Top ETL Tools

Top 10 ETL Tools for Data Integration

Top 6 ELT Tools for Streamlined Data Operations

April 3, 2024

Data drives your organization's success, as it helps you analyze market trends, reduce costs, and maximize profits. However, the key task is integrating this data from various platforms for enhanced analysis and visualization. There are different data integration solutions that you can employ for migrating data, but the ELT approach is one of the most popular and modern solutions that allows you to store raw data as soon as it is generated. 

In this article, you will understand the ELT data integration process and the top six ELT tools you can employ for streamlined data operations in 2024.

What is ELT?

ELT is a popular data integration process that stands for Extract, Load, Transform. It allows you to move data from one place to another effortlessly. The process starts with collecting data from diverse sources in its original format. You then need to replicate the collected data into destinations like data warehouses or data lakes. Once the data is loaded, you can perform the transformation to make your dataset analytics-ready.

Usage of ELT Method

Here are a few major uses of employing the ELT method to fulfill your data integration needs:

  • The ELT approach offers flexibility since the transformation is performed after the data is loaded into the destination. This allows you to add extracted data of any format to your target system without complexity.
  • The ELT data pipelines leverage modern cloud data warehouses for storage purposes, which can be efficient for large datasets. Since the raw data is directly loaded, you have untransformed data at your disposal. This enables you to perform data analytics according to your needs without reaching out to the source for the original data.
  • One of the major benefits of using the ELT method is faster initial data loading compared to ETL. This makes it cost-effective, as you do not have to spend time and resources on an additional system for data transformation.

Top 6 ELT Tools

Here’s a comprehensive list of the top six ELT tools that you can employ to streamline your data integration process:

Airbyte

Airbyte

Introduced in 2020, Airbyte is a robust data integration platform. It uses a modern ELT approach to extract data from diverse sources, such as SaaS applications, flat files, and databases, and load it into a centralized repository. Airbyte provides a rich library of 350+ pre-built connectors to automate the creation of data pipelines. If you can't find a connector of your choice, you can always build custom connectors using CDK or request a new one by contacting Airbyte’s team. To help you adapt to modern integration practices, Airbyte supports data sources that manage unstructured, semi-structured, and structured data types.

Some of the unique features of Airbyte include:

  • Airbyte allows you to employ the Change Data Capture feature to identify changes made in the source dataset and replicate them in the destination. This enables you to keep track of your data, thus ensuring data integrity and consistency.
  • With Airbyte, you can design and manage your data pipelines efficiently using UI, API, PyAirbyte, and Terraform Provider. While the User Interface doesn’t require programming skills, the other three let you utilize programming skills to create custom data pipelines.
  • To protect your data from external threats, Airbyte offers various security measures such as authentication mechanisms, encryption, access controls, and audit logging. In addition to these features, it also complies with security certifications like ISO 27001 and SOC 2 Type 2.
  • You can integrate Airbyte with dbt to leverage transformation capabilities. With dbt, you can perform simple to complex transformations. This enables you to clean and enhance raw data and convert it into a format suitable for analysis and reporting.

Matillion

Matillion

Launched in 2011, Matillion is a cloud-based platform that provides streamlined data integration processes such as ETL, ELT, and Reverse ETL. It empowers you to collect data from multiple sources and load them into your preferred destination system for seamless data analytics. In addition to these features, it also offers an intuitive interface with powerful push-down ETL/ELT functionality. This technology allows you to utilize your data warehouse potential to process complex joins over millions of rows within seconds.

Some of the unique features of Matillion include:

  • With Matillion, you can access a catalog of 150+ pre-built connectors to integrate data from various sources into a target system effortlessly. You can also build custom connectors if your required connector is unavailable in their list.
  • Matillion is a dynamic platform that allows you to perform basic to advanced transformations. The basic ones include functions such as filtering, mapping, and aggregation, while the complex ones leverage SQL and Python scripts to create functions.
  • It is equipped with data replication features, enabling you to eliminate redundant data by keeping it in sync. You can easily copy the changes in your data source and replicate them in your destination file.

Skyvia

Skyvia

Skyvia is a cloud-based data integration platform introduced in 2014 to facilitate effective data management. It allows you to implement different integration solutions, such as ETL, to extract data from multiple sources and load it into a centralized repository. Due to these capabilities, it is capable of managing complex operations like data splitting, conversions, and lookups. In addition to its integration features, Skyvia ensures data integrity across all platforms by enabling you to track changes in your source file and copy them into the destination.

