Top ETL Tools

Top 10 ETL Tools for Data Integration

Top ETL Tools to follow in 2024

February 15, 2024

The evolution of data storage, processing, and integration, particularly with the advent of cloud computing, has transformed how businesses handle and leverage data. Cloud-based solutions allow organizations to store and manage vast amounts of data without significant investments in infrastructure. 

The emergence of cloud-based data operations is a driving force behind the modernization of ETL processes. ETL plays a fundamental role in preparing your dataset for further analysis. It structures, refines, and seamlessly integrates the data into modern data ecosystems. This process elevates the quality and consistency of your data, contributing to strategic enhanced decision-making.

The advancement of the ETL process has resulted in the development of sophisticated tools and technologies. If you are looking to choose the best ETL tool for your business, you have arrived at the right place! This article provides a detailed overview of the ETL process and introduces the top 11 ETL tools to help you make informed decisions.

What is ETL and Why is it Needed?

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ETL, short for Extract, Transform, and Load, is a vital data integration process aimed at consolidating information from diverse sources into a centralized repository. The method involves collecting data, applying standard business rules to clean and reform data in a proper format, and finally, loading it to a data warehouse or database. Look at each of the terms in more detail.

  • Extract: The extraction stage involves retrieving data from different sources, including SQL or NoSQL servers, Customer Relationship Management (CRM) platforms, SaaS applications and software, marketing platforms, and webpages. The raw data is then exported to a staging area, preparing it for subsequent processing.
  • Transform: In the transformation stage, the extracted data undergoes a series of operations to ensure it is clean, formatted, and ready for querying in data warehouses. Transformation tasks can include filtering, de-duplicating, standardizing, and authenticating the data to meet the specific demands of your business.
  • Load: The loading phase in the ETL process is where the transformed data is transferred to the designated data destination, which can be a data warehouse or database. The loading can involve moving the entire dataset or migrating only the latest changes made to the dataset. It can be done periodically or continuously in a way that there is minimal impact on the source and target systems.

Utilizing ETL processes enables you to study raw datasets in a suitable format necessary for analytics and deriving meaningful insights. It facilitates tasks such as studying demand patterns, shifts in consumer preferences, latest trends and ensuring compliance with regulatory standards. 

Today, ETL tools automate the data migration process, offering flexibility to set up periodic integrations or perform them during runtime. They allow you to focus on important tasks at hand instead of carrying out mundane tasks of extracting and loading the data. It is vital to pick the best ETL tool for your business, so take a moment to understand some of the popular ETL tools available today.

Types of ETL Tools and Their Purpose

The landscape of ETL tools has evolved over time, leading to their categorization into four groups based on the infrastructure, organization, or vendor.

Open-Source ETL Tools

Open-source ETL tools offer usage without any charges and have freely available source code for potential enhancements. These tools can vary in quality, integration, ease of use, and available support for pipeline design and execution. With open-source ETL tools, you also get support and feedback from a robust developers community who contribute to the continuous improvement of features.

Cloud-Based ETL Tools

Several prominent cloud-based providers like Amazon Web Services, Google Cloud Platform, and Microsoft Azure offer ETL tools integrated into their infrastructure. Cloud-based ETL tools have notable advantages in efficiency and connectivity with other platform services in the shared infrastructure. Leveraging cloud technology provides high latency, elasticity, and availability. However, a limitation of cloud-based ETL tools is the confinement to the specific cloud provider’s environment. There is a limited or lack of support for data stored in other cloud vendor platforms or on-premise data centers.

Custom ETL Tools

Businesses equipped with internal data engineering teams can design their own ETL tools and pipelines using versatile programming languages like Python, Java, or SQL. These custom ETL tools are tailored to specific business priorities and workflows. While they provide the utmost flexibility, these tools require substantial effort in handling documentation creation, development, testing, maintenance, and more. Additionally, you must consider investing in training internal resources and seeking external assistance to manage the data pipeline.

