How to load data from Pardot to AWS Datalake

Learn how to use Airbyte to synchronize your Pardot data into AWS Datalake within minutes.

Try Airbyte Cloud

Get a demo

Building your pipeline or Using Airbyte

Airbyte is the only open 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 Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Pardot connector in Airbyte

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

Set up AWS Datalake for your extracted Pardot data

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

Configure the Pardot to AWS Datalake 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 Cloud

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

Old Automated Content

TL;DR

set up Pardot as a source connector (using Auth, or usually an API key)
set up AWS Datalake as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Pardot

Pardot is a marketing automation platform that helps businesses streamline their marketing efforts and generate more leads. It offers a range of tools and features, including email marketing, lead scoring, lead nurturing, and analytics. With Pardot, businesses can create targeted campaigns that reach the right audience at the right time, and track their performance to optimize their marketing strategies. The platform also integrates with Salesforce, allowing businesses to seamlessly manage their sales and marketing efforts in one place. Overall, Pardot is designed to help businesses improve their marketing ROI and drive growth.

What is AWS Datalake

An AWS Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It is designed to handle massive amounts of data from various sources, such as databases, applications, IoT devices, and more. With AWS Data Lake, you can easily ingest, store, catalog, process, and analyze data using a wide range of AWS services like Amazon S3, Amazon Athena, AWS Glue, and Amazon EMR. This allows you to build data lakes for machine learning, big data analytics, and data warehousing workloads. AWS Data Lake provides a secure, scalable, and cost-effective solution for managing your organization's data.

Integrate Pardot with AWS Datalake in minutes

Try for free now

Prerequisites

A Pardot account to transfer your customer data automatically from.
A AWS Datalake account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Pardot and AWS Datalake, for seamless data migration.

When using Airbyte to move data from Pardot to AWS Datalake, it extracts data from Pardot using the source connector, converts it into a format AWS Datalake can ingest using the provided schema, and then loads it into AWS Datalake via the destination connector. This allows businesses to leverage their Pardot data for advanced analytics and insights within AWS Datalake, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Pardot as a source connector

1. First, log in to your Pardot account and navigate to the ""Settings"" tab.
2. Click on ""Connectors"" and then select ""Add Connector.""
3. Choose ""API User Key"" as the connector type and give it a name.
4. Generate an API user key by clicking on ""Generate User Key"" and copy it.
5. In Airbyte, navigate to the ""Sources"" tab and click on ""New Source.""
6. Select ""Pardot"" as the source type and give it a name.
7. Paste the API user key into the ""API User Key"" field.
8. Enter your Pardot username and password in the appropriate fields.
9. Click on ""Test Connection"" to ensure that the connection is successful.
10. If the test is successful, click on ""Create Source"" to save the configuration.
11. You can now use the Pardot source connector in Airbyte to extract data from your Pardot account.

Step 2: Set up AWS Datalake as a destination connector

1. Log in to your AWS account and navigate to the AWS Management Console.
2. Click on the S3 service and create a new bucket where you will store your data.
3. Create an IAM user with the necessary permissions to access the S3 bucket. Make sure to save the access key and secret key.
4. Open Airbyte and navigate to the Destinations tab.
5. Select the AWS Datalake destination connector and click on "Create new connection".
6. Enter a name for your connection and paste the access key and secret key you saved earlier.
7. Enter the name of the S3 bucket you created in step 2 and select the region where it is located.
8. Choose the format in which you want your data to be stored in the S3 bucket (e.g. CSV, JSON, Parquet).
9. Configure any additional settings, such as compression or encryption, if necessary.
10. Test the connection to make sure it is working properly.
11. Save the connection and start syncing your data to the AWS Datalake.

Step 3: Set up a connection to sync your Pardot data to AWS Datalake

Once you've successfully connected Pardot as a data source and AWS Datalake as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Pardot from the dropdown list of your configured sources.
Select your destination: Choose AWS Datalake from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Pardot objects you want to import data from towards AWS Datalake. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Pardot to AWS Datalake according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your AWS Datalake data warehouse is always up-to-date with your Pardot data.

