Skip to main content

Top ETL Tools Shortlist

Here are the top ETL tools that I’ll discuss in this article, with some additional options below:

  1. Oracle Data Integrator - Best for integrations
  2. IBM DataStage - Best for batch data extractions
  3. Microsoft SSIS - Best for combining data
  4. Stitch Data - Best for automated data pipelines
  5. Fivetran - Best data connectivity
  6. SAS Data Management - Best for large enterprises
  7. AWS Data Pipeline - Best drag-and-drop console
  8. Informatica PowerCenter - Best in parsing advanced data formats
  9. AWS Glue - Best serverless ETL tool
  10. Talend Open Studio - Best data handling time
  11. Pentaho Data Integration - Best user-friendly interface
  12. Hadoop - Best for large data storage

Loading data from sources like applications, servers, and network devices into a central repository can be time-consuming and result in costly errors if it isn’t done correctly. Fortunately, there are several reliable ETL tools I have found that organizations can choose from. Here’s a curated list of these platforms that can simplify your data integration needs.

What Are ETL Tools?

ETL tools are apps that extract, transform, and load data from various sources to a central repository like a data warehouse. These programs streamline the process and transform the data into a standardized format.

Built-in connectors in ETL tools minimize the need for coding while allowing quicker implementation. Such a feature enables users to integrate their collected data faster and build their data pipelines efficiently.

Overview of the Top 12 ETL Tools

I’ve compiled the top ETL tools I believe are the best in their specific categories. Check out the list below for more details.

1. Oracle Data Integrator - Best for integrations

Oracle Data Integrator interface
Oracle Data Integrator’s interface editor provides users with one area where they can design their integrations. (Source)

Oracle Data Integrator caters to various data integration needs. It handles everything from large-scale batch loads with high performance to real-time event-driven integration and even SOA-enabled data services.

Why I picked Oracle Data Integrator: I chose Oracle Data Integrator because it offers a range of pre-built connectors that you can use to effortlessly link various databases. It allows you to readily connect with Hadoop, EREPs, CRMs, XML, JSON, LDAP, JDBC, and ODBC, right out of the box.

Oracle Data Integrator Standout Features and Integrations:

Features of Oracle Data Integrator that stood out to me were its active integration platform and its ability to allow developers to create their own mappings through standard business logic. The active integration feature allows data-based, event-based, and service-based data integrations, while the latter lets them produce code for a wide range of data processing technologies.

Integrations are available with data warehouse platforms such as Oracle, Teradata, IBM DB2, Sybase, and Exadata. You can also use it to work with other technologies such as ERPs, XML, and LDAP. All of these integrations are pre-built into the platform.

Pricing: $0.7742 OCPU per hour

Trial: Free trial with paid plans


  • Efficient architecture that uses both source and target servers
  • Automatically detects faulty data before application insertion
  • Supports all platforms, hardware, and operating systems


  • Difficult to learn and requires training
  • User interface can be complex

2. IBM DataStage - Best for batch data extractions

IBM DataStage interface
IBM DataStage lets you collaborate with colleagues while having full access control to your projects. (Source)

IBM DataStage is an ETL tool that allows you to extract, transform, apply business principles, and effortlessly load the data into any desired target. It has a basic version of the software that you can install on-premises and an upgrade that lets you reduce data integration time and expenses.

Why I picked IBM DataStage: I decided on IBM DataStage because it is an integration tool that excels at integrating data from a vast range of enterprise and external sources. I like how it is well-equipped to handle the processing and transformation of large data volumes, thanks to its scalable parallel processing approach.

IBM DataStage Standout Features and Integrations:

Features of IBM DataStage that stood out to me were its data science and automated load balancing. I found that its data science feature allows me to quickly derive insights from my data, while the automated load balancing helps me get the most throughput.

Integrations are available with Amazon S3, Azure, BDFS, BigQuery, and FTP Enterprise. You can also link up and transfer data with data sources like IBM Db2 Warehouse on Cloud and IBM Netezza. All of these integrations are pre-built into the platform.