Enterprise Software ETL Tools

Enterprise Software ETL tools are developed and backed by commercial organizations that offer comprehensive data solutions. These tools have better graphical user interfaces for creating data pipelines, support a wide array of relational and non-relational databases (JSON and XML), have extensive documentation, and offer data privacy features. Due to all these features, they can sometimes go beyond your budget. They also necessitate more employee training due to the inherent complexity of the integration services.

Factors for Choosing the Best ETL Tool

Your organization’s use case is a critical determinant in the tool selection process. You must thoroughly assess your data requirements before finalizing your ETL tool. If you have highly sensitive data information, you can go with cloud-based or enterprise software ETL tools that are compliant with strict regulatory requirements and have high-security systems. You can also build a custom ETL tool if your organization has a skilled team.

If you require real-time updates from your data pipeline, opting for cloud-native or open-source tools where you get change data capture (CDC) features is advantageous. It is crucial to prioritize features that ensure data quality and efficiency in execution time. The tool should perform optimally even when handling vast volumes of records. Long processing times can be cumbersome and hinder other priority tasks.

Key Considerations for ETL Tool Selection

  • Compatibility: Your ETL tool should be compatible with various operating systems and offer seamless integration with different data sources via APIs or connectors. Being compatible with several applications can minimize the need for manual pipeline creation through programming. This reduces the chances of error and saves valuable time.
  • Ease of Use: An ideal ETL tool must have a user-friendly interface, ensuring accessibility for both technical and non-technical users. Navigating between different sections and features should be straightforward. You should be able to set up the source and destination in just a few clicks or through a drag-drop functionality for increased efficiency.
  • Scalability: It is essential for an ETL tool to accommodate the growing needs of your business. This includes handling large data volumes, concurrent data loads, processing data from multiple sources and formats, and integrations with third-party applications.
  • Handling Bottlenecks: The ability to handle errors and avoid bottlenecks is a crucial feature of top ETL tools. Unexpected issues like corrupt data or network failures can disrupt your pipelines. Considering these challenges, you must choose a robust tool that has a strong support team to look after potential issues, ensuring data accuracy and consistency.
  • Cost-effective: Cost considerations are pivotal, and your chosen ETL tool must align with your budget. ETL tools offer several pricing plans with different features that can be scaled according to your needs.

The Top 11 ETL Tools in 2024

After a complete understanding of ETL processes and the types of tools, here’s a comprehensive guide elaborating on the best ETL tools to handle modern data workloads.

Airbyte

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Airbyte is one of the best data integration and replication tools for setting up seamless data pipelines. This leading open-source platform offers you a wide catalog of 350+ pre-built connectors. Even if you are not proficient at coding, you can quickly load your data from the source to the destination without writing a single line of code.

Although the catalog library is quite expansive, you can still build a custom connector to data sources and destinations not present in the pre-built list. Creating a custom connector takes a few minutes because Airbyte makes the task easy for you. There is a Connector Development Kit (CDK) that helps you configure your data pipeline seamlessly.

Airbyte Services

  • Airbyte Self-Managed (Open-Source and Enterprise-Ready Features)

Airbyte Self-Managed service grants you the flexibility to host and manage your data pipelines on the platform independently. You can deploy enterprise-wide features while receiving professional support with custom SLAs.

Airbyte open-source provides seamless compatibility with a variety of tools such as dbt, Airflow, Dagster, Prefect, and more. This version allows you to harness the power of APIs for managing connections. You can also handle data configurations through YAML files using the Command Line Interface (CLI).

To accommodate larger teams in a workspace, Airbyte Enterprise enables multiple users to utilize the platform with Single Sign-On (SSO) and role-based access control (RBAC). You gain direct access to Airbyte’s team of data experts for personalized assistance. With this plan, you can store all connector secrets in your company’s secure storage for enhanced security of sensitive information.

