Top ETL Tools

Top 10 ETL Tools for Data Integration

Top 10 Data Transformation Tools to follow in 2024

February 13, 2024

In today’s data-driven world, data is the most crucial part of a business. While raw data holds limited value, transforming it is essential to obtain quality data for analysis. The solution lies in using data transformation tools. These tools simplify the operation and allow you to process large amounts of data in less time. So, choosing a suitable tool is of the utmost importance.

In this article, you’ll learn the basics of the data transformation process and some popular tools available. These tools help you enhance the entire process and simplify the workflow.

What is Data Transformation?

Data transformation is a process of converting data from one format or structure to another. This process involves various tasks such as cleaning, sorting, filtering, validating, and merging data from multiple sources. The primary purpose of data transformation is to be compatible with the target system, making it more suitable and organized for analysis or utilization in decision-making processes. Automating this process is possible using data transformation tools, which are software solutions that expedite and streamline the transformation process. Though you can manually transform data, it is usually time-consuming and prone to errors. Data transformation tools overcome these limitations, reducing the need for human efforts.

Some of the benefits of data transformation are:

  • It ensures the accuracy and consistency of data, enabling organizations to possess high-quality data for analysis and decision-making.
  • Validation, cleansing, and reformatting enhance the data quality by preventing errors like incorrect indexing and duplication.
  • Decluttering datasets optimizes technical performance and enables you to get better insights.
  • Effective transformations remove data flow bottlenecks, where the data slows down or gets congested, making it effortless to scale your assets and the associated process.

Top 10 Data Transformation Tools

Here are the top ten data transformation tools with various features and capabilities that you can utilize to complete the process.

Airbyte

Airbyte is a data integration and replication tool that provides pre-built and custom connectors to migrate your data in minutes. With Airbyte, you can transfer data seamlessly from over 350+ sources to destinations, including popular databases and data warehouses. It enables you to define and apply changes to the data as it flows through the platform, including filtering and renaming columns. Although Airbyte doesn’t have built-in data transformation features, you can use Airbyte with dbt to execute advanced transformations. This lets you handle transformations according to your needs, ensuring a tailored data processing workflow.

Here are some of the key features of Airbyte:

  • Build Custom Connectors: If you do not find the preferred connector in the pre-built list, Airbyte provides various options to create a custom connector. This encompasses using the Connector Development Kit (CDK), language-specific CDKs, and a no-code connector builder. These options allow you to quickly develop connectors tailored to your needs.
  • Security: Airbyte ensures data movement security through robust measures, employing strong encryption, audit logs, role-based access control, and secure data transmission.

dbt

dbt, the Data Build Tool, developed by dbt labs, is a command-line data transformation tool designed for technical experts in SQL and Python coding. It integrates with popular cloud data warehouses like Bigquery, Snowflake, Redshift, Databricks, and other databases. In addition to this, dbt strictly adheres to software best practices, ensuring portability, modularity, Continuous Integration, and Continuous Delivery (CICD), resulting in exceptional scalability.

Here are some of the salient features of dbt:

  • dbt Cloud IDE: The dbt Integrated Development Environment (IDE) consolidates building, testing, running, and version-controlling dbt projects into a single web-based interface, helping experienced and beginner developers.
  • Discovery API: Every time you run a project on dbt Cloud, it generates and stores project information. With the dbt cloud Discovery API, you can query this information and understand the insights into your DAG, visualize the data pipelines, and understand the dependencies between data models. This enhances data discovery, quality, and operational efficiency.

Matillion

Matillion is a cloud-based data integration tool that helps you complete your ETL and ELT jobs effortlessly. It offers a visual user interface and a low-code designer to complete simple transformations. However, for performing complex transformations, you have the option to use Python or SQL. You can also handle changes across multiple jobs with the parallel processing option during transformation, which helps to increase the processing speed.

Some of the amazing features of Matillion include:

  • Unified Platform: The unified platform helps technical experts and users with minimum technical knowledge to move, transform, and organize data pipelines faster, making the data workplace more productive.
  • Orchestration: Matillion’s intuitive Graphical User Interface (GUI) helps you create orchestration jobs and sophisticated ETL pipelines. This empowers you to manage data efficiently, ensuring streamlined operations and enhanced control over the entire process.

IBM DataStage

IBM DataStage is a data-integration tool that provides a graphical framework to develop jobs for moving data from source to destination systems. It offers real-time data transfer capabilities and features like built-in search, automatic metadata propagation, and simultaneous error highlighting, providing a comprehensive solution. You can choose a basic on-premise version or upgrade to IBM Cloud Pak for a more efficient data integration journey.

Here are some of the key features of IBM DataStage:

  • Data and AI Services: IBM DataStage provides various data and AI services, including data science, virtualization, warehousing, and event messaging. With all these services, you can manage data proficiently and securely in one place.
  • Pre-built Connectors: It offers extensive pre-built connectors for data movement between multiple data warehouses and cloud sources such as IBM Db2, Netezza, and many more.

