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10 Best MongoDB ETL Tools for 2026

Compare the best MongoDB ETL tools on change streams, nested document flattening and incremental sync into Snowflake, BigQuery or Redshift.

March 31, 2026

Summarize with AI:

TL;DR

Short answer: MongoDB ETL tools differ mainly in how they read the database. Change streams capture inserts, updates and deletes as they happen; an incremental key only sees new and changed documents and silently misses deletes. That distinction matters more than connector counts. Ten tools:

Airbyte: 700+ connectors, reads MongoDB change streams, self-hosted or managed.
Fivetran: managed change-stream replication with no upkeep. Billed on monthly active rows.
Stitch: simplest setup, with log-based replication from the oplog, though schema changes need manual work.
Matillion: batch extraction with flattening pushed into your warehouse. Billed per credit.
Apache Airflow: an orchestrator, not an ETL tool. It schedules the extraction you write yourself.
Talend: native MongoDB components with data quality attached. No free tier since Open Studio retired.
Informatica PowerCenter: enterprise batch extraction, though 10.5 left standard support in March 2026.
Debezium: streams change-stream events continuously. The purist CDC option, and it needs Kafka Connect.
Rivery: cloud ELT with orchestration included, billed in credits.
Hevo Data: reads change streams and the oplog, no-code and cloud-only.

The other six are Apache Airflow, Talend, Informatica PowerCenter, Debezium, Rivery and Hevo Data, covered in full below.

What are MongoDB ETL tools?

MongoDB ETL tools are specialized tools that help you Extract data from MongoDB databases, Transform it according to your requirements (like changing data types, filtering, or aggregating), and Load it into different destination systems. For example, you can extract customer transaction data from MongoDB, transform it to match your data warehouse schema, and load it into Snowflake for analytics.

What types of MongoDB ETL tools are there?

Open Source ETL Tools

Open-source tools offer pre-built MongoDB connectors, visual workflow designers, and community-supported plugins to handle various ETL scenarios without licensing costs.

Cloud-Based Tools

These tools handle infrastructure management, automatic scaling, and real-time sync capabilities while offering pay-as-you-go pricing models.

Custom ETL Scripts

Engineers often build custom ETL scripts using programming languages like Python or Node.js to handle specific MongoDB data transformation needs. These scripts provide maximum flexibility and control over the ETL process but require internal maintenance and testing. PyAirbyte, an open-source Python library that simplifies data extraction from various sources, including MongoDB. It packages Airbyte connectors for use in Python environments, eliminating the need for hosted services.

Native MongoDB Tools

MongoDB's built-in tools like mongodump, mongoexport, and MongoDB Compass handle basic ETL operations. These tools are free, directly integrated with MongoDB, and suitable for simple and straightforward data migration and export tasks.

Why move MongoDB data into a warehouse?

Companies might do MongoDb ETL for several reasons:

Business intelligence: MongoDb 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.

Tool	Type	Connectors	Reads MongoDB via	Hosting	Open source
Airbyte	ELT	700+	Change streams, with full and incremental sync	Cloud and self-hosted	Yes
Fivetran	ELT	700+	Change streams, managed	Managed cloud	No
Stitch	Extract and load	140+	Log-based replication or incremental key	Cloud	No, built on the open Singer spec
Matillion	ELT	100+	Batch query, flattened in the warehouse	Your own cloud account	No
Apache Airflow	Orchestrator, not ETL	Not applicable	Whatever you write in Python	Self-hosted	Yes
Talend	Integration platform	Hundreds	Native MongoDB components	Cloud and on-premises	No, Open Studio retired January 2024
Informatica PowerCenter	ETL	200+	MongoDB connector, batch	On-premises	No
Debezium	Change data capture	Major databases	Change streams, streamed continuously	Self-hosted, via Kafka Connect	Yes, Apache 2.0
Rivery	ELT	150+	MongoDB connector, batch	Cloud	No
Hevo Data	ELT	150+	Change streams and oplog	Cloud	No

Which MongoDB ETL tools should you consider?