  • Airbyte Cloud 

Airbyte Cloud is a scalable and managed solution for data integration. It gives you the flexibility to customize your data syncing preferences based on your specific needs. Whether it is mirroring your source, tracking historical changes, or capturing snapshots at table level, you can control your sync schedules and receive alerts via email and webhooks.

  • Powered by Airbyte

Powered by Airbyte enables you to integrate the Airbyte data integration platform into your own products or applications. You can seamlessly sync your data from multiple sources with the assurance that none of your data is stored or viewable by Airbyte. It offers two versions: Headless and UI. The former version enables you to fully customize the user experience, providing flexibility in integrating data synchronization into the existing Airbyte API interface. The latter version provides you with the “Done-for-you” UI. You can skip the process of building your user interface and authenticate your data with Airbyte’s user-friendly features.

Airbyte’s Key Features

An interesting feature that Airbyte provides is the ability to consolidate data from multiple sources. If you have large datasets spread across several locations or source points, you can bring it all together at your chosen destination under one platform.

This data integration and replication platform has one of the largest data engineering communities, with over 800+ contributors and more than 15,000 members. Every month, 1000+ engineers are engaged to build connectors and expand Airbyte’s comprehensive connector library.

Another reason why Airbyte ranks as one of the top ETL tools is because it provides you with a version-control tool and options to automate your data integration processes. The platform is suited to support structured and unstructured data sources for your datasets.

Pricing

Airbyte offers separate pricing plans for its three services. Under Airbyte Self-Managed, the Airbyte Open-source is a free functionality. However, you need to contact their sales team to set up Enterprise features. You get a 14-day free trial with Airbyte Cloud, after which you must opt for a paid plan. For the Powered by Airbyte service, the prices vary as per your chosen syncing frequency each month.

Airbyte has much more to offer you! Sign up to explore and create a free account with one of the top ETL tools today!

Meltano

Image Source: Meltano

Developed in 2018, Meltano is an open-source platform that offers a user-friendly interface for seamless ETL processes. Meltano is pip-installable and comes with a prepackaged Docker container for swift deployment. This ETL tool powers a million monthly pipeline runs, making it best suited for creating and scheduling data pipelines for businesses of all sizes.

Key Features:

  • The platform offers a wide range of plugins for connecting to over 300 natively supported data sources and targets. 
  • You can also customize connectors through extensile SDKs, ensuring adaptability to your specific needs.
  • Meltano is aligned with the DataOps best practices and has an extensive Meltano Hub community for continuous development and collaboration.

Pricing: Meltano is an open-source tool that comes with free installation.

Matillion

Image Source: Matillion

Matillion is one of the best cloud-native ETL tools specifically crafted for cloud environments. It can operate seamlessly on major cloud-based data platforms like Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse, and Delta Lake on Databricks. The intuitive user interface of Matillion minimizes maintenance and overhead costs by running all data jobs on the cloud environment.

Key Features:

  • Matillion ensures versatility through its innovative and collaborative features supported by Git.
  • It has an extensive library of over 100 pre-built connectors for popular applications and databases.
  • Matillion’s introduces a generative AI feature for data pipelines where you can connect or load vector databases to develop your preferred large language models (LLM).

Pricing: Matillion has three pricing plans: Basic, Advanced, and Enterprise, priced at $2, $2.50, and $2.70 per credit, respectively.

Fivetran

Image Source: Fivetran

One of the prominent cloud-based automated ETL tools, Fivetran, streamlines the process of migrating data from multiple sources to a designated database or data warehouse. The platform supports over 400+ data connectors for various domains and provides continuous data synchronization from the source to the target destination.

Key Features:

  • This ETL tool offers a low-code solution with pre-built data models, enabling you to handle unexpected workloads easily.
  • Fivetran ensures data consistency and integrity throughout the ETL process by swiftly adjusting to APIs and schema changes.
  • There is a 24x7 access to Support Specialists to help you troubleshoot any technical concerns during the migration process.

