ETL Tool Comparison Matrix: Costs, Features & FAQs

CEO, Portable

ETL Tools: Purpose & Use Cases

Data engineering teams are no stranger to ETL tools.

ETL tools enable analysts to collect and integrate data from multiple data sources into a single big-data warehousing solution.

ETL stands for Extract, Transform, Load: the core functionalities of any ETL tool.

  • Extract: Collects data from different sources.

  • Transform: Converts data into compatible formats.

  • Load: Loads data into the data warehouse.

According to Gartner, "Data integration tools enable organizations to access, integrate, transform, process and move data spanning various endpoints and across any infrastructure to support their data integration use cases."

  • Data integration with ETL requires careful data cleaning and transformation techniques.

  • When dealing with huge volumes of data, you will need automated systems that can quickly load up your ETL pipelines and ensure efficient data collection.

  • The final destination where all your collected data goes would be your data lake or data warehousing solution, which could be located in the cloud or hosted on the premise, depending on your needs.

  • While it is possible to create your own data pipelines manually, it is not a recommended method to follow as it can easily lead to inaccuracies, increased time consumption, and failure of your data project itself.

  • From connecting to various data sources to transforming and loading your data into the data warehouse, ETL tools offer an easy automated framework to carry out your daily data collection and integration operations.

ETL Tool Cost Comparison Matrix


  • Portable is an easy-to-use data integration tool with 350+ data connectors, unlimited data volumes, and hands-on support.

  • Costs: Free plan: Manually Triggered syncs. Paid plan: starts at $200/ month. Custom pricing: Available for business-tailored solutions.

  • Data Integrations: 350+ connectors and white-glove support for custom connectors.


  • An open-source platform for data integration and transformation is Pentaho Kettle, commonly known as Pentaho Data Integration (PDI). It's developed by Hitachi Vantara.

  • Costs: No free plans are listed; only pricing is available on request.

  • Data Integrations: Notable features are dashboards, data modeling, reporting, and analytical capabilities such as Big Data analytics.


  • Fivetran offers data replication functionality where data is collected from applications, databases, events, and more and integrated into a data warehouse.

  • Costs: Pay only for what changed data you use; the unit costs decrease as your data volume increases. Free for low data volumes.

  • Data Integrations: Fivetran claims to provide connectors for every data source possible.

Talend OpenStudio

  • Talend lets you build basic data pipelines with minimal effort. Execute simple ETL operations from a fully managed open-source environment.

  • Costs: $1170 per month per user

  • Data Integrations: 150+ data connectors

Informatica PowerCenter

  • Informatica provides ready-to-use ETL connectors for data transformation. Powercenter is an ETL tool from the Informatica suite that can also be used for data replication, data virtualization, and master data management services.

  • Costs: Available in personal and professional editions, Professional is more expensive and includes premium features. The personal edition is free, with limited options just for personal use.

  • Data Integrations: 400 + pre-built connectors.

Microsoft SQL Server (SSIS)

  • SSIS is a relational database system that can act as an on-premise data platform.

  • Costs: Free versions with limited options are available under the Web and Express editions. Enterprise edition starts at $15123 per 2-core pack - Standard edition at $3945 per core, and Standard edition at $989 per server.

  • Data Integrations: 19+

Oracle Data Integrator

  • Oracle data integrator is a data integration platform that can help deal with high volumes of data, event-driven integration processes, and SOA-enabled data services. It supports both ETL and ELT styles of data integration.

  • Costs: $36400 per processor deployment

  • Data Integrations: 10+

AWS Glue

  • It is a cloud-based, serverless data integration platform where data from multiple sources can be collected and utilized for ML-based analysis.

  • Costs: Custom pricing depending on the ETL tasks required. Ex. $0.44 per DP hour for each Apache spark job.

  • Data Integrations: Custom connections are achieved by PySpark, and Scala methods. Other connectors include documentdb, dynamodb, kinesis. Mongodb, etc.

IBM InfoSphere

  • InfoSphere is a data integration platform that helps with data cleaning, monitoring, transformation, and reporting. It enables high performance via massively parallel processing (MPP)

  • Costs: Pricing starts at $19000 per month for cloud-managed solutions. For more advanced options, the pricing will differ depending on the requirements.

  • Data Integrations: Provides pre-built connectors for Amazon S3, Azure storage, Big data files, BigQuery, Cassandra, file-based connectors, Hive, real-time processing data sources like Kafka, SAP applications, unstructured data, and more.

