Top ETL Tools 2022

    In this data-driven age, enterprises leverage data to analyze products, services, employees, customers, and more, on a large scale. ETL (extract, transform, load) tools enable highly scaled sharing of information by bringing all of an organization’s data together and avoiding data silos.

    What are ETL Tools?

    Extract, transform, and load a data management process for collecting data from multiple sources to support discovery, analysis, reporting, and decision-making. ETL tools are instruments that automate the process of turning raw data into information that can deliver actionable business intelligence. They extract data from underlying sources, transform data to satisfy the data models enterprise repositories, and load data into its target destination.

    “Transform” is perhaps the most important part of ETL: Making sure all data is in the proper type and format for its intended use. The term has been around since the 1970s and typically has referred to data warehousing, but now is also used to power Big Data analytics applications.

    Also read: Best Big Data Tools & Software for Analytics

    Choosing ETL Tools

    There are a variety of factors that determine which ETL tool suits your needs best. Let’s explore some of the most relevant ones.

    Business goals

    Your business goals are the most vital consideration when choosing ETL tools. The data integration needs of the business require ETL tools that ensure speed, flexibility, and effectiveness.

    Use case

    Client use cases determine what kind of ETL tools to implement. For instance, where the implementation covers different use cases or involves different cloud options, modern ETL approaches trump older ETL approaches.


    A good ETL tool should not only be flexible enough to read and write data regardless of location but also enable users to switch providers without long delays.


    An organization’s scope and frequency of integration efforts determine the kind of ETL tools they require. Organizations with more intensive tasks may require more integrations daily. They should ensure the tools they choose satisfy their integration needs.

    Data sources

    Data sources determine the type of ETL tools to be implemented, as some organizations may need to work with only structured data while others may have to consider both structured and unstructured data or specific data types.


    Considering your budget as you research prospective ETL solutions is crucial, as costs can rise considerably with ETL tools that need lots of data mapping and manual coding. Knowing not only the ETL tool but what supporting activities you will be required to pay for is key to ensuring you get the right ETL tool working optimally.

    Top ETL Tools

    Here are our picks for the top ETL tools based on our survey and analysis of the market.

    Oracle Data Integrator

    Oracle Data Integrator (ODI) is a comprehensive data integration platform that encompasses data integration requirements such as high-volume, high-performance batch loads, SOA-enabled data services, and event-driven trickle-feed integration processes. It is part of Oracle’s data integration suite of solutions for data quality, cloud data, metadata management, and big data preparation.

    Oracle Data Integrator offers support for both unstructured and structured data and is available as both an enterprise ETL tool and a cloud-based ETL tool.

    Key Differentiators

    • High-Performance Data Transformation: ODI offers high-performance data transformation through powerful ETL that minimizes the performance impact on source systems. It also lowers cost by using the power of the database system CPU and memory to carry out transformations instead of using independent ETL transformation servers.
    • Out-of-the-Box Integrations: The Enterprise Edition of ODI provides a comprehensive selection of prebuilt connectors. Its modular design offers developers greater flexibility when connecting diverse systems.
    • Heterogeneous System Support: ODI offers heterogeneous system support with integrations for big data, popular databases and other technologies.

    Cons: ODI may require advanced IT skills for data manipulation, as implementation may prove to be complex. Licensing also may prove to be expensive for smaller organizations and teams. Furthermore, it lacks the drag-and-drop features characteristic of other ETL tools.

    Azure Data Factory

    Azure Data Factory simplifies hybrid data integration through a serverless and fully managed integration service that allows users to integrate all their data.

    The service provides more than 90 built-in connectors at no extra cost and allows users to simply construct not only ETL processes but also ELT processes, transforming the data in the data warehouse. These processes can be constructed through coding or through an intuitive code-free environment. The tool also improves overall efficiency through autonomous ETL processes and improved insights across teams.