Pricing: Fivetran comes with a free starter plan, followed by three pricing plans: Starter, Standard, and Enterprise. You only have to pay for the monthly active rows (MAR) that you use.

Stitch

Image Source: Stitch

Stitch is a cloud-based open-source ETL service provider owned by the cloud integration company Talend. The platform is well-known for its security measures and swift data transfer into warehouses without the need for coding.

Key Features:

  • Stitch supports simple data transformation and provides over 130 data connectors. However, it does not support user-defined transformations.
  • It is known for its data governance measures, having HIPAA, GDPR, and CCPA compliance certifications.
  • The open-source version of Stitch has limitations in handling large volumes of data. Hence, you can subscribe to an enterprise version tailored for vast datasets.

Pricing: Stitch offers three monthly pricing plans: Standard, Advanced, and Premium, priced at $100, $1,250, and $2,500, respectively.

Apache Airflow

Image Source: Apache Airflow

Apache Airflow is an open-source framework that has been primarily designed as an orchestrator. The platform provides integrations with some of the best ETL tools through custom logic.

Key Features:

  • Airflow allows you to build and run workflows that are represented through Directed Acyclic Graph (DAG). DAG represents a collection of individual tasks in a proper Python-script structure. It is created to facilitate the simplified management of each task in your workflow.
  • You can deploy Airflow on both on-premise and cloud servers, gaining the flexibility to choose the infrastructure of your choice.
  • You will find several in-built connectors for many industry-standard sources and destinations in Airflow. The platform even allows you to create custom plugins for databases that are not natively supported.

Pricing: Airflow is a free and open-source tool licensed under Apache License 2.0.

Integrate.io

Image Source: Integrate.io

Integrate.io is a low-coding data integration platform offering comprehensive solutions for ETL processes, API generation, and data insights. With a rich set of features, it enables you to swiftly create and manage secure automated pipelines, making it one of the well-known ETL tools available.

Key Features:

  • The platform supports over 100 major SaaS application packages and data repositories, covering a wide range of data sources.
  • You can tailor the data integration process on Integrate.io to suit your specific requirements through its extensive expression language, sophisticated API, and webhooks.
  • Integrate.io has a Field Level Encryption layer that enables encryption and decryption of individual data fields using unique encryption keys.

Pricing: Integrate.io offers you three pricing plans: Starter, Professional, and Enterprise. The first plan is priced at $15,000 per year, the second at $25,000 per year, and the last can be customized per your needs.

Oracle Data Integrator

Image Source: Oracle Data Integrator

Oracle Data Integrator provides a comprehensive and unified solution for the configuration, deployment, and management of data warehouses. The platform is well-known for ETL processes, facilitating seamless integration and consolidating diverse data sources.

Key Features:

  • Oracle Data Integrator supports real-time event processing through its advanced Change Data Capture (CDC) ability. It allows the processing of databases in real-time and keeps your target system up-to-date.
  • This ETL tool can be integrated with Oracle SOA Suite, a unified service infrastructure component for developing and monitoring service-oriented architecture (SOA). Thus, its interoperability with other components of the Oracle ecosystem enhances your data pipeline.
  • Oracle Data Integrator employs Knowledge Modules that provide pre-built templates and configurations for data integration tasks, boosting productivity and modularity.

Pricing: Oracle Data Integrator comes with a Cloud Service as well as a Cloud Service BYOL plan. Both have a unit price, and you need to pay for per unit of OCPU consumed every hour.

IBM Infosphere Datastage

Image Source: IBM InfoSphere DataStage

InfoSphere DataStage, as a part of the IBM InfoSphere Information Server, is one of the best data integration tools. It leverages parallel processing and enterprise connectivity, ensuring scalability and performance for organizations dealing with huge datasets.