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

1. Airbyte

Airbyte is an open-source data integration platform that syncs data from MongoDB to various destinations like data warehouses and data lakes. It handles incremental updates using MongoDB change streams and offers both CDC and full refresh sync modes for different data loading requirements.

Deployment:

Self-hosted
Airbyte Cloud (managed service)

Key Features:

Pre-built MongoDB source and destination connectors
Automated schema mapping and data normalization
Built-in data versioning and transformation using dbt
Custom connector development

2. Fivetran

Fivetran is a cloud-native MongoDB ETL tool that provides automated data pipelines for data integration. Fivetran has 750+ sources and over 5,000 customers.

Deployment Options: Fully managed cloud service

Key Features:

Pre-built MongoDB connectors
MongoDB change data capture
Enterprise-grade security
Built-in data quality monitoring and alerting

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

3. Stitch

Stitch is a specialized cloud ETL service that provides reliable data pipelines for MongoDB integration. It is suitable for teams wanting minimal ETL maintenance overhead.

Deployment: Cloud-based SaaS platform

Key Features:

Point-and-click MongoDB replication
Singer protocol support for custom integrations
Built-in data governance and access controls

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

What else should you consider?

4. Matillion

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. Apache 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. 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. Debezium

Debezium is an open-source change data capture platform, and the most direct route out of MongoDB for teams that need continuous replication rather than scheduled batches. Its MongoDB connector subscribes to change streams, so inserts, updates and deletes arrive as events as they happen, without polling collections or adding query load to the cluster. It streams changes but does not load them, so you still need a sink, and it expects Kafka Connect or an equivalent runtime.

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. Hevo Data

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

How should you choose a MongoDB ETL tool?

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 MongoDb ETL solution.

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What data can you extract from MongoDB?

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

1. Documents: MongoDB stores data in the form of documents, which are similar to JSON objects. Each document contains a set of key-value pairs that represent the data.
2. Collections: A collection is a group of related documents that are stored together in MongoDB. Collections can be thought of as tables in a relational database.
3. Indexes: MongoDB supports various types of indexes, including single-field, compound, and geospatial indexes. Indexes are used to improve query performance.
4. GridFS: MongoDB's GridFS is a specification for storing and retrieving large files, such as images and videos, in MongoDB.
5. Aggregation: MongoDB's aggregation framework provides a way to perform complex data analysis operations, such as grouping, filtering, and sorting, on large datasets.
6. Transactions: MongoDB supports multi-document transactions, which allow multiple operations to be performed atomically.
7. Change streams: MongoDB's change streams provide a way to monitor changes to data in real-time, allowing applications to react to changes as they occur.

Overall, MongoDB provides access to a flexible and powerful data model that can handle a wide range of data types and use cases.

How do you start pulling data from MongoDB?

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

Step 1: Set up MongoDb as a source connector

1. First, you need to have a MongoDB instance running and accessible from the internet. You will also need to have the necessary credentials to access the database.

2. In the Airbyte dashboard, click on "Sources" and then click on "New Source."

3. Select "MongoDB" from the list of available sources.

4. In the "Connection Configuration" section, enter the following information:
- Host: The hostname or IP address of your MongoDB instance.
- Port: The port number on which your MongoDB instance is running.
- Username: The username you use to access your MongoDB instance.
- Password: The password you use to access your MongoDB instance.
- Authentication Database: The name of the database where your authentication credentials are stored.

5. Click on "Test Connection" to ensure that Airbyte can connect to your MongoDB instance.

6. If the connection is successful, click on "Save" to save your MongoDB source configuration.

7. You can now create a new pipeline and select your MongoDB source as the input. You can then configure the pipeline to transform and load your data into your desired destination.

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

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

Step 3: Configure the MongoDb 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.

Which MongoDB ETL tool should you choose?

This article outlined the criteria that you should consider when choosing a data integration solution for MongoDb 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 MongoDb ETL and how to best do it.

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10 Best MongoDB ETL Tools for 2026

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