    Key Differentiators

    • Code-Free Data Flows: Azure Data Factory offers a data integration and transformation layer that accelerates data transformation across users’ digital transformation initiatives. Users can prepare data, build ETL and ELT processes, and orchestrate and monitor pipelines code-free. Intelligent intent-driven mapping automates copy activities to transform faster.
    • Built-in Connectors: Azure Data Factory provides one pay-as-you-go service to save users from the challenges of cost, time, and the number of solutions associated with ingesting data from multiple and heterogeneous sources. It offers over 90 built-in connectors and underlying network bandwidth of up to 5 Gbps throughput.
    • Modernize SSIS in a Few Clicks: Data Factory enables organizations to rehost and extend SSIS in a handful of clicks.

    Con: The tool supports some data hosted outside of Azure, but it primarily focuses on building integration pipelines connecting to Azure and other Microsoft resources in general. This is a limitation for users running most of their workloads outside of Azure.

    Talend Open Studio

    Talend helps organizations understand the data they have, where it is, and its usage by providing them with the means to measure the health of their data and evaluate how much their data supports their business objectives.

    Talend Open Studio is a powerful open-source ETL tool designed to enable users to extract, standardize and transform datasets into a consistent format for loading into third-party applications. Through its numerous built-in business intelligence tools, it can provide value to direct marketers.

    Key Differentiators

    • Graphical Conversion Tools: Talend’s graphical user interface (GUI) enables users to easily map data between source and destination areas by selecting the required components from the palette and placing them into the workspace.
    • Metadata Repository: Users can reuse and repurpose work through a metadata repository to improve both efficiency and productivity over time.
    • Database SCD Tools: Tracking slowly changing dimensions (SCD) can be helpful for keeping a record of historical changes within an enterprise. For databases such as MSSQL, MySQL, Oracle, DB2, Teradata, Sybase, and more, this feature is built-in.

    Cons: Installation and configuration can take a significant amount of time due to the modular nature of the tool. Additionally, to realize its full benefits, users may be required to upgrade to the paid version.

    Informatica PowerCenter

    Informatica is a data-driven company passionate about creating and delivering solutions that expedite data innovations. PowerCenter is Informatica’s data integration product, which is a metadata-driven platform with the goals of improving the collaboration between business and IT teams and streamlining data pipelines.

    Informatica enables enterprise-class ETL for on-premises data integration while providing top-class ETL, ELT, and elastic Spark-based data processing for every cloud data integration needed through artificial intelligence (AI)-powered cloud-native data integration.

    Key Differentiators

    • PowerCenter Integration Service: PowerCenter Integration Service assists to read and manage the integration’s workflow, which in turn delivers multiple integrations according to the needs of the organization.
    • Optimization Engine: Informatica’s Optimization Engine sends users’ data processing tasks to the most cost-effective destination, whether traditional ETL, Spark serverless processing, cloud ecosystem pushdown, or cloud data warehouse pushdown. This ensures the right processing is chosen for the right job, ensuring controlled and optimized costs.
    • Advanced Data Transformation: Informatica PowerCenter offers advanced data transformation to help unlock the value of non-relational data through exhaustive parsing of JSON, PDF, XML, Internet of Things (IoT), machine data, and more.

    Con: For higher volumes, the computational resource requirement may be high.

    Microsoft SSIS

    Microsoft SQL Server Integration Services (SSIS) is a platform for developing enterprise-grade data transformation and integration solutions to solve complex business problems.

    Integration Services can be used to handle these problems by downloading or copying files, loading data warehouses, managing SQL data and objects, and cleansing and mining data. SSIS can extract data from XML files, Flat files, SQL databases, and more. Through a GUI, users can build packages and perform integrations and transformations.

    Key Differentiators

    • Transformations: SSIS offers a rich set of transformations such as business intelligence (BI), row, rowset, split and join, auditing, and custom transformations.
    • SSIS Designer: SSIS Designer is a graphical tool that can be used to build and maintain Integration Service packages. Users can use it to construct the control flow and data flows in a package as well as to add event handlers to packages and their objects.
    • Built-in Data Connectors: SSIS supports diverse built-in data connectors that enable users to establish connections with data sources through connection managers.