SAP BusinessObjects

  • SAP Business Objects is a data platform with key features like real-time analysis, high-volume delivery, self-service, and intuitive visualizations.

  • Costs: Approx $1650 per month. Exact pricing could vary depending on the requirements. It follows a monthly pricing model.

  • Data Integrations: Supports several types of connectors such as SAP business connector, SAP java connector, Microsoft .NET connector, and NetWeaver RFC SDK


  • Stitch is a no-code, easy-to-use ETL tool a part of Talend. It has connectors including Google BigQuery, SQL Server, and MySQL. It enables teams to centralize data in a data warehouse.

  • Costs: Starts at $100 per month

  • Data Integrations: 130 + connectors


  • Skyvia is an all-in-one cloud-based data management solution, a data integration, backup, access, and cloud data management tool. It provides options for ETL. ELT, reverse ETL, rest API, and more.

  • Costs: Comes with a free plan for small volumes of data. The basic plan starts at $15 per month, and the standard plan at $79 per month. The more advanced Professional plan starts at $399 per month

  • Data Integrations: Supports all well-known connectors with no code service, most notably Salesforce integration.

Open-Source Data Integration Tools Comparison

ETL ToolOverviewData Connectors
Apache NiFiApache NiFi is a data logistics platform that automates data movement between different data sources.Supports custom connectors
Apache AirflowApache Airflow is a python-based framework that lets you develop, schedule, and monitor batch-oriented dataflows.Supports custom connectors
Apache SparkApache Spark is an analytics engine to facilitate large-scale data processing with pre-built modules for machine learning, SQL, graph processing, and more.Supports custom connectors
Google Cloud Data FusionGoogle Cloud Data Fusion is a code-free, cloud-native data integration tool that lets you build and manage ELT/ETL data pipelines for easy data integration.100+ pre-built data connectors
AlteryxAlteryx is an end-to-end data integration tool that lets you prepare, blend, and analyze data from various sources, including database connections, APIs, flat files, and much more.40+ pre-built data connectors

ETL Functionality to Consider

  1. Handling multiple data sources and schema
  2. Continuous data integration
  3. Operating within a data warehouse
  4. Supporting business intelligence

Handling multiple data sources and schema

  • If you need an ETL tool to work with structured and unstructured data, you need one that can work with multiple data formats and schema.

  • Look into the types of data sources that your data projects will be working on and make sure the tool you choose will be able to connect with these data sources seamlessly.

  • Your ETL tool should be able to handle the multiple data connections and support the various data formats you will be working on.

Continuous data integration

  • For a small company, it may look like a small team of 2 data engineers would be enough to handle the data workflows.

  • But as your company grows, you will notice that you cannot keep increasing your workforce to deal with the increasing data load.

  • You need an efficient automation system to complete the work at a fixed timeline.

  • So, make sure that your ETL tool can support automation and has the flexibility to define custom workflows that fit your data operations.

  • This way, you can achieve continuous data integration with little effort and efficiency.

Operating within a data warehouse

  • The next important criterion would be the supported data warehouse solution.

  • Many different types of data warehousing solutions are available such as Snowflake, BigQuery, Amazon Redshift, and so on.

  • You could choose any of these available data warehousing solutions based on your preferences which could be on-premise/cloud support, cost, tech support, and so on.

Supporting business intelligence needs

  • Features such as dashboards, data visualization and data analysis functionality is essential to make the most of any ETL solution.

  • These features could make your ETL solution more useful and help you get around with faster decision-making. You should also look into the ease of use and the level of the learning curve required to use the tool.

Top 10 Features for ETL Solutions

  1. Cloud-based connectivity
  2. Data warehousing functionality
  3. Ease of use
  4. SaaS data integrations
  5. Scalability without complexity
  6. Data warehouse interoperability
  7. Pricing model that supports business needs
  8. Ready to use data integrations
  9. Custom ETL connectors
  10. Ingest on-premises relational data sources

1. Cloud-based connectivity

  • As companies move away from rigid on-premise office environments, ensure that all your tools can support a remote/hybrid work culture and environment.

  • This means that you need cloud-based connectivity that can enable remote access facilities for all kinds of data operations, be it managing data flow, ingestion, and dealing with real-time data.

