How to load data from Greenhouse to Postgres destination

Learn how to use Airbyte to synchronize your Greenhouse data into Postgres destination within minutes.

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Set up a Greenhouse connector in Airbyte

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

Set up Postgres destination for your extracted Greenhouse data

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

Configure the Greenhouse to Postgres destination 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.

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TL;DR

set up Greenhouse as a source connector (using Auth, or usually an API key)
set up Postgres destination 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 Greenhouse

Greenhouse is a software company that specializes in helping businesses acquire talent. It offers a variety of software tools and services to help businesses throughout all aspects of the hiring process, from applicant tracking systems to recruiting software. With the goal of helping businesses find and hire the ideal candidate, Greenhouse helps employers improve the efficiency and effectiveness of the recruitment and hiring process.

What is Postgres destination

An object-relational database management system, PostgreSQL is able to handle a wide range of workloads, supports multiple standards, and is cross-platform, running on numerous operating systems including Microsoft Windows, Solaris, Linux, and FreeBSD. It is highly extensible, and supports more than 12 procedural languages, Spatial data support, Gin and GIST Indexes, and more. Many web, mobile, and analytics applications use PostgreSQL as the primary data warehouse or data store.

Integrate Greenhouse with Postgres destination in minutes

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Prerequisites

A Greenhouse account to transfer your customer data automatically from.
A Postgres destination 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 Greenhouse and Postgres destination, for seamless data migration.

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

Step 1: Set up Greenhouse as a source connector

1. First, navigate to the Greenhouse source connector page on Airbyte.com.

2. Click on the "Create a new connection" button.

3. Enter a name for your connection and click "Next".

4. Enter your Greenhouse API key in the "API Key" field.

5. Click "Test Connection" to ensure that your credentials are correct and the connection is successful.

6. Once the connection is successful, click "Next".

7. Select the tables you want to replicate from Greenhouse.

8. Choose the replication frequency and the destination where you want to send the data.

9. Click "Create Connection" to save your settings and start replicating data from Greenhouse.

It is important to note that the Greenhouse source connector on Airbyte.com allows you to replicate data from Greenhouse into your data warehouse or other destination. This can be useful for analyzing your recruiting data, tracking candidate progress, and improving your hiring process. By following the steps above, you can easily set up a connection between Greenhouse and your destination of choice, and start replicating data in just a few minutes.

Step 2: Set up Postgres destination as a destination connector

Step 3: Set up a connection to sync your Greenhouse data to Postgres destination

Once you've successfully connected Greenhouse as a data source and Postgres destination 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 Greenhouse from the dropdown list of your configured sources.
Select your destination: Choose Postgres destination 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 Greenhouse objects you want to import data from towards Postgres destination. 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 Greenhouse to Postgres destination according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Postgres destination data warehouse is always up-to-date with your Greenhouse data.

Use Cases to transfer your Greenhouse data to Postgres destination

Integrating data from Greenhouse to Postgres destination provides several benefits. Here are a few use cases:

Advanced Analytics: Postgres destination’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Greenhouse data, extracting insights that wouldn't be possible within Greenhouse alone.
Data Consolidation: If you're using multiple other sources along with Greenhouse, syncing to Postgres destination 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: Greenhouse has limits on historical data. Syncing data to Postgres destination allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Postgres destination provides robust data security features. Syncing Greenhouse data to Postgres destination ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Postgres destination can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Greenhouse data.
Data Science and Machine Learning: By having Greenhouse data in Postgres destination, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Greenhouse provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Postgres destination, providing more advanced business intelligence options. If you have a Greenhouse table that needs to be converted to a Postgres destination table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Greenhouse account as an Airbyte data source connector.
Configure Postgres destination as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Greenhouse to Postgres destination 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!

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How to Sync Greenhouse to Postgres destination Manually

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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 Greenhouse?

What data can you extract from Greenhouse?

Greenhouse's API provides access to a wide range of data related to the recruitment process. The following are the categories of data that can be accessed through the API:

1. Candidates: Information about candidates who have applied for a job, including their name, contact details, resume, and application status.

2. Jobs: Details about the job openings, including the job title, location, department, and job description.

3. Applications: Information about the applications submitted by candidates, including the date of submission, the source of the application, and the status of the application.

4. Interviews: Details about the interviews scheduled with candidates, including the date, time, location, and interviewer.

5. Offers: Information about the job offers made to candidates, including the salary, benefits, and start date.

6. Users: Details about the users who have access to the Greenhouse account, including their name, email address, and role.

7. Departments: Information about the departments within the organization, including the name, description, and manager.

8. Sources: Details about the sources of the candidates, including job boards, referrals, and social media.

Overall, Greenhouse's API provides a comprehensive set of data that can be used to streamline the recruitment process and make data-driven decisions.

How do I transfer data from Greenhouse?

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 Greenhouse to PostgreSQL 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 Greenhouse to PostgreSQL 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.

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Databases

Finance & Ops Analytics

How to load data from Greenhouse to Postgres destination

Learn how to use Airbyte to synchronize your Greenhouse data into Postgres destination within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

TL;DR

What is Greenhouse

What is Postgres destination

Prerequisites

Step 1: Setup Greenhouse

Step 2: Setup Postgres destination

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Greenhouse and Postgres destination

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Greenhouse as a source connector (using Auth, or usually an API key)
set up Postgres destination 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 Greenhouse

What is Postgres destination

Integrate Greenhouse with Postgres destination in minutes

Try for free now

Prerequisites

A Greenhouse account to transfer your customer data automatically from.
A Postgres destination 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 Greenhouse as a source connector

1. First, navigate to the Greenhouse source connector page on Airbyte.com.

2. Click on the "Create a new connection" button.

3. Enter a name for your connection and click "Next".

4. Enter your Greenhouse API key in the "API Key" field.

5. Click "Test Connection" to ensure that your credentials are correct and the connection is successful.

6. Once the connection is successful, click "Next".

7. Select the tables you want to replicate from Greenhouse.

8. Choose the replication frequency and the destination where you want to send the data.

9. Click "Create Connection" to save your settings and start replicating data from Greenhouse.

Step 2: Set up Postgres destination as a destination connector

Step 3: Set up a connection to sync your Greenhouse data to Postgres destination

Once you've successfully connected Greenhouse as a data source and Postgres destination 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 Greenhouse from the dropdown list of your configured sources.
Select your destination: Choose Postgres destination 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 Greenhouse objects you want to import data from towards Postgres destination. 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 Greenhouse to Postgres destination according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Postgres destination data warehouse is always up-to-date with your Greenhouse data.

Use Cases to transfer your Greenhouse data to Postgres destination

Integrating data from Greenhouse to Postgres destination provides several benefits. Here are a few use cases:

Advanced Analytics: Postgres destination’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Greenhouse data, extracting insights that wouldn't be possible within Greenhouse alone.
Data Consolidation: If you're using multiple other sources along with Greenhouse, syncing to Postgres destination 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: Greenhouse has limits on historical data. Syncing data to Postgres destination allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Postgres destination provides robust data security features. Syncing Greenhouse data to Postgres destination ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Postgres destination can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Greenhouse data.
Data Science and Machine Learning: By having Greenhouse data in Postgres destination, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Greenhouse provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Postgres destination, providing more advanced business intelligence options. If you have a Greenhouse table that needs to be converted to a Postgres destination table, Airbyte can do that automatically.