    Cons: SSIS has high CPU memory usage and performance issues with bulk data workloads. The tool also requires technical expertise, as the manual deployment process can be complex.

    AWS Glue

    AWS Glue is a serverless data integration service that simplifies the discovery, preparation, and combination of data for analytics, application development, and machine learning. It possesses the data integration capabilities that enterprises require to analyze their data and put it to use in the shortest time possible. ETL developers and data engineers can visually build, execute, and monitor ETL workflows through AWS Glue Studio.

    Key Differentiators

    • ETL Jobs at Scale: AWS Glue enables users to simply run and manage ETL jobs at scale, as it automates a significant part of the effort required for data integration.
    • ETL Jobs Without Coding: Through AWS Glue Studio, users can visually create, execute, and monitor AWS ETL jobs. They can create ETL jobs that move and transform data through a drag-and-drop editor, and AWS Glue will automatically generate the code.
    • Event-Driven ETL Pipelines: AWS Glue enables users to build event-driven ETL pipelines, as Glue can run ETL jobs as new data arrives.

    Con: Since AWS Glue is made for AWS console and its products, it makes it difficult to use for other technologies. is a data integration solution and ETL provider that offers customers all the tools they require to customize their data flows and deliver better data pipelines for improved insights and customer relationships. This ETL service is compatible with data lakes and connects with most major data warehouses, proving that it is one of the most flexible ETL tools available.

    Key Differentiators

    • Rapid, Low-Code Implementation: enables users to transform their data with little to no code, offering them the flexibility that alleviates the complexities of dependence on extensive coding or manual data transformations.
    • Reverse ETL:’s low-code Reverse ETL platform enables users to convert their data warehouses into the heartbeats of their organizations by providing actionable data across users’ teams. Users can focus less on data preparation and more on actionable insights.
    • Single Source of Truth: Users have the ability to combine their data from all of their sources and send them a single destination with A single source of truth for customer data enables organizations to save time, optimize their insights, and improve their market opportunities.

    Con: The tool does not support on-premises solutions.

    Hevo Data

    Hevo Data is a no-code data pipeline that simplifies the ETL process and enables users to load data from any data source, including software-as-a-service (SaaS) applications, databases, streaming services, cloud storage, and more.

    Hevo offers over 150 data sources, with more than 40 of them available for free. The tool also enriches and transforms data into a format ready for analysis without users writing a single line of code.

    Key Differentiators

    • Near Real-Time Replication: Near real-time replication is available to users of all plans. For database sources, it is available via pipeline prioritization, while for SaaS sources, it is dependent on API (application programming interface) call limits.
    • Built-in Transformations: Hevo allows users to format their data on the fly with its drag-and-drop preload transformations and to generate analysis-ready data in their warehouses using post-load transformation.
    • Reliability at Scale: Hevo provides top-class fault-tolerant architecture with the ability to scale with low latency and zero data loss.

    Con: Some users report that Hevo is slightly complex, especially concerning operational support.

    Comparing the Top ETL Tools

    MappingDrag and DropReportingAuditingAutomation
    Oracle Data IntegratorX
    Azure Data Factory
    Talend Open Studio
    Informatica PowerCenter
    Microsoft SSISX
    AWS Glue
    Hevo DataX

    Read next: Top Data Quality Tools & Software

    Collins Ayuya
    Collins Ayuya
    Collins Ayuya is pursuing his Master's in Computer Science and is passionate about technology. He loves sharing his experience in Artificial Intelligence, Telecommunications, IT, and emerging technologies through his writing. He is passionate about startups, innovation, new technology, and developing new products as he is also a startup founder. Collins enjoys doing pencil and graphite art and is also a sportsman, and gamer during his downtime.

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