Pricing: Pricing upon request

Trial: Free trial upon request


  • Workload balancing allows users to run workloads faster
  • Reduced data movement costs
  • Access to AI services


  • Lacks automation for error handling and recovery
  • Editing columns can be tedious

3. Microsoft SSIS - Best for combining data

Microsoft SSIS Solution Explorer
Microsoft SSIS has its Solution Explorer and Properties panes on the right side, so you can easily create data flows. (Source)

Microsoft SQL Server Integration Services, simply called Microsoft SSIS, is an enterprise tool that integrates, transforms, and migrates data within Microsoft’s SQL Server database. It offers integration-related capabilities, such as data analysis, cleansing, and performing ETL processes to update data warehouses.

Why I picked Microsoft SSIS: A major reason why I chose SSIS is because of its versatility, supporting various databases like SQL, DB2, and Oracle. Such flexibility allows users to combine data from different sources, while its graphical interface makes it easier to accomplish data warehousing tasks and transformations.

Microsoft SSIS Standout Features and Integrations:

Features of Microsoft SSIS that stood out to me were its graphical tools and built-in catalog database. The graphical tools make it easy to develop packages, while the SSIS catalog database makes it easy to manage projects, parameters, and environments. I also liked how the platform lets you import data from different sources and transform them the way you want.

Integrations are available for Microsoft SSIS with ADO, DQS, Excel, FTP, HTTP, WMI, and more. These connectors come pre-built into the platform. You can also download additional types of connection managers from their website.

Pricing: $1.913/hour (Enterprise)

Trial: No free trial


  • A flexible ETL and data transformation tool
  • UI is easy to use and configure
  • Provides users with a number of documentation features


  • Lacks integration options with other tools
  • Only supports Microsoft Windows

4. Stitch Data - Best for automated data pipelines

Stitch Data editor
Stitch Data lets you easily customize how you want to view your data. (Source)

Stitch Data is a data integration service that allows you to gather data from more than 130 platforms, services, and applications. The ETL tool lets you centralize all of this data into a single data warehouse, eliminating the need for manual coding.

Why I picked Stitch Data: A huge reason why I decided on Stitch Data is because of its focus on compliance. It provides the necessary tools to analyze and govern data, ensuring that it meets internal and external requirements. It also offers a range of data connectors to sources like databases and SaaS applications, which I’ve found simple to use in setting up automated data pipelines.

Stitch Data Standout Features and Integrations:

Features of Stitch Data include real-time data flow alerts, automated ELT processes, advanced monitoring tools, and data preview capabilities. I found that all these capabilities make Stitch Data an ideal choice for enterprises looking for automated data pipelines.

Integrations for Stitch Data are available with Aurora, MySQL, S3 CSV, Asana, Codat, GitHub, and more. These are all pre-built connectors that come with the platform, allowing for direct connections to many popular data sources.

Pricing: From $100/month

Trial: 14-day free trial


  • Compliance tools ensure data meets all requirements
  • Numerous data connectors are available
  • Open source allows for extended capabilities


  • Requires time and training to learn
  • Navigating the UI can be difficult

5. Fivetran - Best data connectivity

Fivetran data connectors
Fivetran is highly compatible with many data sources through its built-in connectors. (Source)

Fivetran is an automated data integration platform that lets you consolidate and synchronize data from various sources. It has over 160 data connectors that let you simplify the process of moving data between different cloud data platforms.

Why I picked Fivetran: The primary goal of Fivetran is to streamline your data management by offering a set of user-friendly tools. I chose the software because of its relative ease in staying up-to-date with API changes and efficiently retrieving the latest data from databases within minutes.

Fivetran Standout Features and Integrations:

Features of Fivetran that stood out to me were its quickstart data models and automated schema drift handling. The quickstart models let me create tables ready for analytics in a short amount of time. Meanwhile, the handling of schema drift allows the platform to automatically copy any changes made while backfilling applicable data.

