Top ETL Tools

Top 10 ETL Tools for Data Integration

Top 8 Data Replication Tools to follow in 2024

March 11, 2024
Selecting the right data replication tool is crucial for organizations aiming to optimize data usage and enhance productivity by ensuring seamless availability, reliability, and operational efficiency.

This article defines data replication and why it is important for businesses and curates the top-tier data replication tools you can use for maximum output. With the proper replication tool, you can shape the trajectory of efficient data utilization and organizational success in a dynamic digital era.

What is Data Replication?

Database replication is a pivotal function in data management, involving the movement of data from a primary database to one or more replica databases. This replication process can occur in real-time, continuously monitoring changes in the primary database. Alternatively, it can be executed periodically or as one-time batch processes.

The primary goal of database replication is to enhance data availability, which is critical for improving analytics capabilities and ensuring effective disaster recovery. By replicating data across multiple databases, organizations can minimize downtime and maintain business continuity in the event of system failures or disruptions.

Importance of Data Replication in Modern Business

In contemporary business operations, data replication stands as a cornerstone for ensuring continuity, security, and accessibility of vital data assets. This process involves duplicating and synchronizing data across multiple databases or storage systems, bolstering data availability and integrity amidst evolving business needs and technological advancements.

Data replication plays a pivotal role in modern business for several compelling reasons:

  • Enhanced Data Availability: Replicating data across distributed environments ensures continuous access to critical information, fostering agility and responsiveness in decision-making processes.
  • Business Continuity and Disaster Recovery: Maintaining synchronized copies of data enables swift recovery from unexpected disruptions, safeguarding business operations and mitigating potential losses.
  • Improved Performance and Scalability: Distributing data processing loads enhances system performance and scalability, ensuring optimal performance even during peak usage periods.
  • Data Distribution and Collaboration: Real-time data replication facilitates seamless distribution and collaboration across diverse teams, fostering innovation and informed decision-making.
  • Regulatory Compliance and Data Governance: Adhering to data replication best practices helps organizations demonstrate compliance with data protection regulations, mitigating legal and regulatory risks associated with data breaches.

In summary, data replication serves as a vital component of modern data management strategies, empowering organizations to optimize data utilization, mitigate risks, and drive innovation in today's competitive business landscape.

Top Data Replication Tools

Data Replication is an important process for maximizing analytics output. Choosing the right data replication tool is even more important. Here are some of the best data replication tools to consider.

#1 Airbyte

Airbyte Landing Page

Airbyte boasts a robust catalog of 300+ pre-built connectors, supporting both structured and unstructured data for data replication. Beyond connector flexibility, Airbyte streamlines data replications with automation, version control, and monitoring. It also seamlessly integrates with orchestration and transformation tools such as Airflow, Prefect, Dagster, dbt, and more.

Key Features

  • Airbyte’s no-code connector builder allows you to create custom connectors in 10 minutes.
  • Airbyte employs a log-based CDC for the rapid detection of data changes and efficient replication with minimal resource usage.
  • Airbyte supports structured and unstructured data sources, catering to various data and AI use cases.
  • Airbyte prioritizes end-to-end security by offering reliable connection methods like SSL/TLS and SSH tunnels.
  • You can automate text embeddings in the Extract, Load, Transform (ELT) process to capture the meaning of unstructured fields within your data.

#2 Fivetran

Fivetran is an automated data integration platform that excels in synchronizing data from various sources, including cloud applications, databases, and logs. With Fivetran, you can move large volumes of data efficiently from your database with minimal latency. It also ensures seamless data synchronization, even when schemas and APIs evolve.

Key Features

  • With Fivetran, you can access over 400 ready-to-use source connectors without writing any code.
  • With log-based CDC, You can quickly identify data changes and replicate them to your destination, all with a straightforward setup and minimal resource usage.
  • Fivetran’s high-volume agent empowers you to replicate huge data volumes in real-time effortlessly. These high-volume agent connectors utilize log-based change data capture to extract information from the source system’s logs, optimizing your replication process.
  • You can enable log-free replication with teleport sync. Fivetran’s Teleport Sync presents a specialized method for database replication, blending snapshot thoroughness with log-based system speed.

#3 Informatica

Informatica offers a comprehensive data integration tool set, catering to diverse needs through on-premises and cloud deployment options. It seamlessly merges advanced hybrid integration and governance capabilities with user-friendly self-service access for various analytical tasks.

Augmented integration is achievable through Informatica’s CLAIRE Engine, a metadata-driven AI engine that harnesses machine learning.

