Top ETL Tools

Top 10 ETL Tools for Data Integration

Best 5 AWS Data Migration Service Alternatives

February 7, 2024

Discovering the ideal data migration service is paramount in today’s dynamic cloud landscape. While AWS Data Migration Service offers a robust solution, it may slow down when moving enormous volumes of data. So, exploring alternatives can be helpful to you for diverse business needs. From seamless database transfers to efficient data synchronization, each tool empowers you to replicate data with agility and precision. By delving into the unique features of these substitutes, you can make informed decisions while capitalizing on the flexibility and reliability of these services.

In this article, you will learn about AWS Data Migration Service, its benefits, limitations, and the alternative services for streamlined data transfer.

AWS Data Migration Service Overview

AWS Data Migration Service (AWS DMS) is a cloud service by AWS that helps you migrate data across 20+ databases and analytic engines. It allows you to actively manage data replication to AWS, ensuring fast and secure migration of your database and analytical workloads with zero data loss. DMS supports various data types, such as relational database structures, NoSQL data formats, semi-structured documents, large objects (LOBs), and primitives like integers, floats, strings, dates, and booleans.

AWS DMS’s capabilities extend beyond one-time data migration, allowing for continuous data replication. It operates by gathering changes from the database logs through the native API of the database system. This enables you to handle ongoing changes effectively, synchronizing source and target. 

Benefits of Using AWS Data Migration Service

Here are some advantages of using AWS DMS:

  • Ease of Use: While using AWS DMS for data integration, you don’t have to install any drivers or applications. Initiating a data migration is as simple as a few clicks using the AWS Management Console.
  • Downtime is Minimal: You can seamlessly transfer your databases to AWS with AWS DMS, ensuring minimal downtime. It maintains continuous replication of all data changes from the source database to the target, enabling full operational functionality of the source database throughout the migration process.

Limitations of AWS Data Migration Service

Here are some of AWS DMS's limitations that initiate you to choose other alternatives.

  • Large Data Volumes: Migrating extensive or full-load data can be challenging when using AWS DMS. During this process, it uses resources from your source database. 
  • Sync Issues: Sometimes, a discrepancy arises between the source and the target database, known as sync lag. This occurs due to interruptions in the replicating process, causing a ripple effect in the entire dataset of the target database. As a result, important changes in the source might not be detected in the target database.
  • Limited Support for Certain Databases: AWS DMS supports an array of databases. However, it is important to note that certain databases and versions are not fully supported. Examples include legacy or proprietary databases with closed ecosystems, niche or specialized databases, and some older versions of mainstream databases. 

Best 5 Alternatives for AWS Data Migration Service

Here are the top five alternatives for AWS DMS.


Airbyte is a data integration platform that helps you synchronize data from various sources to destinations such as data warehouses, lakes, databases, and more. It offers an extensive set of over 350+ pre-built connectors for seamless integration. If you don’t find your specific connectors, you can leverage its Connector Developer Kit (CDK) to construct a custom one. In addition, Airbyte allows you to create specific pipelines according to your preferences. You can build pipelines using UI, custom code, or API.

Some of the key features of Airbyte are:

  • Change Data Capture (CDC): Airbyte supports CDC functionality, enabling incremental data replication by capturing only the changes made to the source data, which enhances efficiency and reduces processing overhead.
  • Security: Airbyte guarantees the security of data movement by implementing robust measures, including strong encryption, audit logs, role-based access control, and secure transmission of data.
  • Handling Schema Changes: With Airbyte, you have the flexibility to manage schema changes for each transfer. It ensures the robustness of the data migration process, even in situations where the source schema undergoes continuous evolution.
  • Multiple User Access: You can create a collaboration in Airbyte with multiple users on a single instance, using Single Sign-On (SSO) and role-based access control (RBAC) for streamlined user management. This approach ensures smooth scalability, allowing you to effortlessly expand into multiple workspaces to meet the needs of larger teams.

