Top ETL Tools

Top 10 ETL Tools for Data Integration

Top 5 BigQuery ETL Tools

February 7, 2024

Harnessing and interpreting data isn’t merely an advantage but a fundamental necessity for success across industries. It stands as a driving force revealing critical insights essential for informed decision-making. BigQuery plays a pivotal role in leveraging this information that empowers businesses to shape strategies and streamline operations. To unlock the full potential of BigQuery, organizations rely on various ETL tools designed to optimize data processing and manipulation. These tools simplify the process and improve data quality, accessibility, and analysis capabilities.

In this article, you’ll learn about the ETL process and the popular tools compatible with Google BigQuery. These tools aid in improving the entire process and streamlining the workflow.

What is ETL?

ETL stands for Extract, Transform, and Load. It is the process of extracting data from various sources, transforming it into a form that fits a desired structure or format, and then loading the necessary information into a target database or data warehouse. This process is repeated as new data is added, ensuring accuracy and completeness in the warehouse and maintaining up-to-date information suitable for data mining and reporting. You can automate the ETL process using ETL tools, which are software solutions that simplify data management strategies and improve data quality by offering a standardized method for retrieving, modifying, and ingesting.

What is BigQuery?

BigQuery is a fully managed data warehouse with built-in features like machine learning, geospatial analysis, and business intelligence that allow you to handle large data volumes. Its serverless architecture lets you execute SQL queries without the need to manage any infrastructure. 

BigQuery’s scalable, distributed analysis engine enables you to execute queries on terabytes in seconds and petabytes in minutes. You can query data stored in BigQuery or directly from where it resides using external tables. Overall, BigQuery offers a comprehensive platform that adapts to the evolving demands of modern data analytics.

Here are some of the key features of BigQuery:

  • With BigQuery, you can analyze and visualize geographical data using geography data types.
  • To assess data efficiently, it employs a columnar storage format for storing data. This format enables the system to scan individual columns across the extensive dataset, enhancing query performance.
  • Interacting with BigQuery is facilitated through interfaces like the Google Cloud console and command-line tool. In addition, you can use Python, Java, JavaScript, and BigQuery’s REST API and RPC API for transformation and management.

Top 5 BigQuery ETL Tools

Here are the top BigQuery ETL tools with various features and capabilities.


Airbyte is a data integration and replication platform with 350+ built-in connectors that allow you to have seamless data integration from multiple sources to destinations within a few minutes. You can also use its robust scheduling capabilities to automate data extraction at predefined intervals and facilitate data loading with an incremental loading approach. For incremental loading, it also supports CDC, which processes only the latest changes to the dataset, thereby ensuring efficiency in the data extraction process.

Here are some of the key features of Airbyte:

  • With Airbytes’s user-friendly Connector Developer Kit (CDK), you can create personalized connectors in 30 minutes. This helps you to connect data sources and destinations that aren’t readily available in the pre-built connectors list.
  • You can utilize dbt integration within Airbyte to perform custom transformations seamlessly.
  • It maintains data security using strong encryption, audit logs, access control based on roles, and secure data transmission.
  • You can choose the specific data streams you want to replicate from all available API streams supported by Airbyte. This implies customizing and prioritizing specific data sources based on your requirements.


Fivetran is a cloud-based platform with over 400+ pre-built source connectors that allow seamless data integration from various sources to destinations. It is an ETL cloud service that assists you in centralizing and transferring information from multiple SaaS sources, databases, data warehouses, and more. Fivetran helps you load substantial amounts of unstructured and semi-structured data using batch processes to data lakes, and it can also ingest structured data into data warehouses. 

Here are some of the amazing features of Fivetran:

  • Fivetran’s column blocking and hashing feature allows you to protect sensitive data like Personally Identifiable Information (PII), reducing the risk of data loss or cyberattacks.
  • To perform intricate transformations in Fivetran, you can integrate it with dbt core, an open-source tool crafted to simplify the task of data transformation.
  • Fivetran offers enhanced visibility into your data integration process through visual data lineage graphs. This allows you to quickly monitor the path of data from its source to its destination through various stages, facilitating comprehensive oversight and analysis.


Matillion is a cloud-based ETL and ELT platform with an extensive inventory of pre-built, out-of-the-box connectors for popular applications and databases. It lets you connect to any data source and swiftly build pipelines to ingest data into your preferred destination. This helps you to create a single source of truth with your data warehouse for effective data analytics workflows.

