Top ETL Tools

Top 10 ETL Tools for Data Integration

Top 10 Data Migration Tools to follow in 2024

February 13, 2024

With increasing data-driven requirements, you might need to perform various actions on data to analyze it and make strategic decisions. This necessitates the migration of data from one platform, OS, or database to another. Failing to migrate necessary data from one place to another can result in severe complications like high costs, longer transfer durations, data loss, or security issues. These factors will not only impact the overall flow but also other downstream activities. Therefore, migrating data successfully becomes one of the crucial processes. To expedite the tasks, you need a strong understanding of available data migration tools. 

To cut down your efforts, here’s a list of the best 10 data migration tools with their salient features.

What is Data Migration?

Transferring data from one system, storage, or format to another is a critical process known as data migration. This process is essential if you are aiming to streamline your ongoing operations, seek advantage of the latest technologies, and enhance your data management.

The data migration approach is taken into consideration for several reasons:

  • Transitioning from on-premises to cloud-based platforms.
  • Consolidation of data from various sources.
  • Upgrading the system while preserving existing data.
  • Relocating data centers.
  • Business mergers or acquisitions.

What are Data Migration Tools?

Numerous data migration tools are available to facilitate the process of transferring data between different systems, data warehouses, databases, or platforms. These migration tools play a vital role in ensuring projects' accuracy, efficiency, and success.

When selecting a data migration tool, consider your specific requirements, including:

  • Compatibility of migration tool with source and destination.
  • Volume and type of data.
  • Level of support for the target environment.
  • The complexity of the migration process.
  • Cost considerations.
  • Security measures.

Top 10 Data Migration Tools

Here’s a comprehensive list of the top 10 data migration tools that you can prefer to complete your migration process.


Airbyte is an open-source data integration and ETL tool that allows you to move and orchestrate data. It supports the migration of data from 350+ sources to destinations. With Airbyte, you can transfer data to various target systems, including databases, data warehouses, and more. One of its key features is its support for incremental data synchronization, enabling you to transfer and update only the changed data since the last migration. This capability not only enhances efficiency but also reduces the amount of data transferred, making the migration process resource-effective.

Here are some key features of Airbyte:

  • Schema Handling: For each transfer, Airbyte allows you to specify how schema changes in the source should be managed. This flexible schema handling ensures that the data migration process remains robust even in scenarios where source schema evolves continuously.
  • Build Your Connector: If the connector you need is not available in the pre-build list, Airbyte offers a variety of options for connector development. This includes the use of a Connector Development Kit (CDK), a no-code connector builder, and language-specific CDKs. These diverse options allow you to develop custom connectors according to your specific requirements and technical preferences.

Azure Migrate

Azure Migrate is a Microsoft service that allows you to assess and migrate your on-premises data or applications to the Azure Cloud. With its built-in features and assessment tools, you can simplify the migration process and optimize the performance of workloads in the Azure environment. 

Key features of Azure Migrate include:

  • Project Tracking: You can monitor and track your projects on its central dashboard. This helps you to observe the entire migration process and quickly address issues that may arise during migration.
  • Cost Estimation: It provides a cost estimation tool to help you understand the potential costs associated with the workload in Azure. This allows for better financial planning and decision-making.

AWS Database Migration

AWS offers a range of tool sets, amongst which AWS DMS is a fully managed database migration service. AWS DMS offers replication and migration services that allow you to quickly move your database and analytics workload to AWS. It supports homogeneous as well as heterogeneous migrations. This provides you with the flexibility to handle diverse migration scenarios.

Know some of the amazing features of AWS DMS:

  • Data Synchronization: DMS tasks are preferable for one-off migration or ongoing replication. Once the data migration is complete, DMS support for continuous data replication keeps the target database synchronized with the source for as long as you choose.
  • Cloud-native Integrations: As a cloud-based service, AWS DMS can seamlessly integrate with other AWS services.


Fivetran is an automated ETL (Extract, Transform, Load) solution that can be used to complete your migration tasks. It facilitates the transition of data from diverse sources into a centralized data warehouse with its large set of connectors. With the facilitation of multiple data integration processes, it places a strong emphasis on privacy, ensuring sensitive information is safeguarded through stringent measures.

Some of the best features of Fivetran are:

  • Extensive Connectors: It provides an array of 150+ pre-built connectors to numerous sources and destinations. If Fivetran doesn’t support your custom data source or private API, you have the option to develop a custom connector as an extension of Fivetran using a Function connector.
  • Schema Evolution: Fivetran provides automatic schema handling. It seamlessly adapts to changes in the source schema, such as changes in data types or new column names, and updates the target database columns accordingly. 


Matillion is a cloud-native data integration platform that facilitates you to extract, transform, and load data. It provides a low-code visual interface for designing, managing, and orchestrating data pipelines. This allows you to quickly perform data movement to and from multiple cloud data warehouses or databases. Its extensive support for various sources and target systems includes BigQuery, AWS, Oracle, SAP, Azure Cosmos, and more. 

Some of the significant features of the Matillion are:

  • Data Integration Process: Matillion supports ELT, ETL, and reverse ETL. This allows you to perform multiple data integration tasks within the Matillion platform. 
  • Data Transformation: You can leverage Matillion’s intuitive interface to develop straightforward transformation workflows. This is particularly well-suited for users with limited coding experience. However, for intricate queries and customized transformations, you can use SQL and Python scripts.


Acquired by Qlik, Talend is a data integration and ETL tool that allows you to connect and migrate data to a wide range of sources and destinations. With its simple drag-and-drop interface, you can move and manage data across multiple systems.

Here are some of the key aspects of Talend:

  • Monitoring: Talend provides powerful monitoring and logging features. This allows you to track the real-time execution of your migration jobs, identify errors, and view performance metrics.
  • Alerts and Notifications: You can also set alerts and notifications to be informed of job status changes or specific events. This helps ensure timely response to issues or completion of critical processes.


snapLogic is an integration Platform as a Service platform (iPaaS) that provides comprehensive data integration and workflow automation solutions. It helps you to move data between diverse systems, applications, and cloud platforms. With its user-friendly interface, you can design integration pipelines using a drag-drop approach. This makes it accessible to users with varying technical expertise.

Here are some salient features of snapLogic:

  • Connectivity: snapLogic offers a broad range of pre-built connectors, known as Snaps, to connect with various sources, applications, and databases. These connectors help you simplify the process of migrating data from diverse systems without the need for extensive coding.
  • Auto Sync: Depending on the configuration, with snapLogic’s AutoSync, you can automate data integration and synchronization from numerous sources to popular cloud data warehouses.

IRI NextForm

Developed by Innovative Routines International (IRI), IRI NextForm simplifies your data management tasks. It is primarily used for migrating data between multiple sources and targets. Apart from these services, it offers data integration, governance, replication, and data type conversion.

Some essential features of IRI NextForm include:

  • Format Conversions: With IRI NextForm, you can convert data between different formats. This includes converting files from one format to another or data between different data types.
  • Data Masking: You may include this feature to secure and manage sensitive information during data migration or transformation.

IBM Informix

IBM Informix is a relational database management system (RDBMS) designed to handle tremendous volumes of data and critical transactional workloads. With Informix, you can seamlessly migrate and manage various data types, including JSON, SQL, NoSQL, geospatial data, and time series data from source to target system.

Some of the essential features of IBM Informix include:

  • Data Management: IBM Informix provides a smart trigger feature for event processing, enabling push notifications when specific changes occur to data in a table. This functionality lets you quickly overview alterations, allowing for prompt actions.
  • Robust Against Failures: It ensures a successful migration process by automatically recovering from errors and failovers. This reduces the manual burden of constant verification until process completion.


CloudFuze is a cloud content management platform that enables you to migrate and govern data across multiple cloud storage providers. This includes moving documents, files, email accounts, and other content from one cloud service to another.

Some notable features of CloudFuze include:

  • Automation and Collaboration: It enables you to automate the migration process as well as collaborate with teams by facilitating actions such as file sharing across different cloud storage providers. 
  • Reporting: Beyond automation, it offers reporting and analytics features that allow you to develop reports and gain insights into data usage and other relevant metrics.

Wrapping Up

The increasing significance of data migration over the years has resulted in the availability of diverse high-performance tools. From the tools we have listed, you can see there’s a wide array of options available to migrate your data depending on the scenario. The right tool will help you seamlessly move your data from source to destination. A meticulous evaluation of your needs can assist you in making the right decision.

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

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

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • These ETL and ELT tools help in extracting data from 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 ETL as a reference to all data integration tools, ETL and ELT included, to integrate data from .

    How data integration from to a data warehouse can help

    Companies might do ETL for several reasons:

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

    Overall, ETL from 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 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 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.

    Top ETL tools

    Here are the top 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 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 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 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.


    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 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 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.

    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.


    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 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. 


    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. 


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

    Which data can you extract from ?

    How to start pulling data in minutes from

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

    Step 1: Set up as a source connector

    Step 2: Set up a destination for your extracted data

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

    Step 3: Configure the 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.


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

    What should you do next?

    Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

    flag icon
    Easily address your data movement needs with Airbyte Cloud
    Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
    Get started with Airbyte for free
    high five icon
    Talk to a data infrastructure expert
    Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
    Talk to sales
    stars sparkling
    Improve your data infrastructure knowledge
    Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
    Subscribe to newsletter

    Frequently Asked Questions

    What is ETL?

    ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

    What is ?

    What data can you extract from ?

    How do I transfer data from ?

    This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: set it up as a source, choose a destination among 50 available off the shelf, and define which data you want to transfer and how frequently.

    What are top ETL tools to extract data from ?

    The most prominent ETL tools to extract data include: Airbyte, Fivetran, StitchData, Matillion, and Talend Data Integration. These ETL and ELT tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities.

    What is ELT?

    ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

    Difference between ETL and ELT?

    ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.