How to load data from HubSpot to DynamoDB

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

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.

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 HubSpot connector in Airbyte

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

Set up DynamoDB for your extracted HubSpot data

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

Configure the HubSpot 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

Andre Exner

Director of Customer Hub and Common Analytics

"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."

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 HubSpot to DynamoDB Manually

Start by logging into your HubSpot account. Navigate to the data you want to export, such as contacts or deals. Use the HubSpot export feature to download the data in a CSV format. This CSV file will act as your raw data source for further processing.

Open the exported CSV file in a spreadsheet application like Excel or Google Sheets. Review the data to ensure all necessary fields are included and clean up any unnecessary or malformed data that might affect import. Ensure that the data types match the intended schema of your DynamoDB table.

Install the AWS CLI on your machine if you haven�t already. Open your terminal and configure your AWS CLI with the command `aws configure`. Enter your AWS Access Key, Secret Access Key, region, and output format when prompted. This configuration is crucial for accessing AWS services.

Log in to your AWS Management Console and navigate to DynamoDB. Create a new table that matches the schema of your CSV data. Define the primary key and any required indexes. Make sure the table is set up to handle the projected data volume and read/write capacity.

Use a script in Python or another language of your choice to convert your CSV data to JSON format, which is compatible with DynamoDB. This script should read each row of the CSV and output a JSON object that matches the structure of your DynamoDB table items.

With your JSON data ready, use the AWS CLI or a script to batch write the data to your DynamoDB table. The AWS CLI command for this is `aws dynamodb batch-write-item`. If using a script, utilize AWS SDKs (like Boto3 for Python) to automate the process. Ensure you respect DynamoDB's batch write limits by splitting large data sets into multiple requests.

Once the data has been imported, verify the integrity and consistency of the data in DynamoDB. You can do this by running queries or scans on your table to check that the data matches your expectations. This verification step ensures that the migration was successful and that no data was lost or corrupted during the process.

How to Sync HubSpot to DynamoDB Manually - Method 2:

FAQs

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.

A platform focused on sales and inbound marketing, Hubspot helps businesses optimize their online marketing strategies for greater visibility to attract more visitors, collect leads, and convert prospects into customers. HubSpot provides a variety of essential services and strategies to move businesses forward, including social media and email marketing, website content management, search engine optimization, blogging, and analytics and reporting. Hubspot is an all-around solution for business teams to grow their customer base through effective marketing.

HubSpot's API provides access to a wide range of data categories, including:  

1. Contacts: Information about individual contacts, including their name, email address, phone number, and company.  

2. Companies: Information about companies, including their name, industry, and location.  

3. Deals: Information about deals, including their stage, amount, and close date.  

4. Tickets: Information about customer support tickets, including their status, priority, and owner.  

5. Products: Information about products, including their name, price, and description.  

6. Analytics: Data on website traffic, email performance, and other marketing metrics.  

7. Workflows: Information about automated workflows, including their triggers, actions, and outcomes.  

8. Forms: Information about forms, including their fields, submissions, and conversion rates.  

9. Social media: Data on social media engagement, including likes, shares, and comments.  

10. Integrations: Information about third-party integrations, including their status and configuration.  

Overall, HubSpot's API provides access to a wide range of data categories that can be used to improve marketing, sales, and customer support efforts.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up HubSpot to DynamoDB as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from HubSpot to DynamoDB and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

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.

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:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter