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Top ETL Tools for AWS CloudTrail Integration to follow

Maximize AWS CloudTrail data efficiency with leading ETL tools. Streamline AWS analytics effortlessly!

March 31, 2026

Summarize with AI:

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

Airbyte

Fivetran

Stitch

Matillion

These ETL and ELT tools help in extracting data from AWS CloudTrail 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 is ELT?

More recently, ETL has been replaced by ELT (Extract, Load, Transform). ELT Tool is a variation of ETL one that automatically pulls data from even more heterogeneous data sources, loads that data into the target data repository - databases, data warehouses or data lakes - and then performs data transformations at the destination level. ELT provides significant benefits over ETL, such as:

Faster processing times and loading speed
Better scalability at a lower cost
Support of more data sources (including Cloud apps), and of unstructured data
Ability to have no-code data pipelines
More flexibility and autonomy for data analysts with lower maintenance
Better data integrity and reliability, easier identification of data inconsistencies
Support of many more automations, including automatic schema change migration

For simplicity, we will only use AWS CloudTrail ETL as a reference to all data integration tools, ETL and ELT included, to integrate data from .

How data integration from AWS CloudTrail to a data warehouse can help

Companies might do AWS CloudTrail ETL for several reasons:

Business intelligence: AWS CloudTrail 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 AWS CloudTrail allows companies to leverage the data for a wide range of business purposes, from integration and analytics to compliance and performance optimization.

Furthermore, AWS ETL tools offer robust features that enable companies to seamlessly integrate, transform, and analyze data from various sources, empowering them to drive actionable insights and enhance decision-making across their operations.

Tool	Type / Model	Connectors	Ease of Use	Ideal For
Airbyte	Open-source ELT	600+ (customizable)	Very easy – UI + AI + API	Flexible, scalable data integration
Fivetran	Closed-source ELT	300+ managed	Plug-and-play	Fully managed, hands-off ELT
Stitch Data	Cloud ETL (Singer-based)	~30 maintained	Easy but limited	Low-cost ETL for small teams
Matillion	Self-hosted ELT	100+	Moderate setup	Secure, on-prem ELT control
Apache Airflow	Open-source orchestrator	N/A (operators)	Technical, Python-based	Workflow automation & scheduling
Talend	Enterprise ETL / Data Quality	1000+	Complex enterprise setup	Data quality, governance & ETL
Pentaho	Enterprise ETL + BI	100+	Complex	Data analytics + ETL
Informatica PowerCenter	Enterprise ETL	100+	Technical setup	Large enterprises & governance
Singer	Open-source ETL framework	100+ (community taps)	Technical (JSON-based)	Developers building connectors
Rivery	Cloud ELT	150+	Easy, low-code	Unified data pipeline automation

Top AWS CloudTrail ETL tools

Here are the top AWS CloudTrail 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.

Other potential services

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.

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.

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.

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.

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.

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.

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.

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

Which data can you extract from AWS CloudTrail?

AWS CloudTrail provides access to a wide range of data related to AWS account activity and resource usage. The following are the categories of data that can be accessed through the API:

1. Event history: This includes information about all the events that have occurred in an AWS account, such as API calls, console sign-ins, and resource changes.
2. Resource activity: This category includes data related to the usage of AWS resources, such as EC2 instances, S3 buckets, and RDS databases.
3. User activity: This category includes data related to user activity in an AWS account, such as user sign-ins, password changes, and access key usage.
4. Security analysis: This category includes data related to security events in an AWS account, such as failed login attempts, unauthorized access attempts, and changes to security groups.
5. Compliance auditing: This category includes data related to compliance auditing in an AWS account, such as changes to IAM policies, CloudTrail configuration changes, and VPC network changes.

Overall, the AWS CloudTrail API provides a comprehensive view of AWS account activity and resource usage, making it a valuable tool for monitoring and managing AWS environments.

Criterias to select the right AWS CloudTrail 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 AWS CloudTrail 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 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.

How to start pulling data in minutes from AWS CloudTrail

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

Step 1: Set up AWS CloudTrail as a source connector

1. First, navigate to the AWS Management Console and log in to your account.
2. Once logged in, search for the CloudTrail service and select it.
3. In the CloudTrail dashboard, select the Trails option from the left-hand menu.
4. Click on the name of the trail you want to use as your source connector.
5. In the trail details page, scroll down to the section labeled "Management events" and click on the "Edit" button.
6. In the "Data events" section, click on the "Add data event" button.
7. Select the type of data event you want to capture and configure the settings as needed.
8. Once you have configured the data event, click on the "Save" button to save your changes.
9. Next, navigate to the Airbyte dashboard and select the "Sources" option from the left-hand menu.
10. Click on the "Create a new source" button and select the AWS CloudTrail connector.
11. Enter your AWS access key ID and secret access key in the appropriate fields.
12. Enter the name of the S3 bucket where your CloudTrail logs are stored.
13. Enter the name of the CloudTrail trail you want to use as your source connector.
14. Click on the "Test" button to ensure that your credentials are valid and that Airbyte can connect to your CloudTrail logs.
15. Once the test is successful, click on the "Create" button to create your AWS CloudTrail source connector in Airbyte.

Step 2: Set up a destination for your extracted AWS CloudTrail data

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

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

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Top ETL Tools for AWS CloudTrail Integration to follow

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