Top ETL Tools

Top 10 ETL Tools for Data Integration

Best 9 Data Transfer Tools 2024

February 6, 2024

Organizations rely heavily on their data, progressively shifting more systems to the cloud. This shift indicates an increase in data volumes, necessitating the need for efficient data transport. Moving data conveniently and securely between different environments is essential for successful migration, data consolidation, collaboration, and analysis. This is where data transfer tools come into play. They are software applications designed to automate and simplify data movement between systems. Providing you with a variety of advantages, including increased efficiency, improved security, greater flexibility, and streamlined performance.

Choosing the suitable data transfer tool for your business involves considering several factors. Start with compatibility with your existing system. Next, check if the tool scales with your growing data volume. Lastly, ensure smooth integration into your workflow. By carefully evaluating these factors, you can select the one that best suits your needs.

As you read along, you will get to know about the top data transfer tools and their key features. 

Let’s begin!

Airbyte

Airbyte is a cloud-based data integration platform that simplifies data transfer between various sources and destinations. This transfer tool relies on a comprehensive system of pre-built connectors, acting as adapters to communicate with specific data systems.

It offers a vast catalog of over 350 pre-built connectors that allow you to connect data sources, including databases, APIs, and flat files hosted on the cloud. You can seamlessly transfer data to various destinations, including data warehouses, databases, and other cloud platforms.

The platform prioritizes cloud-hosted deployment with its Airbyte Cloud service, providing a convenient and scalable solution for businesses that prefer a managed approach. This centralized option eliminates the need for infrastructure management and allows for easy access and collaboration. If you are seeking greater control and customization, then Airbyte also offers a self-hosted open-source version.

Key Features:

  • If the current connectors that Airbyte provides do not support your data source, you can build a custom connector with the low-code connector builder feature.
  • The intuitive web interface enables you to configure and manage data pipelines without programming, offering simplicity and ease of use.
  • Flexible scheduling and pipeline execution options grant you granular control over data movement, allowing you to tailor data flows per your needs and requirements.
  • To ensure that only new or updated data is transferred into your target system, Airbyte supports incremental sync to avoid redundant transfers and the risk of errors or inconsistencies.

Stitch (Talend)

Stitch is a cloud-based data transfer tool that can be used to move data from source to data warehouse and databases. It is well-known for its ELT feature, which allows you to extract and load data into desired applications.

With Stitch, you can automate the data integration process using its 150 data sources and destination connectors. It eliminates the need for manual configurations or custom coding.

Stitch offers flexibility with both on-premise and cloud-based deployment options. You can go with on-premise if you want complete control and customization over the data integration process. However, cloud-based deployment is suitable if you prefer a user-friendly interface and seamless scalability.

Key Features:

  • You can detect and fix errors before they impact your data pipeline, as Stitch lets you set data quality rules at the source to keep your analytics accurate and reliable.
  • Stitch has data masking and anonymization features to protect sensitive information during integration and testing.
  • It supports the Change Data Capture (CDC) technique through binary log files in databases like MySQL, PostgreSQL, and Oracle, keeping your pipelines in sync with minimal lag.

Astera Centerprise

Astera Centerprise is an end-to-end data management platform that allows you to streamline data integration and governance processes. It offers a comprehensive suite of tools for data extraction, loading, transformation, and warehousing. This enables you to connect diverse data sources effortlessly, cleanse and enrich your data, and build robust data pipelines. With its intuitive interface and pre-built connectors, Astera caters to both technical and non-technical users, making it a versatile solution for your organization.

Beyond data integration capabilities, Astera Centerprise provides robust data governance features. This includes data lineage tracking, access control, and auditing to ensure the integrity and security of your data throughout its lifecycle. 

With Astera, you also get advanced data quality features to ensure the accuracy and reliability of your information. It adds another layer of assurance by employing profiling, validation rules, and anomaly detection to safeguard data integrity.

Key Features:

  • Astera can handle complex data structures like nested lists and arrays seamlessly with built-in functions and tools, so you don’t need custom scripting or manual manipulation.
  • Its built-in hierarchical transformations allow you to easily manipulate and extract meaningful insights from complex data formats.

 Fivetran

Fivetran is a cloud-based data integration platform that lets you implement both ELT and ETL processes. This flexibility allows you to choose your data integration approach based on your requirements and preferences.

The platform automates data extraction and loading, removing the burden of managing complex data pipelines and freeing IT resources for strategic tasks. It empowers you to analyze and derive insights from the consolidated data. 

Fivetran offers diverse deployment models, including cloud-native for agility, self-hosted for control, and hybrid for flexibility. It helps you tailor data integration needs per your specific infrastructure and security requirements.