Integrations for Fivetran include BigQuery, Redshift, Snowflake, and Azure. These database connectors are all pre-built into the platform and are fully managed as well. It also integrates with many SaaS applications like Airtable and Asana.

Pricing: Pricing upon request

Trial: 14-day free trial


  • Can synchronize with various data sources
  • Offers customizable security features
  • Excellent customer support services


  • Data transformation support is limited
  • Syncing a lot of data can be costly

6. SAS Data Management - Best for large enterprises

SAS Data Management interface
SAS Data Management can show users standard data views that come with profile metrics. (Source)

SAS Data Management is an integration platform designed to connect with data from diverse sources, including cloud platforms, legacy systems, and data lakes. It optimizes workflows by leveraging reusable data management rules, making it ideal for large enterprises with complex data integration processes.

Why I picked SAS Data Management: I chose SAS Data Management because it eliminates the need to build ETL pipelines, allowing you to easily connect with various data sources and transport data to different destinations. I found that its exceptional speed in transferring analytics data from source to warehouse can be useful for large organizations that want to quickly generate valuable reports and visualizations using business intelligence tools.

SAS Data Management Standout Features and Integrations:

Features of SAS Data Management that stood out to me were its ability to let users customize metadata and access audit history. Having the capacity to modify server metadata lets you configure a server according to your requirements. Additionally, being able to view your audit trails can provide operational integrity as well as proof of compliance for major corporations.

Integrations for SAS Data Management include Hadoop, Impala, ODBC, Oracle, PostgreSQL, and more. These connectors are pre-built within the platform and do not require a third-party integration manager.

Pricing: Pricing upon request

Trial: Free demo available


  • Easy connectivity with various data sources
  • Non-technical stakeholders can easily start using it
  • Exceptional analytics data transfer speed


  • Needs third-party drivers to connect with other data sources
  • Can be quite costly

7. AWS Data Pipeline - Best drag-and-drop console

AWS Data Pipeline data connectors
AWS Data Pipeline’s interface showing its connector status with Hadoop. (Source)

AWS Data Pipeline is a managed ETL tool that allows you to transfer data between AWS services and other on-premise resources. It lets you specify the data you want to move, define transformation jobs or queries, and set schedules for performing these transformations.

Why I picked AWS Data Pipeline: What I really like about AWS Data Pipeline is its user-friendly drag-and-drop console that lets you simplify the process of building and managing your pipelines. It also offers fault-tolerant and customization capabilities that ensure smooth data pipeline operations.

AWS Data Pipeline Standout Features and Integrations:

Features of AWS Data Pipeline that stood out to me were its drag-and-drop UI and high fault tolerance. The drag-and-drop capability makes its console quite convenient to use, while the added fault tolerance helps in minimizing the impact of user errors.

Integrations for AWS Data Pipeline include Redshift, SQL, and DynamoDB. Such connectors are available as pre-built options when using the platform.

Pricing: From $0.60/month (for low-frequency activities)

Trial: Free plan available


  • Easy drag-and-drop console that simplifies processes
  • Lets you specify the data you wish to move
  • High fault tolerance and customization capabilities


  • Managing data can be time-consuming
  • Doesn’t support third-party data

8. Informatica PowerCenter - Best in parsing advanced data formats

Informatica PowerCenter interface
Here’s the default Designer interface of Informatica PowerCenter. (Source)

Informatica PowerCenter is the ideal ETL tool for enterprise-level extraction, transformation, and loading. Many of its features are designed for analyzing and parsing advanced data formats.

Why I picked Informatica PowerCenter: I decided to go with Informatica PowerCenter because it lets you readily define the logic for data transformation by establishing mappings between the source and target systems. You can also find essential components such as a server and repository with Informatica PowerCenter, which further streamlines ETL processes.

Informatica PowerCenter Standout Features and Integrations:

Features of Informatica PowerCenter that stood out to me were its role-based tools, which allow you to run ETL processes based on the type of user and its support for grid computing. Having the ability to configure workflows to operate on a grid can be useful when working with complex datasets.