Key Features

  • Real-time replication of changes and metadata from source to target databases over LAN or WAN, using batched or continuous methods.
  • You can replicate data across diverse databases and platforms while ensuring consistency.
  • You can use nonintrusive log-based capture techniques that do not disrupt databases or applications.
  • Employ InitialSync and Informatica Fast Clone for efficient data loading. It includes high-speed Oracle data transfer and data streaming for specific targets like Greenplum and Teradata.
  • Scale the replicable solution to meet your data distribution, migration, and auditing needs.

#4 IBM Informix

IBM Informix is a trusted embeddable database designed for optimal performance in OLTP and Internet of Things (IoT) data handling. Informix stands out for its seamless integration of SQL, NoSQL, time series, and spatial data. Offering reliability, flexibility, user-friendliness, and cost-effectiveness, it caters to everyone from developers to global enterprises.

Key Features

  • Whether on-premise or Cloud, IBM data replication centralizes dispersed data.
  • Ensure high availability and safeguard mission-critical data against disruptions.
  • Enhance your business decision-making with real-time data for up-to-the-minute insights.
  • IBM Data replication operates with minimal impact, exclusively monitoring changes captured in the log.
  • You can use IBM Data Replication to send data changes to support data management, including data warehouses, quality processes, and critical systems.

#5 Qlik Replicate

Qlik Replicate empowers you to expedite data replication, ingestion, and streaming across diverse databases, data warehouses, and big data platforms. With a worldwide user base, Qlik Replicate is tailored for secure and efficient data movement while minimizing operational disruptions.

Key Features

  • Qlik Replicate streamlines data availability management, reducing time and complexity in heterogeneous environments.
  • Centralized monitoring and management facilitate scalability, enabling data movement across many databases.
  • You can handle data replication, synchronization, distribution, and ingestion across major databases, regardless of deployment location.
  • The platform optimizes workloads and provides robust support for business operations, applications, and analytical needs.
  • Qlik Replicate ensures data availability and accessibility, making it a valuable asset for organizations.

#6 Hevo Data

Hevo is a zero-maintenance data pipeline platform that autonomously syncs data from 150+ sources, encompassing SQL, NoSQL, and SaaS applications. Over 100 pre-built integrations are native and tailored to specific source APIs. With the help of Hevo, you can gain control over how data lands in your warehouse by performing on-the-fly actions such as cleaning, formatting, and filtering without impacting load performance.

Key Features

  • Hevo incorporates a streaming architecture that can automatically detect schema changes in incoming data and replicate them to your destination.
  • Monitor pipeline health and gain real-time visibility into your ETL with intuitive dashboards, revealing every pipeline and data flow stats. Utilize alerts and activity logs for enhanced monitoring and observability.
  • With Hevo, you can process and enrich raw data without coding.
  • Hevo offers a top-tier fault-tolerant architecture that scales seamlessly, ensuring zero data loss and low latency.

#7 Dell RecoverPoint

Dell RecoverPoint is a leading data replication solution designed to ensure continuous data protection and disaster recovery for organizations of all sizes. With its advanced features and robust capabilities, RecoverPoint offers peace of mind by safeguarding critical data and minimizing downtime in the event of unforeseen incidents.

Key Features

  • RecoverPoint provides real-time replication of data, enabling organizations to recover to any point in time with minimal data loss.
  • It supports replication across multiple sites, allowing for flexible disaster recovery strategies and business continuity planning.
  • RecoverPoint automates the failover and failback processes, ensuring seamless transitions between primary and secondary data centers.
  • RecoverPoint ensures application-consistent replication, preserving data integrity and consistency across replicated environments.
  • It enables granular recovery of files, folders, and virtual machines, empowering organizations to quickly recover specific data elements as needed.

#8 Carbonite

Carbonite Availability utilizes continuous replication technology to duplicate databases. This process is executed without burdening the primary system or consuming bandwidth. You can install Carbonite on both primary and secondary systems, which mirrors the primary system’s configuration at the secondary location.

In the event of a failure, the secondary system becomes active, and DNS redirection seamlessly directs users. Depending on the system configuration, you may not even discern that they operate on the secondary system.

Key Features

  • With Carbonite’s real-time application at the byte level, you can achieve a Recovery Point Objective (RPO) within seconds.
  • Combat malware and data loss by recovering from corruption and ransomware through the use of “snapshots,” often used to protect data.
  • You can achieve platform independence by seamlessly supporting physical, virtual, or cloud source and target systems.
  • You can implement automatic failover using a server heartbeat monitor.
  • Carbonite’s encryption protects your data during transfer from the source to the destination.

Quick Note- In addition to the established players like IBM InfoSphere and Dell RecoverPoint, emerging tools such as Skyvia, Acronis Cyber Backup, Zerto, Oracle GoldenGate, and NAKIVO Backup & Replication are also gaining attention for their innovative approaches to data replication.