Here are some of the significant features of Matillion:

  • Matillion helps you to effortlessly schedule data pipelines using various parameters such as time or event-based triggers, API calls, or the availability of new data.
  • The push-down ELT technology harnesses your data warehouse’s power, processing intricate joins across millions of rows in seconds.
  • With the visual and low-code designer, you can perform simple conversions on all your data for seamless integration, enabling quick and smooth processing. However, you have to choose between Python or SQL for complex transformations.

Hevo Data

Hevo Data is a cloud-based ELT platform with around 150+ data source connectors that provide reliable data integration for your growing data needs. The intuitive user interface simplifies the setup and management of your data pipelines for both technical and non-technical users. Using Hevo, you can automate data collection from different applications and databases and load it into a data warehouse. It allows you to use its powerful drag-and-drop transformation feature to enrich your data without any manual coding.

Here are some of the key features of Hevo data:

  • Hevo provides flexible data replication options for syncing data between sources and destinations. This enables you to replicate the entire database, specific tables, or individual columns, allowing you to focus solely on relevant information.
  • With schema mapper, you have the option to define how data extracted from the source is stored in the destination. This feature automates the mapping between source event types and destination tables.
  • By assigning primary keys, Hevo allows you to remove duplicate data while loading to a database destination. If the warehouses have non-enforceable keys, only unique records are uploaded.


Talend is a cloud-based ETL and ELT data integration platform designed to manage large volumes of data. It seamlessly unifies data from multiple sources into on-premise or cloud-based data warehouses. With Talend, you can quickly construct essential data pipelines to perform ETL. It not only allows you to move and merge data but also helps with governance into a unified, low-code platform compatible with nearly any data source and architecture.

Here are some key features of Talend:

  • It provides flexibility across setups like on-premise, cloud, multi-cloud, and hybrid environments, ensuring that Talend can be utilized regardless of your organization’s preferences.
  • Delivers consistent value while upholding security and compliance requirements.
  • You can prepare data collaboratively with your team members in real-time, ensuring a smooth and streamlined workflow.

How to Import Data into BigQuery in Minutes

BigQuery ETL will provide you with an opportunity to utilize your data for diverse business objectives, from integration and analytics to compliance and performance optimization.

To unlock the full potential of your data, you need to extract it from your preferred source and load it into BigQuery. For this process, we recommend leveraging Airbyte, which helps you migrate data within a few clicks by following these three straightforward steps.

Step 1: Configure Your Source Connector

Log in to the Airbyte account and use the user-friendly interface to set up a source connector from which you intend to extract data.

Step 2: Configure BigQuery as a Destination

  • Navigate to the dashboard and click on the Destinations option.
  • Type BigQuery in the Search Box of the destination page and click on the connector.
  • On the BigQuery destination page, fill in the details such as Project ID, Dataset Location, and Default Dataset ID. Choose the Loading Method between GCS Staging and Standard Inserts. Then click on Set up Destination.

Step 3: Configure the Data Pipeline in Airbyte

After setting both the source and destination, proceed to configure the connection. This step involves choosing the source data (step 1), setting the sync frequency, and specifying the destination (step 2).

These three steps complete the data integration process using Airbyte, and you can initiate analyzing your data in BigQuery. This streamlined approach not only ensures reliable data transfer but also allows you to derive meaningful insights from your consolidated data.


This article outlines the top five ETL tools with diverse solutions catering to data integration needs. Each tool offers unique strengths, from robust features to user-friendly interfaces. Considering factors such as scalability, ease of use, and replication capabilities is crucial based on your requirements for making informed decisions. So choose among these top ETL BigQuery tools to seamlessly align with your organization’s objectives, ensuring a streamlined and efficient data extraction, transformation, and loading process within the Google BigQuery ecosystem.

We recommend using a user-friendly tool like Airbyte, which provides an extensive set of connectors and robust security features to simplify your workflows. Try Airbyte today!

What should you do next?

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

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

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • These ETL and ELT tools help in extracting data from and other sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities. Airbyte distinguishes itself by offering both a self-hosted open-source platform and a Cloud one..

    What is ETL?

    ETL (Extract, Transform, Load) is a process used to extract data from one or more data sources, transform the data to fit a desired format or structure, and then load the transformed data into a target database or data warehouse. ETL is typically used for batch processing and is most commonly associated with traditional data warehouses.

    What is ELT?