Key Features:

  • Fivetran automatically adapts to schema changes in source systems and reflects them in the target system, eliminating manual intervention during the data integration process.
  • It offers ready-to-use data models that translate your extracted data into the required format for your chosen tools.
  • You can configure a secure and private network connection in Fivetran for data transfer to ensure enhanced security and compliance for sensitive data.

integrate.io (formerly Xplenty)

integrate.io is a cloud-based data integration platform that helps you connect your data from various sources and applications. It provides a visual interface for building data pipelines to extract, transform, and load (ETL) data into a central repository.

With integrate.io, you get various data transformation features, such as filtering, aggregation, and joining. These features enable you to clean and prepare your data before loading it into your target destination.

Key Features: 

  • It offers a built-in REST API connector that allows you to connect any API seamlessly without any manual coding and configuration.
  • integrate.io continuously monitors your data sources, logs changes in real-time, and automatically replicates them to the destination system as configured.
  • For backup compliance or analysis, you can take periodic data snapshots at defined intervals.

Google Cloud Dataflow

Google Cloud Dataflow is a cloud-based data transfer tool built for executing data pipelines that handle real-time and batch processing. It utilizes the Apache Beam programming model, offering a unified framework for designing and deploying data processing workflows.

With Dataflow, you can target data processing within the Google Cloud Platform (GCP) ecosystem utilizing services such as BigQuery and Cloud Storage. However, Dataflow also includes connectors to external systems like databases and cloud platforms, allowing you to integrate with broader data platforms.

Dataflow acts as your automated conductor for data pipelines, dynamically adjusting resources based on workload. It eliminates manual server setup and automatically provisions resources within the Google Cloud Platform.

Key Features:

  • Dataflow has a serverless architecture that automatically scales to handle varying data volumes without infrastructure management.

CloverDX

CloverDX is a data integration tool that helps you easily move your data from any source to many targets. Using its features, you can bring different types of data together and ensure accuracy. It also allows you to focus on data discovery and analysis within data lakes, as it provides insights into data lineage, quality, and structure.

In addition to its integration capabilities, CloverDX offers cloud-based deployment and on-premises deployment options. You can choose cloud-based if you have an agile team or organization. But, if your organization has strict security requirements or hybrid data infrastructure, then the on-premise deployment is a suitable choice.

Key Features:

  • The data profiling feature in CloverDX allows you to analyze data quality and identify potential inconsistencies and errors.
  • With visualizations and dashboards, CloverDX makes your data exploration and understanding easier.
  • Its collaboration features enable data governance and ensure data-driven decision-making.

AWS Data Migration Service

AWS Data Migration Service (AWS DMS) allows you to streamline data transfer processes, facilitating migration from various sources to target systems within the AWS ecosystem. You can transfer data from other cloud providers or between different AWS accounts.

Unlike other data transfer tools, AWS DMS primarily focuses on transferring data into AWS services and offers limited outbound capabilities. It provides some connectors to transfer data into AWS, such as Amazon RDS or Amazon S3, from external databases and cloud platforms.

AWS DMS is a cloud-based service within AWS that eliminates the need for infrastructure management or software installation. You can easily configure and manage transfer tasks through the AWS Management Console or programmatic tools.

Key Features:

  • It supports schema conversion, so you can seamlessly migrate between most database types, such as Amazon Aurora, PostgreSQL, MySQL, Oracle, etc.
  • DMS has continuous data replication to ensure your ongoing data synchronizes between source and target systems.
  • DMS automatically handles resource allocation and orchestration to streamline deployment and data transfer.

Informatica PowerCenter

With Informatica PowerCenter, you can tackle large-scale and complex data integration challenges across on-premises, cloud, and hybrid environments. Among all the data transfer tools, this utilizes traditional methods and cutting-edge AI through CLAIRE, an AI engine, to deliver essential facilities like intelligent data processing and advanced data quality management.

Informatica PowerCenter provides a vast library of pre-built transformations, mapping functions, and AI-powered data cleansing and enrichment capabilities through CLAIRE. These ensure data quality and consistency before integrating into target systems.

PowerCenter distinguishes itself by offering extensive connectivity to various data sources and destinations. It was traditionally deployed on-premises and has now evolved to cater to modern cloud environments. 

Key Features:

  • Informatica PowerCenter enables dynamic mapping, where mappings adapt to schema changes in source or target systems without manual intervention.
  • It offers you an option for leveraging in-memory processing for specific tasks, significantly accelerating data transformations and cleaning operations.
  • PowerCenter features robust data governance capabilities, including data lineage tracking, role-based access control, and auditing tools.
  • It also allows you to integrate custom machine-learning models for advanced data manipulation tasks.

Conclusion

There are various data transfer tools with their unique capabilities. Each tool comes with its own distinct features, allowing you to select on the basis of your needs. Some tools with cloud-native approaches, like Airbyte, focus on ease of use. Its extensive library of pre-built connectors, drag-and-drop interface, and robust scalability cater to various organizations, from agile startups to established enterprises.

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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TL;DR

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 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.

    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

    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. 

    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. 

    Meltano

    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.

    Conclusion

    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.