Use Cases to transfer your Pardot data to AWS Datalake

Integrating data from Pardot to AWS Datalake provides several benefits. Here are a few use cases:

Advanced Analytics: AWS Datalake’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Pardot data, extracting insights that wouldn't be possible within Pardot alone.
Data Consolidation: If you're using multiple other sources along with Pardot, syncing to AWS Datalake allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Pardot has limits on historical data. Syncing data to AWS Datalake allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: AWS Datalake provides robust data security features. Syncing Pardot data to AWS Datalake ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: AWS Datalake can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Pardot data.
Data Science and Machine Learning: By having Pardot data in AWS Datalake, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Pardot provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to AWS Datalake, providing more advanced business intelligence options. If you have a Pardot table that needs to be converted to a AWS Datalake table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Pardot account as an Airbyte data source connector.
Configure AWS Datalake as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Pardot to AWS Datalake after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

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

Talk to a data infrastructure expert

Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure.

Talk to sales

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

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 supports both incremental and full refreshes, for databases of any size.

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 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

Jean-Mathieu Saponaro

Data & Analytics Senior Eng Manager

"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"

Learn more

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

Alexis Weill

Data Lead

“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria. The value of being able to scale and execute at a high level by maximizing resources is immense”

Learn more

How to Sync Pardot to AWS Datalake Manually

Step 1:

Step 2:

Step 3:

Step 4:

Step 5:

Step 6:

Step 7:

Step 8:

FAQs

What is ETL?

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.

What is Pardot?

What data can you extract from Pardot?

Pardot's API provides access to a wide range of data related to marketing automation and lead management. The following are the categories of data that can be accessed through Pardot's API:

1. Prospects: Information about individual leads, including their contact details, activity history, and lead score.

2. Campaigns: Details about marketing campaigns, including their status, performance metrics, and associated assets.

3. Lists: Information about lists of prospects, including their size, membership criteria, and segmentation rules.

4. Emails: Details about email campaigns, including their content, delivery status, and engagement metrics.

5. Forms: Information about web forms used to capture lead data, including their design, submission data, and conversion rates.

6. Landing Pages: Details about landing pages used to drive lead generation, including their design, traffic sources, and conversion rates.

7. Tags: Information about tags used to categorize prospects, campaigns, and other marketing assets.

8. Users: Details about Pardot users, including their roles, permissions, and activity history.

9. Custom Objects: Information about custom objects created in Pardot, including their fields, records, and relationships with other objects.

Overall, Pardot's API provides a comprehensive set of data that can be used to optimize marketing campaigns, improve lead management, and drive business growth.

How do I transfer data from Pardot?

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 Pardot to AWS Datalake 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 Pardot to AWS Datalake and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

What is ELT?

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.

Difference between ETL and ELT?

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.

Back to all tutorials

Warehouses and Lakes

Marketing Analytics

How to load data from Pardot to AWS Datalake

Learn how to use Airbyte to synchronize your Pardot data into AWS Datalake within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

Step 2: Setup AWS Datalake

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Pardot and AWS Datalake

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Pardot as a source connector (using Auth, or usually an API key)
set up AWS Datalake as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Pardot

What is AWS Datalake

Integrate Pardot with AWS Datalake in minutes

Try for free now

Prerequisites

A Pardot account to transfer your customer data automatically from.
A AWS Datalake account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Step 1: Set up Pardot as a source connector

Step 2: Set up AWS Datalake as a destination connector

Step 3: Set up a connection to sync your Pardot data to AWS Datalake

Once you've successfully connected Pardot as a data source and AWS Datalake as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Pardot from the dropdown list of your configured sources.
Select your destination: Choose AWS Datalake from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Pardot objects you want to import data from towards AWS Datalake. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Pardot to AWS Datalake according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your AWS Datalake data warehouse is always up-to-date with your Pardot data.

Use Cases to transfer your Pardot data to AWS Datalake

Integrating data from Pardot to AWS Datalake provides several benefits. Here are a few use cases:

Advanced Analytics: AWS Datalake’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Pardot data, extracting insights that wouldn't be possible within Pardot alone.
Data Consolidation: If you're using multiple other sources along with Pardot, syncing to AWS Datalake allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Pardot has limits on historical data. Syncing data to AWS Datalake allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: AWS Datalake provides robust data security features. Syncing Pardot data to AWS Datalake ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: AWS Datalake can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Pardot data.
Data Science and Machine Learning: By having Pardot data in AWS Datalake, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Pardot provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to AWS Datalake, providing more advanced business intelligence options. If you have a Pardot table that needs to be converted to a AWS Datalake table, Airbyte can do that automatically.