Some of the unique features of Skyvia include:

  • Skyvia provides access to more than 160 pre-built connectors for migrating data across multiple platforms. But if you are unable to find a connector of your choice, you can always request a new one by reaching out to Skyvia’s platform.
  • It offers a powerful backup and restore feature for cloud applications to keep your data secure. You can perform manual or scheduled backups and ensure that data is not lost during the replication process.
  • With Skyvia, you can create a synchronization package that enables you to perform bi-directional data synchronization between relational databases and cloud applications. It also allows you to synchronize data with different structures, maintain all data relations, and provide strong mapping settings for configuring the entire process.

Stitch Data

Stitch Data

Stitch Data, an integral part of the Qlik Data Integration platform, is an open-source ELT tool designed for developing and managing data pipelines. It lets you quickly collect data from various sources, including databases, and load it into a centralized repository. Stitch also provides features like orchestration, scheduling, monitoring, and error handling that help you take full control and visibility over data as it moves from the source to the target system.

Some of the unique features of Stitch Data include:

  • To maintain data synchronization, it offers replication features that enable you to choose which source columns or tables to duplicate, establish replication schedules, and automate loading into the destination system.
  • With its pre-built connections to over 140 data sources, including databases, SaaS apps, and cloud platforms, Stitch facilitates data movement in a few minutes. Apart from built-in ones, if your preferred source isn't accessible, you can develop new ones by adhering to Singer's guidelines, an open-source framework for writing data movement scripts. 
  • Stitch Data has many security measures to protect data confidentiality and integrity. These safety features include IP address whitelisting, SSH tunnels, control access, and encryption based on SSL/TLS. 

Fivetran 

Fivetran

Fivetran, developed in 2012, is a cloud-based platform for integrating data. It assists with many integration tasks, including ELT, data migration, transformation, and governance. Due to its user-friendly interface, you can quickly connect various sources and destinations and leverage the flexibility to optimize your integration strategy to meet specific business goals. The platform manages complicated data pipelines by offering an automated mechanism for data extraction and loading, saving up IT resources for other uses.

Some of the unique features of Fivetran include:

  • With more than 500 connectors, Fivetran offers comprehensive support for all major databases, including DynamoDB and MySQL, as well as data warehouses like Redshift and Snowflake. Using these connectors, you can quickly load data into the target system after extracting it from various sources. 
  • Fivetran’s data transformation features allow you to prepare, organize, and analyze information while maintaining the data quality. It primarily enables you to perform transformations with the dbt Core and Quick Start data models. Both approaches facilitate complex transformations in the dataset using simple SQL queries. 
  • It provides various data replication techniques to provide effective, real-time data replication suited to different business workloads. Using a simple setup, you can employ the log-based CDC functionality to rapidly detect changes in your source data and replicate them into your desired destination.

Hevo Data

Hevo Data

Hevo Data is a powerful cloud-native integration service that provides end-to-end automated data pipelines. Its pre-built connectors library lets you collect data from 150+ sources, including SaaS applications or databases, and load them to over 15 destinations. This makes it easier to automate the integration process and employ the unified data for analytics and visualization. 

Some of the unique features of Hevo Data include:

  • Hevo Data leverages CDC functionality, which enables you to monitor and record changes in your source data files and replicate them to your preferred destination.
  • It mainly facilitates three transformations—in-flight, user-driven, and post-data. The in-flight process allows you to make minor changes, such as removing non-alphanumeric characters from a table, while the user-driven process lets you clean and filter the data. Both these processes are performed before loading the data into the destination. Finally, the post-data process involves data refining after loading.
  • With Hevo, you can safeguard your data from unauthorized access using features such as VPN, SSH, and Reverse SSH connections. It also adheres to best security practices, such as GDPR, HIPAA, and SOC 2, to maintain data confidentiality.

Final Word

Data integration is essential to your business activities as it empowers you to perform extensive data analysis by keeping them in one place. With the advent of cloud-based data lakes and warehouses, the ELT approach to moving and consolidating data has become quite popular. This article briefly discussed the top six ELT tools you can leverage to solve your data replication needs. Each tool is equipped with diverse features and is tailored to perform specific tasks based on your enterprise needs.

We suggest using Airbyte to move your data. It offers a rich library of pre-built and custom connectors to automate your data pipelines. Sign in on the Airbyte platform today to navigate through the different features it offers.

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

    Build powerful data pipelines seamlessly with Airbyte

    Get to know why Airbyte is the best ELT Tools

    Sync data from ELT Tools to 300+ other data platforms using Airbyte

    Try a 14-day free trial
    No card required.

    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.