Key Features:

  • InfoSphere DataStage provides a graphical interface for designing data flows. This makes it user-friendly and accessible to extract data from diverse sources.
  • The tool enables the development of jobs that interact with big data sources, including accessing files on the Hadoop Distributed File System (HDFS) and augmenting data with Hadoop-based analytics.
  • InfoSphere DataStage supports real-time data integration, enhancing the responsiveness in workflows.

Pricing: IBM DataStage offers four plans. The IBM DataStage as a Service begins at $1.75 per Capacity Unit-Hour. The other plans include IBM DataStage On-premises, Enterprise, and Enterprise Plus.

AWS Glue

Image Source: AWS Glue

AWS Glue is a comprehensive serverless data integration service provided by AWS. It allows you to orchestrate your ETL jobs by leveraging other AWS services to move your datasets into data warehouses and generate output streams.

Key Features:

  • The platform facilitates connection to over 70 diverse data sources. It allows you to manage data in a centralized data catalog, making it easier to access data from multiple sources.
  • AWS Glue operates in a serverless environment, giving you a choice to conduct ETL processes using either the Spark or the Ray engine.
  • Another way of running data integration processes on AWS Glue is through table definitions in the Data Catalog. Here, the ETL jobs consist of scripts that contain programming logic necessary for transforming your data. You can also provide your custom scripts through the AWS Glue console.

Pricing: There are four AWS Glue jobs: Apache Spark, Apache Spark Streaming, Python Shell, and Ray (Preview). Each has pricing beginning from $0.44 per data processing units (DPU) per hour. Pricing also varies according to the region you are operating in.

Azure Data Factory

Image Source: Azure Data Factory

Azure Data Factory is a fully managed, serverless data integration service offered by Microsoft Azure. It is one of the best ETL tools for creating data pipelines and managing transformations without extensive coding.

Key Features:

  • Azure Data Factory comes with over 90 built-in connectors that are all maintenance-free.
  • The platform supports easy rehosting of SQL Server Integration Services (SSIS) to build ETL pipelines. It also includes built-in Git integration and facilitates continuous integration and continuous delivery (CI/CD) practices.
  • Azure Data Factory allows you to leverage the full capacity of underlying network bandwidth, supporting up to 5 Gbps throughput.

Pricing: Azure Data Factory has two versions: V1 and V2. V1 has separate pricing options for low and high frequencies. The number of your activity runs and hours required to execute the integration runtime determines your V2 pricing.

Final Takeaways

ETL procedures form the foundation for data analytics and machine learning processes. It is a crucial step in preparing raw data for storage and analytics. You can later use this stored data to generate reports and dashboards in business intelligence tools or create predictive models for making timely forecasts. 

ETL tools facilitate you to conduct advanced analytics, enhance data operations, and improve end-user experience. Hence, it is of utmost importance that you select the best ETL tool that is well-suited for your business to make the right strategic decisions. 

We recommended using Airbyte, as it is one of the top ETL tools today, trusted by over 4000 companies. The platform is equipped to handle complex data challenges while providing you with a swift and seamless experience. To explore its capabilities firsthand, you can try it for free! Additionally, to deploy Airbyte throughout the enterprise, you can contact their sales team.

Frequently Asked Questions

  • Are ETL tools easy to learn?
    Yes, most ETL tools have intuitive features that are easy to learn. Airbyte is one of the most accessible ETL tools because of its simple UI and no-coding feature.
  • How do I load data into ETL?
    Simply register with the ETL tool of your choosing. In ETL tools like Airbyte, you only have to configure your source and destination in two quick steps.
  • Can users with minimal technical knowledge use ETL tools?
    Yes. Building a custom ETL tool requires technical expertise, but you can use other ETL tools that do not need you to write a single line of code. Open-source ETL tools have a vast community of developers that can come to your aid in case you face difficulties. If you are familiar with cloud-native platforms, you can easily familiarize yourself with cloud-based ETL tools.

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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:

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    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.