Marketo is a top-rated cloud-based marketing automation tool that assists businesses in automating marketing engagement tasks and workflows. It handles all the marketing campaigns and helps generate quality leads for businesses. With Marketo reports, companies can track their marketing campaigns using visualizations and reports.

Businesses can store all their Marketo campaign data in a database like PostgreSQL, which can be used for in-depth analysis. Companies can combine PostgreSQL and the Marketo data with powerful BI tools such as Google Data Studio, Tableau, Power BI, and more to gain meaningful insights and make better business decisions. They can use third-party ETL (Extract, Load, and Transform) tools or standard APIs to connect Marketo data with the PostgreSQL database.

This article will guide you in connecting Marketo to PostgreSQL using different processes.


Basic need for integration

What is Marketo?

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Developed in 2006, Adobe Marketo is a very popular Software-as-a-Service(SaaS) based marketing automation software. Adobe has developed Marketo to help businesses automate and measure marketing engagement tasks and workflows. Marketo monitors automation tasks like email marketing, lead management, revenue attribution, account-based marketing, customer-based marketing, and more.

Marketo provides businesses with an engaging platform that can help marketers to build brand value and generate more revenue. It is one of the most trustworthy platforms for more than 51464 companies due to its scalability, reliability, and openness. Several companies, including Accenture, Tesla Motors, Zendesk, Docker, Honeywell, and more, use Marketo.

Key Features of Marketo

  • Engagement Engine: With the engagement engine, Marketo allows businesses to conduct research and gain meaningful insights into customer data. Marketo also enables businesses to design industry-specific email campaigns for customers.
  • Account-Based Marketing: This feature in Marketo allows businesses to develop an intelligent list of characters that characterize the personas they want to target. It helps companies improve their lead experience on the website and engage them in several channels like emails, phones, and more.
  • Lead Nurturing: Marketo helps businesses segment their leads based on marketing personas, target industries, or interactions taking place. This process helps build customer connections and partnerships that increase conversion rates.

What is PostgreSQL?

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Developed in July 1996, PostgreSQL is an open-sourced relational management system that supports SQL and JSON queries. Due to its scalability and flexibility, PostgreSQL is one of the best choices for data warehouses, web applications, mobile applications, analytics solutions, and geospatial applications. PostgreSQL consists of many features that aim to assist developers in building superior applications, maintaining data integrity, and building fault-tolerant applications.

Besides being an open source, PostgreSQL is highly extensible that allows you to define your data types, create your custom functions, and even interact with using different programming languages. PostgreSQL is a robust database that has served over 30 years of active development along with a solid reputation in reliability, robustness, and performance. It has the potential to handle all the SQL queries virtually.

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Key Features of PostgreSQL

  • Compatibility: PostgreSQL databases can run on several operating systems, such as Microsoft Windows, Linux, macOS, and Unix (ASX, HP-UX, SGI IRIX, and Solaris). It is also compatible with several programming languages like C, C++, Java, Python, Perl, Ruby, and more.
  • Highly Secured: PostgreSQL database offers a robust access control system. It consists of several authentications, such as the Lightweight Directory Access Protocol (LDAP), Generic Security Service Application Program Interface (GSSAPI), SCRAM-SHA-256, and Security Support Provider Interface (SSPI), Certificate, and more. PostgreSQL also supports column and row-level security.
  • Highly Reliable: PostgreSQL is highly reliable and provides disaster recovery like Active standbys, Tablespaces, Point in time recovery (PITR), and more. It supports different replication such as Asynchronous, Synchronous, and Logical. PostgreSQL supports Internalization consisting of the international character set, including ICU collations, case-sensitive collations, accent-insensitive collations, and full-text searches.

Why Integrate Marketo to PostgreSQL?

Today, businesses need a marketing tool like Marketo to focus on their marketing campaign tasks to improve lead and customer engagement. Companies can store all the marketing campaign data in a database like PostgreSQL, where it can be used for in-depth analysis. Integrating Marketo to PostgreSQL will help get a better understanding of data and analysis is more efficient and easier.

Marketing data can be analyzed and will be able to give valuable insights with PostgreSQL. You can load your Marketo campaign data into a database like PostgreSQL, where it can be stored in the rows and columns format and used for gaining meaningful insights with the powerful BI tools.

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Connecting Marketo to PostgreSQL

Method 1: Using Hevo to Set Up Marketo to PostgreSQL

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Hevo provides an Automated No-code Data Pipeline that helps you move your Marketo to PostgreSQL. Hevo is fully-managed and completely automates the process of not only loading data from your 150+ data sources(including 40+ free sources)but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled securely and consistently with zero data loss.