Integrations for Informatica PowerCenter include DB2, Hadoop, JDBC, Azure, and flat files. These connectors are pre-built into the platform as free trial products.

Pricing: Pricing upon request

Trial: 30-day free trial


  • Real-time data integration visibility
  • Accurate and reliable data integrations
  • Access to graphical and no-code tools


  • Debugging workflows can be tricky
  • Lacks scheduling options

9. AWS Glue - Best serverless ETL tool

AWS Glue interface
AWS Glue's new graphical interface will allow you to manage and monitor ETL jobs with ease. (Source)

AWS Glue provides access to a serverless data integration service that simplifies the process of discovering, preparing, handling, and integrating data from different sources. Its visual interface lets you facilitate the loading of data into your data lakes, ensuring data is readily available for analysis.

Why I picked AWS Glue: I chose AWS Glue because of its access to more than 70 data sources while efficiently managing your data using a centralized data catalog. It can even scale up or down depending on the current demands of your organization. These capabilities are the reasons why I believe AWS Glue is the best serverless ETL tool on this list.

AWS Glue Standout Features and Integrations:

Features of AWS Glue that stood out to me were its access to multiple data stores and ability to build complex ETL pipelines. Being able to tap into different data sources makes business intelligence gathering more convenient while developing complex pipelines can result in more in-depth insights.

Integrations for AWS Glue include MySQL, Oracle, Redshift, Amazon S3, and more. All these connectors come pre-built when you choose the platform.

Pricing: Pricing upon request

Trial: Free plan available


  • Failed jobs in AWS Glue can be retrieved
  • Provides filtering for faulty data
  • Easy maintenance and deployment


  • Lacks compatibility with commonly used data sources
  • Not the best choice for real-time ETL jobs

10. Talend Open Studio - Best data handling time

Talend Open Studio interface
Talend Open Studio’s drag-and-drop job designer lets you effortlessly execute ETL processes. (Source)

Talend Open Studio is an open-source ETL tool designed for data integration and handling of big data. This Eclipse-based developer tool and job designer enables you to effortlessly create and execute ETL jobs by simply dragging and dropping components and connecting them.

Why I picked Talend Open Studio: One of the key advantages that made me choose this tool is its ability to automatically generate Java code for jobs. This feature streamlines the development process, allowing users to save time and effort.

Talend Open Studio Standout Features and Integrations:

Features of Talend Open Studio that stood out to me were its graphical mapping tool and access to extensions. Both of these capabilities allow the platform to process data faster than other ETL programs.

Integrations for Talend Open Studio include MSSQL, MySQL, Teradata, DB2, Oracle, and more. These connectors are available as pre-built integrations.

Pricing: Free

Trial: Free


  • GUI platform allows for countless pre-built connectors
  • Can connect with many data sources and technologies
  • No need to write code


  • Monitoring features need improvement
  • Errors can be difficult to debug

11. Pentaho Data Integration - Best user-friendly interface

Pentaho Data Integration interface
Pentaho Data Integration has a simple and clean interface for processing data. (Source)

Pentaho Data Integration (PDI) is a powerful ETL tool that allows you to extract, transform, and load data through a convenient drag-and-drop interface. It lets you efficiently process complex data and generate meaningful reports with valuable insights.

Why I picked Pentaho Data Integration: What I liked about PDI is that it lets you gather data from diverse sources, including SQL databases and OLAP data sources, and process them using a simple and easy interface. I also like how its flexibility extends to its report creation capabilities, supporting various formats such as HTML, Excel, PDF, Text, CSV, and XML.

Pentaho Data Integration Standout Features and Integrations:

Features of Pentaho Data Integration that stood out to me were its crisp UI and report designer. Both of these features make PDI my top choice when it comes to usability.

Integrations for Pentaho Data Integration include MySQL, Oracle, and PostgreSQL. These are all pre-built connectors available when using the platform.