Hevo Data

Hevo Data is a cloud-based data integration tool designed for ETL and ELT data pipeline requirements. It offers an intuitive no-code user interface to seamlessly replicate data from over 150+ sources in near to real-time. Once the data is extracted from the source, you can perform the transformations based on your requirements before loading the data to the destination. This can be achieved using a drag-and-drop feature or Python-based script.

Here are some of the significant features of Hevo Data:

  • Schema-mapper: The schema mapper allows you to specify how the data extracted from the source application should be stored in the destination. With this feature, you can automate the mapping between the source event types and the destination tables.
  • Real-time Monitoring: Hevo Data helps you to monitor the data pipeline in real-time. This enables you to promptly identify and address any errors or issues, thus minimizing data downtime and ensuring continuous data flow.

Informatica

Informatica is a cloud-based data integration tool for unified data management. It allows you to transform data across cloud or hybrid infrastructures and supports both real-time and batch integrations with traditional databases. Informatica provides two types of transformations: Connected Lookup transformation, where one transformation is connected to the other during mapping, and Unconnected Lookup transformation, which is not linked to any further transformation. This flexibility enables you to perform intricate transformations and address business logic in your integration workflow.

Here are some salient features of Informatica:

  • Visual Interface: Informatica provides a user-friendly graphical interface that helps in seamless data integration, enabling you to transform data into manageable workflows.
  • Security: Its robust security measures allow you to secure complete data integration and transformation processes by implementing complete user authentication and granular management. This comprehensive approach to security ensures that only authorized users have access with complete control over privacy settings.

Talend

Talend is a cloud data integration tool designed to handle vast data volumes. It helps you seamlessly unify data from various sources into on-premises or cloud-based data warehouses, ensuring a secure foundation for analysis. With Talend, you can rapidly detect quality issues, hidden patterns, and anomalies through graphical representation. This allows you to ensure secure data sharing, identify datasets that require further cleansing, and streamline the analysis process.

Here are some of the important features of Talend:

  • Data Management: Talend supports end-to-end data management needs across the organization by offering a comprehensive platform for data integration, governance, and quality control.
  • Flexibility: It offers flexibility on various setups such as on-premises, cloud, multi-cloud, and hybrid. This versatility ensures you can leverage Talend irrespective of your organization’s infrastructure preferences or requirements.

Trifacta

Acquired by Alteryx, Trifacta is a robust data integration tool that empowers you to prepare data interactively and collaboratively. The intuitive drag-and-drop feature accelerates the process by leveraging AI/ML-based suggestions that guide you through the transformations. With Trifacta’s user-friendly interface, you can create data pipelines to perform seamless transformations without writing a single line of code.

Here are some of the key features of Trifacta:

  • Data Wrangler: Trifacta provides a visual interface for data wrangling tasks that include data cleansing, transformation, and data enriching. This helps you achieve accurate and meaningful data, thereby enhancing analysis accuracy.
  • Scalability: Trifacta ensures optimal performance by offering unlimited scalability. This enables you to handle increasing workloads and data volumes without limitations to processing and managing the information.

Dataform

Dataform is a cloud-based platform that enables you to manage and streamline data workflows. It is used to transform data that is already loaded in your warehouse. With the help of Dataform, you can run SQL commands to create tables and views in the data warehouse. As a result, you get well-tested datasets ready to be used for fast and efficient data management.

Here are some of the amazing features of Dataform:

  • Documentation: The Dataform platform offers a robust documentation feature for datasets. Using Javascript, you can activate script code and reuse it to prevent repetition.
  • Version Control: With the version control feature, review all changes before performing transformations. Once the data is validated, you can rely on a well-documented and thoroughly tested dataset showcased on your reporting dashboards.

Easymorph

Easymorph is a purpose-built tool that simplifies data preparation and ETL processes, catering to technical as well as non-technical users. Its visual UI allows you to design workflows without writing any code, and you can execute them easily with a single button click or on a scheduled basis. This streamlines the transformation process by reducing human errors, eliminating tedious routine work, and increasing efficiency.

Here are some of the significant features of Easymorph:

  • Database Support: Easymorph supports 25+ relational databases, including SQL Server, Postgres, Oracle, and more, to import and export data from databases, spreadsheets, and others.
  • Workgroups Collaboration: By incorporating the Easymorph server, you can create workgroups for collaboration. It also facilitates integration with enterprise or cloud applications, enhancing the capabilities beyond individual use provided by the desktop.

Conclusion

Over the years, the increasing significance of data transformation has spurred the development of numerous high-performance tools. Each new tool brings its own set of unique features. The tools listed here showcase some of the best options available for your data transformation, catering to different scenarios. Depending on specific requirements, you can select the most suitable tool from this array of options.

We recommend using Airbyte with dbt to streamline the data transformation process. Airbyte provides a user-friendly interface and an extensive set of connectors, making it an ideal choice for simplifying your workflows.

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.