Best ETL Tools for PostgreSQL Integration to follow in 2024

TL;DR

The most prominent ETL and ELT tools to transfer data from Postgres include:

These ETL and ELT tools help in extracting data from Postgres and other sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities. Airbyte distinguishes itself by offering both a self-hosted open-source platform and a Cloud one..

What is ETL?

ETL (Extract, Transform, Load) is a process used to extract data from one or more data sources, transform the data to fit a desired format or structure, and then load the transformed data into a target database or data warehouse. ETL is typically used for batch processing and is most commonly associated with traditional data warehouses.

‍What are PostgreSQL ETL Tools?

PostgreSQL ETL tools are specialized software that helps data engineers move and transform data from PostgreSQL databases to other destinations. For example, you might use Airbyte to extract customer data from PostgreSQL, transform date formats, and then load it into Snowflake for analytics.

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Common Use Cases for PostgreSQL ETL Tools

Data Warehouse Loading

Data teams regularly extract product and customer data from multiple PostgreSQL operational databases into their data warehouse. The ETL tool standardizes IDs, converts timestamps to UTC, and ensures consistent formatting before loading cleaned data into platforms like Snowflake or BigQuery for business intelligence reporting.

Real-time Analytics Pipeline

E-commerce companies stream their PostgreSQL sales and inventory data through ETL tools that aggregate metrics in real-time for live monitoring. The transformed data feeds directly into analytics dashboards, helping teams track KPIs and make quick operational decisions based on current performance.

Data Migration Projects

When companies upgrade their technology stack, ETL tools help migrate data from legacy PostgreSQL databases to new platforms. The tool manages the entire process - from schema conversion and data validation to integrity checks - ensuring all historical data moves accurately to the new system while maintaining data quality.

Criterias to select the right Postgres ETL solution for you

As a company, you don't want to use one separate data integration tool for every data source you want to pull data from. So you need to have a clear integration strategy and some well-defined evaluation criteria to choose your Postgres ETL solution.

Here is our recommendation for the criteria to consider:

• Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?
• Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?
• Ability to build new connectors: all data integration solutions support a limited number of data sources.
• Support of change data capture: this is especially important for your databases.
• Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature
• Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?
• Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ?
• Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an square integration with dbt
• Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.
• Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.
• Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.

Top Postgres ETL tools

Here are the top Postgres ETL tools based on their popularity and the criteria listed above:

1. Airbyte

Airbyte is an open-source ETL tool that can sync data from PostgreSQL to various destinations using pre-built connectors. It handles both full and incremental loads, making it suitable for datasets of any size while offering custom transformation capabilities through dbt.

Deployment options: Both self-hosted and cloud-hosted

Platform type: Open-source with a cloud offering

Key features:

• CDC support for real-time PostgreSQL syncs
• Built-in data normalization and transformations
• Custom sync schedules with checkpoint-based recovery

2. Fivetran

Fivetran is a cloud-native, fully-managed PostgreSQL ETL tool that automatically handles schema changes and updates when moving data from PostgreSQL.

Deployment options: Cloud-hosted only

Platform type: Proprietary SaaS

Key features:

• PostgreSQL connectors with automatic schema drift handling
• Historical sync with compressed archive tables
• Modern security with SOC 2 Type II compliance

Here are more critical insights on the key differentiations between Airbyte and Fivetran

3. Stitch Data

Stitch is one of the cloud-based PostgreSQL ETL tools owned by Qlik that specializes in replicating PostgreSQL databases to data warehouses. It offers a straightforward interface for managing data pipelines while handling complex replication tasks like table selection and column hashing under the hood.

Deployment options: Cloud-hosted only

Platform type: Proprietary SaaS

Key features:

• Automated schema change detection
• Advanced scheduling
• Configurable replication methods

Here are more insights on the differentiations between Airbyte and Stitch, and between Fivetran and Stitch.

4. Airflow

Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.

What's unique about Airflow?

Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.

Here are more insights on the differentiations between Airbyte and Airflow.

5. Talend

Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.

What’s unique with Talend?

What sets Talend apart is its open-source architecture with Talend Open Studio, which allows for easy customization and integration with other systems and platforms. However, Talend is not an easy solution to implement and requires a lot of hand-holding, as it is an Enterprise product. Talend doesn't offer any self-serve option.

6. Pentaho

Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.

What is unique about Pentaho?

What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions.

However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.

7. Informatica PowerCenter

Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.

8. Singer

Singer is also worth mentioning as the first open-source JSON-based ETL framework.  It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.

9. HevoData

HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs.

10. Meltano

Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.

All those ETL tools are not specific to Postgres, you might also find some other specific data loader for Postgres data. But you will most likely not want to be loading data from only Postgres in your data stores.

Which data can you extract from Postgres?

PostgreSQL gives access to a wide range of data types, including:  

1. Numeric data types: This includes integers, floating-point numbers, and decimal numbers.  

2. Character data types: This includes strings, text, and character arrays.  

3. Date and time data types: This includes dates, times, and timestamps.  

4. Boolean data types: This includes true/false values.  

5. Network address data types: This includes IP addresses and MAC addresses.  

6. Geometric data types: This includes points, lines, and polygons.  

7. Array data types: This includes arrays of any of the above data types.  

8. JSON and JSONB data types: This includes JSON objects and arrays.  

9. XML data types: This includes XML documents.  

10. Composite data types: This includes user-defined data types that can contain multiple fields of different data types.  

Overall, PostgreSQL's API provides access to a wide range of data types, making it a versatile and powerful tool for data management and analysis.

How to start pulling data in minutes from Postgres

If you decide to test Airbyte, you can start analyzing your Postgres data within minutes in three easy steps:

Step 1: Set up Postgres as a source connector

1. Open your PostgreSQL database and create a new user with the necessary permissions to access the data you want to replicate.

2. Obtain the hostname or IP address of your PostgreSQL server and the port number it is listening on.

3. Create a new database in PostgreSQL that will be used to store the replicated data.

4. Obtain the name of the database you just created.

5. In Airbyte, navigate to the PostgreSQL source connector and click on "Create Connection".

6. Enter a name for your connection and fill in the required fields, including the hostname or IP address, port number, database name, username, and password.

7. Test the connection to ensure that Airbyte can successfully connect to your PostgreSQL database.

8. Select the tables or views you want to replicate and configure any necessary settings, such as the replication frequency and the replication method.

9. Save your configuration and start the replication process.

10. Monitor the replication process to ensure that it is running smoothly and troubleshoot any issues that arise.

Step 2: Set up a destination for your extracted Postgres data

Choose from one of 50+ destinations where you want to import data from your Postgres source. This can be a cloud data warehouse, data lake, database, cloud storage, or any other supported Airbyte destination.

Step 3: Configure the Postgres data pipeline in Airbyte

Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.

And that's it! It is the same process between Airbyte Open Source that you can deploy within 5 minutes, or Airbyte Cloud which you can try here, free for 14-days.

Conclusion

This article outlined the criteria that you should consider when choosing a data integration solution for Postgres ETL/ELT. Based on your requirements, you can select from any of the top 10 ETL/ELT tools listed above. We hope this article helped you understand why you should consider doing Postgres ETL and how to best do it.

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