As a data engineer, you know that making data accessible to all business teams is essential for driving business growth. Your team has requested a priority connection between HubSpot and Amazon Aurora so that your sales, marketing, and customer service teams can gain critical business insights.

Don’t worry, we’ve got you covered. This blog post will discuss a simple, step-by-step guide for migrating data from HubSpot to Amazon Aurora. Let’s get started. 

How to Connect Hubspot to Amazon Aurora?

Method 1: Using Hubspot APIs

You can migrate data from HubSpot to Amazon Aurora using Hubspot APIs. Follow the following stepwise process for a successful migration:

Step 1: Create a HubSpot developer account.

  • Go to the HubSpot Developer website: and click on the Create Account button.
  • Provide your Name, email address, and password, and create an Account.

Step 2: Choose the authentication method and create an access token.

  • The HubSpot API requires authentication before you can access it. You can choose to use OAuth 2.0 or a private app access token.
  • OAuth 2.0 is more secure, but it is also more complex to set up. A private app access token is less secure, but it is easier to set up.

   To create an access token:

  • Go to the Settings tab on the HubSpot Developer website.
  • Click on the API tab.
  • If you use a private app access token, click Create a private app access token.
  • If you choose to use OAuth 2.0, follow the instructions on the HubSpot Developer website to generate an access token.

Once you have created an access token, make sure you save it at a secure location. 

Hubspot API Authentication 
Hubspot API Authentication 

Step 3: Install the HubSpot API Client Library

  • Find the client library for your programming language on the HubSpot Developer website. HubSpot Developer website has client libraries for a variety of programming languages, including Python, Java, and JavaScript.
  • Install the client library according to the instructions on the HubSpot Developer website.
   Hubspot Supported Client Libraries 
   Hubspot Supported Client Libraries 

Step 4: Create a Staging Table in Your Amazon Aurora Database

The staging table is a temporary table that you can use to store your migrated data before it is loaded into your production database. The staging table should have the same schema as the production database table that you are migrating data to.

To create a staging table:

  • Use the AWS Management Console or the AWS CLI to create a connection to your Amazon Aurora database.
  • Run the SQL command to create the staging table.

Here’s an example 

CREATE TABLE staging_table (
  name VARCHAR(255),
  email VARCHAR(255),
  phone VARCHAR(255),
  deal_name VARCHAR(255),
  deal_stage VARCHAR(255),
  deal_value VARCHAR(255)

Step 5: Write a Script to Retrieve the Data From HubSpot and Load It into the Staging Table

Here is an example of a script that you can use to migrate contacts and deals from HubSpot to Amazon Aurora:

import hubspot
import psycopg2

def migrate_data():
    Migrates data from HubSpot to Amazon Aurora.
    # Create a connection to the HubSpot API.
    client = hubspot.Client(access_token="my_access_token")

    # Create a connection to the Amazon Aurora database.
    conn = psycopg2.connect("host=localhost dbname=mydb user=myuser password=mypassword")
    # Retrieve the data from HubSpot.
    contacts = client. contacts.get_all()
    deals =

    # Load the data into the Amazon Aurora database.
    for contact in contacts:
        cursor = conn.cursor()
        cursor.execute("INSERT INTO staging_table (name, email, phone) VALUES (%s, %s, %s)", (contact["name"], contact["email"], contact["phone"]))
    for a deal in deals:
        cursor = conn.cursor()
        cursor.execute("INSERT INTO staging_table (deal_name, deal_stage, deal_value) VALUES (%s, %s, %s)", (deal["name"], deal["stage"], deal["value"]))

    # Commit the changes to the database.

    # Load the data from the staging table into your production database.
    cursor = conn.cursor()
    cursor.execute("INSERT INTO production_table (name, email, phone) SELECT name, email, phone FROM staging_table;")
    cursor.execute("INSERT INTO production_table (deal_name, deal_stage, deal_value) SELECT deal

This method is useful in the following scenarios:

  • Small data: If your data requirement from HubSpot is small, you can use this method to migrate data from HubSpot to Amazon Aurora one record at a time.
  • More control over the migration process: This method will allow you more control in customizing the migration process to meet your specific needs.

However, when you need to migrate a large amount of data on a regular basis, this method is not scalable as it’s time-consuming, error-prone, and requires specialized skills.

If you’re aiming to integrate data fast and accurately you can….

Method 2: Integrate Data from Hubspot to Amazon Aurora Using an Automated ETL Tool

Migrating data from HubSpot to Amazon Aurora can be a daunting task, especially if you are dealing with a large volume of data. Custom scripts can be difficult to scale to handle large volumes of data, which can lead to performance bottlenecks and delays in the migration process. Additionally, custom scripts can be costly to develop and maintain, and they can also put constraints on your bandwidth.