Criteria for Selecting the Right Data Replication Tool

When it comes to selecting the right data replication solution for your organization, several crucial criteria should be considered to ensure optimal performance and compatibility with your specific needs.

Here are some key factors to keep in mind:

  • Performance: Emphasize solutions showcasing high-performance capabilities to adeptly manage data processing tasks.
  • Scalability: Verify the tool's ability to seamlessly expand alongside escalating data requirements while maintaining peak performance levels.
  • Reliability: Prioritize features such as automatic failover mechanisms and robust error handling protocols to mitigate the potential for data loss.
  • Ease of Deployment: Opt for solutions offering intuitive interfaces and streamlined setup procedures to expedite deployment processes.
  • Compatibility: Ensure seamless integration with existing databases and platforms to foster a cohesive operational environment.
  • Security: Prioritize tools fortified with robust encryption protocols and stringent access controls to safeguard sensitive data assets.
  • Cost-effectiveness: Evaluate the comprehensive cost of ownership, encompassing upfront expenses and ongoing operational costs.
  • API Support: Favor tools equipped with extensive API support, facilitating smooth integration with diverse system architectures.
  • Custom Connectors: Seek out solutions featuring tailored connectors for popular databases, streamlining integration efforts.
  • Data Transformation: Confirm the software's capability to execute data transformation tasks proficiently, accommodating diverse data formats seamlessly.
  • Real-time Integration: Prioritize solutions offering real-time data replication capabilities to ensure timely synchronization across systems.
  • Change Data Capture (CDC): Consider solutions equipped with CDC functionality for efficient and precise data replication processes.
  • Monitoring and Management: Select data replication software equipped with advanced monitoring and management functionalities, enabling comprehensive oversight and control.


In today's rapidly changing data management environment, selecting the right data replication solution can significantly impact an organization's ability to maintain data availability, reliability, and efficiency. We've evaluated several leading replication tools in the field, each offering distinct strengths and capabilities.

As technology progresses, these tools become increasingly essential for businesses to leverage data effectively, make well-informed decisions, and maintain a competitive edge in a data-centric world. It's crucial to select a tool that aligns with your specific objectives to optimize your data management strategy.

Looking ahead, the evolution of data replication tools is expected to prioritize advancements in real-time replication, automation, seamless cloud integration, and enhanced security functionalities. Machine learning and AI technologies are poised to play pivotal roles in streamlining replication processes and enhancing overall efficiency.

Moreover, there's a growing emphasis on hybrid and multi-cloud replication solutions to accommodate diverse deployment environments and ensure consistent data availability and reliability across distributed systems. To quench your thirst for knowledge, do checkout this amazing article about Top Data Integration tools.

Data Replication Tools FAQs

  1. What is the importance of selecting the right data replication tool for organizations?
    Selecting the right data replication tool is paramount for organizations to optimize data usage and productivity. These tools ensure seamless availability, reliability, and operational efficiency, vital for maintaining uninterrupted business operations and meeting customer demands in today's digital landscape.
  2. How does data replication enhance data availability and reliability for businesses?
    Data replication plays a pivotal role in enhancing data availability and reliability for businesses. By creating replicas of primary databases, organizations can safeguard mission-critical data and ensure continuous operations, even in the event of primary database failures or system disruptions. This ensures business continuity and minimizes the risk of data loss, supporting efficient decision-making and customer service delivery.
  3. What industries or use cases are these data replication tools best suited for?These data replication tools are best suited for various industries and use cases, including finance, healthcare, retail, manufacturing, and telecommunications. They support critical business functions such as analytics, reporting, customer relationship management, and regulatory compliance, offering flexibility and scalability to meet diverse business needs.
  4. How do data replication tools contribute to disaster recovery and business continuity strategies?
    Data replication tools play a crucial role in disaster recovery and business continuity strategies by ensuring continuous data availability and minimizing downtime in the event of system failures or disasters. These tools enable organizations to quickly failover to replicated systems, ensuring uninterrupted operations and minimizing the impact on business operations and customer service delivery.
  5. What is the difference Between Data Replication vs. Data Backup?
    While data replication involves creating replicas of primary databases to ensure continuous availability and reliability, data backup involves making copies of data for archival purposes, typically stored in separate locations. While data replication ensures real-time synchronization and immediate failover in case of primary system failures, data backup serves as a secondary copy of data for recovery purposes in case of data loss or corruption.
  6. Can data replication tools seamlessly integrate with existing databases and systems?
    Data replication tools are designed to seamlessly integrate with existing databases and systems, ensuring compatibility and interoperability. These tools support a wide range of databases, platforms, and data formats, allowing organizations to replicate data from various sources and destinations without disruption to existing systems or workflows.

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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.