file locally on your computer.

Step 2: Set Up AWS CLI and SDK

Ensure that you have the AWS Command Line Interface (CLI) and AWS SDK installed on your computer. These tools will be used to interact with AWS services. You can install the AWS CLI from the official AWS website and use package managers like pip for Python-based SDKs. Configure the CLI with your AWS credentials using `aws configure`.

Step 3: Create a DynamoDB Table

Log into your AWS Management Console, navigate to DynamoDB, and create a new table. Define the primary key attributes that will uniquely identify your records. For instance, you might use a form response ID or a combination of participant ID and timestamp. Note the table name and key structure for use in later steps.

Step 4: Parse CSV Data

Use a scripting language like Python to parse the exported CSV file. Libraries such as `csv` in Python can help read the CSV data and transform it into a dictionary or JSON format. This transformation is crucial for preparing the data for insertion into DynamoDB, where it needs to be in a JSON-like format.

Step 5: Prepare Data for DynamoDB

Adjust the parsed data to match the schema of your DynamoDB table. Ensure that each data item includes the primary key attributes and any other necessary fields. Convert data types as required by DynamoDB, such as changing number fields to integers or floats and ensuring strings are properly formatted.

Step 6: Write a Script to Insert Data

Use the AWS SDK for your chosen programming language to write a script that inserts data into DynamoDB. For Python, for example, use `boto3` library. The script should iterate over each data item from the parsed CSV and use the `put_item` method to add it to the DynamoDB table. Handle exceptions to manage any errors during insertion, such as duplicate keys or format mismatches.

Step 7: Run and Validate the Data Transfer

Execute your script to transfer the data from the CSV file to DynamoDB. After running the script, validate the data transfer by checking the DynamoDB table through the AWS Management Console. Ensure that all records are correctly inserted and that the data integrity is maintained by comparing a few sample records with the original CSV data.

This guide provides a structured approach to manually transferring data from Typeform to DynamoDB using AWS tools and programming.