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10 Microsoft Dataverse ETL Tools To Use in 2026

Compare 10 Microsoft Dataverse ETL tools that sync tables, choices, and relationships into your warehouse for Power Platform reporting in SQL.

March 31, 2026

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TL;DR

This guide compares 10 Microsoft Dataverse ETL tools for extracting data from the Power Platform.

Connector-led ELT: Airbyte, Fivetran, Stitch Data and Hevo Data handle Dataverse's API structure and access patterns for you.
Microsoft-native: SQL Server Integration Services fits teams already invested in the Microsoft stack and on-premises tooling.
Transformation and orchestration: Matillion, Pentaho, Informatica PowerCenter and Rivery reshape data in flight; Airflow schedules the jobs.
Three common use cases: warehouse integration, cross-platform sync and feeding analytics platforms.

Microsoft Dataverse ETL tools extract data from Dataverse and load it where it can be analysed, working around an API structure and set of access patterns that trip up generic connectors. This guide compares the options on real-world implementation rather than marketing claims. Whether you are building a warehouse or syncing Dataverse with other systems, the ten tools below cover the ground.

What is Microsoft Dataverse?

Microsoft Dataverse is a SaaS data platform that acts as the storage foundation for Microsoft Power Platform and Dynamics 365 applications, handling everything from basic tables to complex file storage. It's designed to store and manage various data types including relational, file-based, and observational data, making it suitable for both transactional and analytical workloads.

What is Microsoft Dataverse ETL?

Microsoft Dataverse ETL refers to the process of extracting data from Dataverse, transforming it to match your target system's requirements, and loading it into various destinations like data warehouses, databases, or other applications. It's about building reliable data pipelines that can handle Dataverse's unique API constraints while ensuring data consistency and freshness.

Common Use Cases for Microsoft Dataverse ETL Tools

Data Warehouse Integration

Your teams store business-critical data in Dataverse through Power Platform apps and Dynamics 365, but need it in your data warehouse for company-wide analytics. ETL tools automate this data movement, keeping your warehouse updated with fresh Dataverse data.

Cross-Platform Data Sync

You run multiple systems (like a custom CRM and marketing automation) that need customer data from Dataverse. ETL tools maintain consistent data flow between these platforms, ensuring your customer data stays synchronized across your tech stack.

Analytics Platform Integration

Your analysts need Dataverse data in their preferred analytics tools (like Tableau or Power BI) for business reporting. ETL tools handle the regular extraction and formatting of Dataverse data, making it readily available for analysis without manual exports.

Tool	Type	Founded	Connectors	Hosting	Open Source	Integrations
Airbyte	ELT	2020	700+	Cloud & Self-Hosted	✅ Yes	dbt, Airflow, Prefect, Dagster
Fivetran	ELT	2012	~300	Managed Cloud	❌ No	dbt
Stitch Data	ETL	2016	~30 maintained	Cloud	Partial (Singer)	Singer.io
Matillion	ELT	2011	~100	Self-Hosted	❌ No	None (verticalized)
Airflow (Apache)	Workflow Orchestrator	2014	N/A	Self-Hosted	✅ Yes	Airbyte, dbt, etc.
Pentaho	ETL + Analytics	2004	~100+	Enterprise	✅ Yes (originally)	Various
Informatica PowerCenter	ETL	1993	N/A	On-Prem Enterprise	❌ No	N/A
MS SQL SSIS	ETL	2005	N/A	On-Prem (Microsoft)	❌ No	Microsoft ecosystem
Rivery	ELT	2018	~150	Cloud	❌ No	Built-in orchestration
HevoData	ELT	2017	~150	Cloud	❌ No	Built-in transformation & activation

Top Microsoft Dataverse ETL tools

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

1. Airbyte

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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. 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. Microsoft SQL Server Integration Services (SSIS)

MS SQL Server Integration Services is the Microsoft alternative from within their Microsoft infrastructure. It offers ETL, and not ELT and its benefits.

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 Microsoft Dataverse, you might also find some other specific data loader for Microsoft Dataverse data. But you will most likely not want to be loading data from only Microsoft Dataverse in your data stores.

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Criterias to select the right Microsoft Dataverse 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 Microsoft Dataverse 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.

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Which data can you extract from Microsoft Dataverse?

Microsoft Dataverse's API provides access to a wide range of data types, including:

1. Entities: These are the primary data objects in Dataverse, such as accounts, contacts, and leads.
2. Fields: These are the individual data elements within an entity, such as name, address, and phone number.
3. Relationships: These define the connections between entities, such as the relationship between a contact and an account.
4. Business rules: These are rules that govern how data is entered and processed within Dataverse.
5. Workflows: These are automated processes that can be triggered by specific events or conditions within Dataverse.
6. Plugins: These are custom code modules that can be used to extend the functionality of Dataverse.
7. Web resources: These are files such as HTML, JavaScript, and CSS that can be used to customize the user interface of Dataverse.

Overall, the Dataverse API provides access to a wide range of data types and functionality, making it a powerful tool for developers and users alike.

How to start pulling data in minutes from Microsoft Dataverse

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

Step 1: Set up Microsoft Dataverse as a source connector

1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.
2. Click on the "Microsoft Dataverse" source connector and select "Create New Connection".
3. Enter a name for the connection and click "Next".
4. In the "Authentication" section, select "OAuth2" as the authentication method.
5. Click on the "Configure OAuth2" button and enter the required credentials for your Microsoft Dataverse account.
6. Once the credentials have been entered, click "Authorize" to allow Airbyte to access your Microsoft Dataverse data.
7. Select the entities you want to replicate and configure any additional settings, such as the replication frequency and data mapping.
8. Click "Test" to ensure that the connection is working properly.
9. If the test is successful, click "Create Connection" to save the connection and begin replicating data from Microsoft Dataverse to Airbyte.

Step 2: Set up a destination for your extracted Microsoft Dataverse data

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

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

10 Microsoft Dataverse ETL Tools To Use in 2026

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