An automated tool can streamline the data replication process and improve efficiency and accuracy. Here are some of the benefits of using ETL tools:

  • Reduced time to market: Automated tools can help you get your data into production faster. This is because they can handle the complex tasks of data extraction, transformation, and loading (ETL) automatically.
  • Improved data quality: Automated tools can help you improve the quality of your data by detecting and correcting errors. This can help you avoid costly mistakes and improve the accuracy of your reports and analysis.
  • Increased scalability: Automated tools can scale easily as your data volumes grow. This means you can easily add new data sources and users without having to worry about the performance of your system.
  • Reduced costs: Automated tools can help you drastically reduce the cost of data replication by automating manual tasks and eliminating the need to hire a dedicated big data engineering team. 

If you want to improve the efficiency, accuracy, and scalability of your data replication process, then an automated tool is a good option.

For instance, here’s how Hevo Data, a cloud-based ETL tool, makes Hubspot to Amazon Aurora Data replication possible in just two steps.

Step 1: Configure Hubspot as a Source 

Hubspot to Amazon Aurora: Configure Source
 Hubspot to Amazon Aurora: Configure Source

Step 2: Configure Amazon Aurora as a Destination 

Hubspot to Amazon Aurora: Configure Destination
Hubspot to Amazon Aurora: Configure Destination

That’s it. Your ETL pipeline is now set up !!

Hevo Data is a data pipeline platform that offers 150+ plug-and-play connectors, including 40+ free sources like Harvest. It can efficiently replicate data from Hubspot to Amazon Aurora, other databases, data warehouses, or any other destination of your choice in a completely hassle-free and automated manner.

Hevo’s fault-tolerant architecture ensures that the data is handled securely and consistently with zero loss. It also enriches the data and transforms it into an analysis-ready form without writing a single line of code. With Hevo Data, you can create data pipelines that just work, without having to write any code or worry about maintenance. 

By using Hevo to simplify your data integration needs, you can leverage its salient features:

  • Reliability at scale: Hevo’s world-class fault-tolerant architecture scales with zero data loss and low latency.
  • Monitoring and observability: Hevo provides intuitive dashboards that reveal every stat of the pipeline and data flow. These dashboards allow you to monitor pipeline health and bring real-time visibility into your ELT with alerts and activity logs.
  • Stay in total control: When automation isn’t enough, Hevo offers flexibility in terms of data ingestion modes, ingestion and load frequency, JSON parsing, destination workbench, custom schema management, and more, so that you can have total control.
  • Auto-schema management: Hevo automatically maps the source schema with the destination of your choice, so you don’t have to worry about schema errors.
  • 24×7 customer support: Hevo offers 24×7 support through live chat, so you can always get help when you need it.
  • Transparent pricing: Hevo’s transparent pricing model gives you complete visibility into your ELT spending. Choose a plan based on your business needs and stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow.

What Can You Achieve by Migrating Your Data From Hubspot to Amazon Aurora?

By replicating data from HubSpot to Amazon Aurora, you can gain insights into your business that you may not be able to see with just HubSpot’s native reporting tools. Here are five questions that you can answer by replicating data from HubSpot to Amazon Aurora: 

  1. What are the top sources of leads for my business?
  2. What are the most popular pages on my website?
  3. What are my most effective marketing campaigns?
  4. What are my most profitable customers?
  5. What are the trends in my industry?.

Final Thoughts: 

There are two ways to migrate data from HubSpot to Amazon Aurora: using HubSpot APIs or a third-party ETL tool.

Using HubSpot APIs involves creating a HubSpot developer account, getting an access token, installing the HubSpot API Client Library, creating a staging table, writing a script, and loading the data. This method can be a great option if your data requirements are small and infrequent.

But what if you need to migrate large amounts of data on a regular basis? Will you be still dependent on the manual and laborious tasks of exporting data from different sources?

In that case, you can free yourself from the hassle of writing code and manual tasks by opting for an automated ETL Tool like Hevo Data.

A custom ETL solution becomes necessary for real-time data demands such as monitoring email campaign performance or viewing the sales funnel. You can free your engineering bandwidth from these repetitive & resource-intensive tasks by selecting Hevo Data’s 150+ plug-and-play integrations (including 40+ accessible sources).

Saving countless hours of manual data cleaning & standardizing, Hevo Data’s preload data transformations get it done in minutes via a simple drag-and-drop interface or your custom Python scripts—no need to go to your data warehouse for post-load transformations. You can run complex SQL transformations from the comfort of Hevo Data’s interface and get your data in the final analysis-ready form. 

Want to take Hevo for a spin? SIGN UP for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.

Former Marketing Associate, Hevo Data

Bhushan is a data science enthusiast who loves writing content to help data practitioners solve challenges associated with data integration. He has a flair for writing in-depth articles on data science.

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