Using Hevo, you can connect Marketo to PostgreSQL in the following 2 steps:

  • Step 1: Configure Marketo as the Source in your Pipeline by following these steps:
    • Step 1.1: For your Marketo instance, get authenticated access credentials.
    • Step 1.2: In the Asset Palette, select PIPELINES.
    • Step 1.3: In the Pipelines List View, click + CREATE.
    • Step 1.4: Select Marketo on the Select Source Type page.
    • Step 1.5: Enter the following information on the Configure your Marketo Source page:
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  • Pipeline Name: A distinct name for the Pipeline that isn’t longer than 255 characters.
  • Client ID: Accessible at the recently launched service.
  • Client Secret: Accessible at the recently launched service.
  • Endpoint: The primary URL that is used for all API requests.
  • Identity Endpoint: The endpoint where access tokens can be retrieved using the client secret and client ID.
  • Step 1.6: Simply press TEST & CONTINUE.
  • Step 1.7: Configure the data ingestion and establish the destination after that.
  • Step 2: After configuring the source, let’s configure PostgreSQL as the destination by following the below steps.
    • Step 2.1: After configuring the Source during Pipeline creation, click ADD DESTINATION or Click DESTINATIONS in the Asset Palette, and then, click +CREATE in the Destinations List View.
    • Step 2.2: Select PostgreSQL on the Add Destination page.
    • Step 2.3: Configure PostgreSQL connection settings as mentioned below:
      • Destination Name: Give a unique name for your Destination.
      • Database Host: Use the PostgreSQL host’s IP address or DNS as the database host.
      • Database Port: The port number on which your PostgreSQL server listens for connections. Default value: 5432.
      • Database User: In the PostgreSQL database, a user with a non-administrative role.
      • Database Password: The password of the PostgreSQL database user.
      • Database Name: The name of the Destination PostgreSQL database to which the data is loaded.
      • Database Schema: The name of the Destination PostgreSQL database schema. Default value: public.
      • Additional Settings: Choose advance setting as per the need.
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  • Step 2.4: After configuring the PostgreSQL settings, click on TEST CONNECTION to test connectivity to the Destination Postgres server.
  • Step 2.5: Once the test connection is successful, save the connection by clicking on SAVE & CONTINUE.
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Method 2: Using Custom Code to Move Data from Marketo to PostgreSQL

Businesses can connect Marketo to PostgreSQL by manually exporting and importing data.

Exporting Marketo Data

As an admin, businesses can export their Marketo data to analyze their marketing campaigns and related tasks.

Follow the below steps to export Marketo data. 

  • It is assumed that you have signed in to Marketo. Click on Admin.
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  • Click on Field Management.
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  • Search the desired field and then select it.
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  • Click on the Field Actions drop-down list and select the ‘Export Used By’ option.
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As a result, an excel file will be exported. You can also export the list of all Marketo API Field names as a Marketo Admin. Follow the below steps to export the list of all Marketo API Field names.

  • Navigate to the Admin and click on Field Management.
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  • Click on Export Field Names to download the Excel file.
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  • You will have the below excel file that contains the list of all your field with their API names.
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Importing Data into PostgreSQL

Follow the below steps to load a CSV file into the PostgreSQL database. 

  • Create a new table named persons with the below columns.
    • id: the person id
    • first_name: first name
    • last_name: last name
    • dob: date of birth
    • email: the email address
  • Use the below commands to create the persons table.
CREATE TABLE persons (
  id SERIAL,
  first_name VARCHAR(50),
  last_name VARCHAR(50),
  dob DATE,
  email VARCHAR(255),


marketo to postgresql: postgresql persons table
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  • Prepare the CSV file using the below format.
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  • Set the CSV file path as C:\sampledb\persons.csv. You can download the persons.csv file.
  • Import the persons.csv file into a PostgreSQL table using the COPY statement.
  • Use the below COPY command to import the CSV file into a PostgreSQL table.
COPY persons(first_name, last_name, dob, email)
FROM 'C:\sampledb\persons.csv'


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Therefore, the above COPY command has successfully copied two rows in the persons table. Check the persons table.

SELECT * FROM persons;


marketo to postgresql: postgresql output of copy command
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You can also import the CSV file into the PostgreSQL database using the pgAdmin tool. Follow the below steps to import the CSV file in the PostgreSQL database using the pgAdmin tool.

  • Right-click on the persons table and select the Import/Export option.
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  • Navigate to import and browse the import file. 
  • Select the format as CSV and the delimiter as a comma.
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  • Click on the columns tab, uncheck the column id and then click on the OK button.
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  • Wait for the import process to complete. The below dialog box shows the progress of the import.
marketo to postgresql: import process complete acknowledgment
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Limitations of Manually Connecting Marketo to PostgreSQL

Using standard APIs and manual processes, you can connect Marketo data with the PostgreSQL database. With the manual processes, you can easily export Marketo data and import it into PostgreSQL. But this process cannot work with real-time data. And in the case of the standard APIs, you need a strong technical team to work with standard APIs. Therefore, to eliminate such issues, you can use the third-party ETL tools to integrate Marketo to PostgreSQL seamlessly.


In this article, you learned about connecting Marketo with PostgreSQL. Marketo is mainly used by medium-sized companies that help them simplify lead management, email marketing, consumer marketing, mobile marketing, and more.

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Hevo offers a No-code Data Pipeline that can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Marketing, Customer Management, etc.

This platform allows you to transfer data from 150+ sources (including 40+ Free Sources) such as Marketo to a destination of your choice such as PostgreSQL. It will provide you with a hassle-free experience and make your work life much easier.

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Manjiri Gaikwad
Technical Content Writer, Hevo Data

Manjiri is a proficient technical writer and a data science enthusiast. She holds an M.Tech degree and leverages the knowledge acquired through that to write insightful content on AI, ML, and data engineering concepts. She enjoys breaking down the complex topics of data integration and other challenges in data engineering to help data professionals solve their everyday problems.

No-code Data Pipeline For PostgreSQL