Oracle GoldenGate

Oracle GoldenGate empowers you to replicate, filter, and transform data seamlessly. You can migrate data between Oracle and other supported heterogeneous databases. It also facilitates you to move data to Java Messaging Queues and Big Data targets by integrating with Oracle GoldenGate for Big Data. 

Some of the significant features of Oracle GoldenGate are:

  • Reduce Downtime: It helps you operate your system uninterruptedly during activities such as routine database maintenance, application updates, and migrations to new platforms. You can safeguard all your operations using the failback capabilities that minimize the risk of data loss, ensuring a secure and reliable process for handling these activities without experiencing downtime.
  • End-to-end Monitoring: Oracle GoldenGate ensures you the service level agreement (SLA) commitment by employing data verification techniques and gaining real insights into performance and usage statistics across all sources and targets. This focuses on end-to-end monitoring, assuring reliability and accountability.


Fivetran is an automated data integration platform that has the flexibility to work with either cloud or on-premise infrastructure. It has over 400 built-in, no-code source connectors for migrating data from various sources to your destinations. If it does not support any custom data source or private API, you can develop a custom connector with the help of its Function connector.

Some of the key features of Fivetran are:

  • Security: Fivetran enhances data security, safeguarding sensitive information like Personally Identifiable Information (PII) through features such as column blocking and hashing. Column blocking restricts specific replication-required columns, while column hashing protects PII data before transferring it to the target file. These measures minimize the risk of data exposure, providing you with greater control over your data.
  • Transformation: You can manage complex transformations in Fivetran by integrating with the dbt core, a freely available tool designed to simplify transformations.

Azure Migrate

Microsoft’s Azure Migrate is a cloud-computing platform that allows you to evaluate and effortlessly move your on-premises data or applications to the Azure cloud. It provides integrated features and assessment tools like server and database assessment, data migration assistance, and more. With the help of these tools, you can streamline the migration journey and enhance the efficiency of workloads within the Azure environment. The extensible framework also supports the integration of third-party tools, broadening the range of use cases it can handle.

Some of the significant features of Microsoft Azure are:

  • Discovery and Assessment: Azure Migrate serves as a centralized hub to monitor and manage the discovery, assessment, and migration process of on-premise infrastructure and applications. It also provides tools for discovering on-premise resources and evaluating their readiness for migration. Along with these offerings, Azure Migrate also supports third-party tools from Independent Software Vendors (ISVs).
  • Developer Tools: It offers extensive development tools that cater to various programming languages and operating systems, simplifying the process for you to create and deploy applications.

Qlik Replicate

Qlik Replicate is a data integration and replication tool offered by Qlik. It enables you to ingest, replicate, and integrate data across a broad spectrum of heterogeneous databases, data warehouses, and big data platforms. It is known to securely synchronize data with minimal operational impact, often using the log-based CDC technology for continuous replication in the target systems.

Some of the amazing features of Qlik Replicate are:

  • Monitoring and Control: Qlik Replicate helps you streamline your data management by utilizing a unified interface to create data endpoints and execute replication tasks. This empowers you to monitor thousands of tasks seamlessly through a single console, with user-defined alerts and Key Performance Indicators (KPIs).
  • Optimize Data: With Qlik Replicate, you can enable low-impact, near real-time Change Data Capture (CDC) across various database systems, providing flexible choices for handling captured data changes. This allows you to optimize data ingestion by capturing only the appended data, ensuring your target system stays up-to-date.


Exploring the five best alternatives to AWS Data Migration Service uncovers a diverse set of solutions, each crafted to address specific needs. These platforms offer a spectrum of capabilities from robustness to flexibility and simplicity. If you are looking for options outside the AWS ecosystem, refer to the tools mentioned above. You can select a migration solution that aligns with your scalability requirements, ease of use, and budget constraints. This array of options will not only enhance data migration processes but also empower you to optimize data management in the ever-changing cloud environment. Ultimately, searching for suitable replacements for AWS DMS opens avenues for tailored solutions catering to the unique demands of modern data migration.

We recommend the user-friendly features of Airbyte, a tool with a diverse range of connectors and robust security measures. Simplify your workflows today with Airbyte’s open-source version for free!

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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Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
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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.