    More recently, ETL has been replaced by ELT (Extract, Load, Transform). ELT Tool is a variation of ETL one that automatically pulls data from even more heterogeneous data sources, loads that data into the target data repository - databases, data warehouses or data lakes - and then performs data transformations at the destination level. ELT provides significant benefits over ETL, such as:

    • Faster processing times and loading speed
    • Better scalability at a lower cost
    • Support of more data sources (including Cloud apps), and of unstructured data
    • Ability to have no-code data pipelines
    • More flexibility and autonomy for data analysts with lower maintenance
    • Better data integrity and reliability, easier identification of data inconsistencies
    • Support of many more automations, including automatic schema change migration

    For simplicity, we will only use ETL as a reference to all data integration tools, ETL and ELT included, to integrate data from .

    How data integration from to a data warehouse can help

    Companies might do ETL for several reasons:

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

    Overall, ETL from allows companies to leverage the data for a wide range of business purposes, from integration and analytics to compliance and performance optimization.

    Criterias to select the right ETL solution for you

    As a company, you don't want to use one separate data integration tool for every data source you want to pull data from. So you need to have a clear integration strategy and some well-defined evaluation criteria to choose your ETL solution.

    Here is our recommendation for the criteria to consider:

    • Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?
    • Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?
    • Ability to build new connectors: all data integration solutions support a limited number of data sources.
    • Support of change data capture: this is especially important for your databases.
    • Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature
    • Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?
    • Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ? 
    • Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an integration with dbt
    • Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.
    • Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.
    • Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.

    Top ETL tools

    Here are the top ETL tools based on their popularity and the criteria listed above:

    1. Airbyte

    Airbyte is the leading open-source ELT platform, created in July 2020. Airbyte offers the largest catalog of data connectors—350 and growing—and has 40,000 data engineers using it to transfer data, syncing several PBs per month, as of June 2023. Major users include brands such as Siemens, Calendly, Angellist, and more. Airbyte integrates with dbt for its data transformation, and Airflow/Prefect/Dagster for orchestration. It is also known for its easy-to-use user interface, and has an API and Terraform Provider available.

    What's unique about Airbyte?

    Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required.. 

    Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data. 

    Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.

    2. Fivetran

    Fivetran is a closed-source, managed ELT service that was created in 2012. Fivetran has about 300 data connectors and over 5,000 customers.

    Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.

    What's unique about Fivetran? 

    Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.

    Here are more critical insights on the key differentiations between Airbyte and Fivetran

    3. Stitch Data

    Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool More than 3,000 companies use it.

    Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be  maintained by the open-source community.

    What's unique about Stitch? 

    Given the lack of quality and reliability in their connectors, and poor support, Stitch has adopted a low-cost approach.

    Here are more insights on the differentiations between Airbyte and Stitch, and between Fivetran and Stitch.

    Other potential services


    Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.

    What's unique about Matillion? 

    Being self-hosted means that Matillion ensures your data doesn’t leave your infrastructure and stays on premise. However, you might have to pay for several Matillion instances if you’re multi-cloud. Also, Matillion has verticalized its offer from offering all ELT and more. So Matillion doesn't integrate with other tools such as dbt, Airflow, and more.

    Here are more insights on the differentiations between Airbyte and Matillion.


    Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.

    What's unique about Airflow? 

    Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.

    Here are more insights on the differentiations between Airbyte and Airflow.


    Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.

    What’s unique with Talend?

    What sets Talend apart is its open-source architecture with Talend Open Studio, which allows for easy customization and integration with other systems and platforms. However, Talend is not an easy solution to implement and requires a lot of hand-holding, as it is an Enterprise product. Talend doesn't offer any self-serve option.


    Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.

    What is unique about Pentaho? 

    What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions. 

    However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.

    Informatica PowerCenter

    Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.

    Microsoft SQL Server Integration Services (SSIS)

    MS SQL Server Integration Services is the Microsoft alternative from within their Microsoft infrastructure. It offers ETL, and not ELT and its benefits.


    Singer is also worth mentioning as the first open-source JSON-based ETL framework.  It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.


    Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate. 


    HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs. 


    Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA. 

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

    Which data can you extract from ?

    How to start pulling data in minutes from

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

    Step 1: Set up as a source connector

    Step 2: Set up a destination for your extracted data

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

    Step 3: Configure the data pipeline in Airbyte

    Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.

    And that's it! It is the same process between Airbyte Open Source that you can deploy within 5 minutes, or Airbyte Cloud which you can try here, free for 14-days.


    This article outlined the criteria that you should consider when choosing a data integration solution for ETL/ELT. Based on your requirements, you can select from any of the top 10 ETL/ELT tools listed above. We hope this article helped you understand why you should consider doing ETL and how to best do it.

    What should you do next?

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

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

    Frequently Asked Questions

    What is ETL?

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

    What is ?

    What data can you extract from ?

    How do I transfer data from ?

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

    What are top ETL tools to extract data from ?

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

    What is ELT?

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

    Difference between ETL and ELT?

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