Pricing: Pricing upon request

Trial: 30-day free trial


  • Numerous options for transforming data
  • Has many data mining and extraction tools
  • Excellent OLAP solutions


  • Data integration can be time-consuming
  • Limited options for data visualization

12. Hadoop - Best for large data storage

Reducing overall latency with Hadoop
This new Hadoop feature lets you use a policy that reduces overall latency. (Source)

Hadoop is an open-source ETL tool that lets you store and process data. Instead of relying on a single computer, the software allows you to cluster multiple devices together, enabling fast analysis and storage of huge datasets.

Why I picked Hadoop: I chose Hadoop because it provides access to extensive storage capacity capable of accommodating any type of data. The tool offers immense processing power, allowing you to handle an extraordinary number of concurrent tasks or jobs.

Hadoop Standout Features and Integrations:

Features of Hadoop that stood out to me were its cluster job scheduling and access to common Java libraries. I found that these capabilities allowed the platform to quickly process large datasets, which matches well with its significant data storage capacity.

Integrations for Hadoop include MySQL, PostgreSQL, and Oracle. All these connectors are pre-built into the platform.

Pricing: Free

Trial: Free


  • Can process a huge amount of data simultaneously
  • Can deal with any kind of dataset
  • Highly scalable ETL tool


  • Java framework can be easily exploited
  • Not ideal for smaller datasets

Other ETL Software Options

Besides the ones above, I’ve also shortlisted a few more ETL tools that are worth checking out:

  1. - Best out-of-the-box data connectors
  2. Apache Airflow - Best for data pipeline orchestrations
  3. Azure Data Factory - Best for business and IT-led data analytics
  4. Google Cloud Dataflow - Best for real-time data streaming
  5. Hevo Data - Best bi-directional data pipeline platform
  6. SAP Data Services - Best for data cleansing
  7. Qlik Compose - Best for leveraging proven design patterns

Selection Criteria For ETL Tools

Here’s a short summary of the main selection criteria I used to develop my list of the best ETL tools for this article:

Core Functionality

One of the first things I considered was the core functionalities that every ideal ETL tool should have. The following are the basic capabilities that these products have to provide to tech leaders and organizations before they make it on my list:

  • Ability to connect with a range of data sources
  • Extent of data integration customizability
  • Level of automation with various features
  • Overall performance, reliability, and customer support
  • Level of data security and compliance

Key Features

The best ETL tools can provide the core functionalities that I’ve outlined above by having a certain set of features. These are the key attributes that every good ETL solution should have:

  • Data source and warehouse compatibility: You want to make sure that your chosen ETL tool can readily integrate with your current data sources and warehouses.
  • Ability to scale with demand: Another essential feature that top ETL tools have is their ability to handle the amount of data the organization generates over time.
  • High-quality data: Your chosen ETL tool should provide high-quality data and have the capacity to maintain that standard.


Besides functionality and features, I focused on ETL tools that allowed users to quickly set up integrations with their data sources. Platforms that were easy to use and implement were my top priority.

People Also Ask

The following are the answers to the most frequently asked questions about ETL tools:

Final Thoughts

The market for big data analytics was valued at more than $240 billion in 2021 and is expected to grow significantly in the coming years. Unfortunately, many organizations are struggling with poor data quality, costing an average of $12.9 million each year.

One way you can remedy this issue is by carefully considering the right ETL tool that aligns closely with your company’s needs and objectives. You also have to consider the specific requirements of your organization and its future plans.

Subscribe to The CTO Club newsletter for access to product updates, tool reviews, and exclusive deals.

By Paulo Gardini Miguel

Paulo is the Director of Technology at the rapidly growing media tech company BWZ. Prior to that, he worked as a Software Engineering Manager and then Head Of Technology at Navegg, Latin America’s largest data marketplace, and as Full Stack Engineer at MapLink, which provides geolocation APIs as a service. Paulo draws insight from years of experience serving as an infrastructure architect, team leader, and product developer in rapidly scaling web environments. He’s driven to share his expertise with other technology leaders to help them build great teams, improve performance, optimize resources, and create foundations for scalability.