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Best ETL Tools for Looker Integration to follow in 2026

Improve data analysis with Looker integration using top ETL tools. Enhance business intelligence seamlessly.

Team Airbyte

March 31, 2026

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✨ AI Generated Summary

Key Takeaway

Extracting data from Looker requires specialized ETL tools beyond its basic API exports to handle complex pipelines and transformations.

Main Points

Airbyte Leads ETL Solutions: Open-source platform with 350+ connectors, easy UI, and supports dbt integration for data transformation workflows.
Looker Export Limitations: While Looker excels at visualization, native export capabilities are limited for complex data pipeline requirements.
Alternative ETL Tools Available: Fivetran, StitchData, Matillion, and Talend offer robust solutions for Looker data extraction and loading.
Google Cloud BI Integration: Looker provides real-time analytics through LookML modeling layer, querying data warehouses directly for business intelligence.
Automated Pipeline Benefits: Third-party ETL tools enable scheduling, complex transformations, and automated Looker data exports across systems.

Looker offers powerful data visualization and analytics capabilities, but extracting data from Looker to other destinations requires specialized ETL tools. While Looker provides basic data export options through its API, data engineers often need robust third-party ETL solutions to handle complex data pipelines, scheduling, and transformations. This guide explores the top ETL tools that integrate with Looker, helping you choose the right solution for automating your Looker data exports.

What is Looker?

Looker, a Google Cloud BI platform, enables organizations to analyze and visualize data directly from their data warehouses through its unique LookML modeling layer and real-time query capabilities.

While Looker excels at data analysis and dashboarding, getting data out of Looker and into other systems can be challenging due to the limitations of its native export capabilities. This is where third-party Looker ETL tools become crucial - they provide automated, reliable, and scalable solutions for extracting Looker data, transforming it as needed, and loading it into various destinations while handling complex requirements like scheduling, error recovery, and data type mappings.

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Looker ETL Tools Workflow

How data integration from Looker to a data warehouse can help

Companies might do Looker ETL for several reasons:

Business intelligence: Looker data may need to be loaded into a data warehouse for analysis, reporting, and business intelligence purposes.
Data Consolidation: Companies may need to consolidate data with other systems or applications to gain a more comprehensive view of their business operations
Compliance: Certain industries may have specific data retention or compliance requirements, which may necessitate extracting data for archiving purposes.

Overall, ETL from Looker allows companies to leverage the data for a wide range of business purposes, from integration and analytics to compliance and performance optimization.

Criterias to select the right Looker 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 Looker 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 10 Looker ETL tools

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

1. Airbyte

Airbyte is the leading open-source ELT platform, created in July 2020. Airbyte offers the largest catalog of data connectors—350 and growing—and has 40,000 data engineers using it to transfer data, syncing several PBs per month, as of June 2023. Major users include brands such as Siemens, Calendly, Angellist, and more. Airbyte integrates with dbt for its data transformation, and Airflow/Prefect/Dagster for orchestration. It is also known for its easy-to-use user interface, and has an API and Terraform Provider available.

What's unique about Airbyte?

Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required..

Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data.

Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.

2. Fivetran

Fivetran is a closed-source, managed ELT service that was created in 2012. Fivetran has about 300 data connectors and over 5,000 customers.

Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.

What's unique about Fivetran?

Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.

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

3. Stitch Data

Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.

Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be maintained by the open-source community.

What's unique about Stitch?

Given the lack of quality and reliability in their connectors, and poor support, Stitch has adopted a low-cost approach.

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

4. Matillion

Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.

What's unique about Matillion?

Being self-hosted means that Matillion ensures your data doesn’t leave your infrastructure and stays on premise. However, you might have to pay for several Matillion instances if you’re multi-cloud. Also, Matillion has verticalized its offer from offering all ELT and more. So Matillion doesn't integrate with other tools such as dbt, Airflow, and more.

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

5. 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.

6. 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.

7. 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.

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. Rivery

Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate.

10. 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.

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

Which data can you extract from Looker?

Looker's API provides access to a wide range of data categories, including:

1. User and account data: This includes information about users and their accounts, such as user IDs, email addresses, and account settings.

2. Query and report data: Looker's API allows users to retrieve data from queries and reports, including metadata about the queries and reports themselves.

3. Dashboard and visualization data: Users can access data about dashboards and visualizations, including the layout and configuration of these elements.

4. Data model and schema data: Looker's API provides access to information about the data model and schema, including tables, fields, and relationships between them.

5. Data access and permissions data: Users can retrieve information about data access and permissions, including which users have access to which data and what level of access they have.

6. Integration and extension data: Looker's API allows users to integrate and extend Looker with other tools and platforms, such as custom applications and third-party services.

Overall, Looker's API provides a comprehensive set of data categories that enable users to access and manipulate data in a variety of ways.

How to start pulling data in minutes from Looker ETL Tool

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

Step 1: Set up Looker as a source connector

1. Open Looker and navigate to the Admin panel.

2. Click on "Connections" and then "New Connection".

3. Select "Airbyte" as the type of connection.

4. Enter a name for the connection and the URL for the Airbyte instance.

5. In the "Authentication" section, select "OAuth2" as the authentication method.

6. Enter the Client ID and Client Secret provided by Airbyte.

7. In the "Advanced" section, set the "API Version" to "v1".

8. Click "Test" to ensure the connection is successful.

9. Save the connection and navigate to the "Explore" panel.

10. Select the Airbyte connection as the data source and choose the relevant tables to explore.

Note: It is important to ensure that the Airbyte instance is properly configured and the necessary connectors are installed before attempting to connect to Looker. Additionally, the specific steps for adding credentials may vary depending on the version of Looker being used.

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

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

Step 3: Configure the Looker 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 Looker 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 Looker ETL and how to best do it.

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Team Airbyte

Engineering Team of Airbyte.

Best ETL Tools for Looker Integration to follow in 2026

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