2. Data warehousing functionality

  • Data warehousing functionality provides you with a massive data repository where you can integrate all the large volumes of data collected from multiple sources.

  • Aside from costs, you'll need to see if your desired ETL solution can fit the analytical requirements of your company, and how flexible it is to scale up and scale down.

3. Ease of use

  • The very reason why ETL tools gained popularity was because of their ability to reduce coding efforts and automate data flows.

  • So, make it a point to ensure that your data team finds it easy to adapt the ETL tool you choose into their daily workflows.

  • It should not become an extra burden to keep the ETL going. Look for features like no-code, drag and drop, self-service, templates, Excel, intuitive user interface, CSV support, easy imports and exports, and so on.

4. SaaS data Integrations (Salesforce, Rest API, web services, providers)

  • If you are working with SaaS applications, it is important to check whether your ETL tool can work with the rest of your modern data stack.

  • Check for compatibility with Salesforce, Rest API, web services, and any other third-party providers you might be working with and could serve as data channels.

5. Scalability without complexity

  • Traditional data warehousing solutions used to be housed on the premise and required expensive maintenance and time-intensive coding procedures.

  • But with cloud-based data lakes, you can easily collect data from multiple sources, be it IoT, cloud data, or your legacy systems, scale your warehouse size as required, and pay only for the storage you use.

  • You can also employ methods like Change Data Capture (CDC), where only the changes for long-standing data are captured without necessarily having to copy the entire database every time.

6. Data warehouse interoperability

  • Another good advantage that cloud-based data warehouses bring you is that switching over to a different data integration platform is easier as and when required.

  • As data warehouses are the destination of a data flow, it should be easier to change the data warehouse without changing the ETL pipelines.

  • It is highly recommended to use ETL tools that provide interoperability between the major data warehouses like Google BigQuery, Amazon Redshift, Azure Synapse Analytics, and Microsoft SQL Server.

7. Pricing model aligns to business needs

  • Before you evaluate a tool against its pricing model and your requirements, it is first important to finalize your exact requirements.

  • If you are dealing with a large volume of data, calculate how much it would cost to support such volumes of data.

  • Look for the data sources you need to connect and calculate the cost. Similarly, calculate the final pricing for features like replication to get the final price you will be paying.

Related Read: How Much Do ETL Solutions Cost?

8. Ready to use ETL data integration solutions

  • For teams looking for no-code, SaaS data integrations and data warehouses to be implemented quickly, you can look for ready-to-use ETL data integration solutions.

9. Custom ETL connectors

  • When working with highly specific and unstructured data that applies uniquely to your company, you may require several customized ETL solutions, such as custom ETL connectors, ELT adaptations, and more.

  • ETL tools that can help you get around your unique problems with custom solutions at low turnaround time can be a great match in that case.

Related Read: Need a Custom Integration? We've Got You Covered.

10. Ingest on-premises relational data sources

  • Finally, you cannot ignore legacy data and all the valuable info coming from your on-premise relational databases.

  • Moving to the cloud may be a good choice for various reasons, but you still need to account for your existing relational databases and systems like CRM, maintain them, and use them as proper data sources.

  • So, your ETL tool should also be equipped to work in these kinds of use cases and be compatible with your on-premise databases.

Portable for Data Science Teams

  • Having tabulated the top ETL tools in the market and the criteria with which you can evaluate them, we can confidently say that Portable would top the list anytime.

  • Portable has more than 300 ETL connectors and also allows for the development of custom connectors as per your requirement.

  • Portable also has an attractive flat fee pricing model and integrates with all major data warehousing solutions.


1. What is an ETL tool?

An ETL tool is software that integrates data between various data sources. It does so by extracting data from the source system, transforming it into a common format, and loading it into a target system.

2. Why is ETL tool comparison important?

ETL tool comparison helps organizations select the best tool for their specific data integration needs. A thorough comparison of ETL tools allows users to compare features, functionality, and pricing to determine which tool will provide the best value for their business.

3. What are some factors to consider when comparing ETL tools?

When comparing ETL tools, consider factors such as data volume and complexity, ease of use, scalability, performance, integration capabilities, data quality and profiling, support for cloud and on-premise data, and cost.

4. What are some other popular ETL tools on the market?

Some popular ETL tools on the market include Portable, Informatica PowerCenter, Microsoft SQL Server Integration Services (SSIS), Talend Open Studio, Apache NiFi, AWS Glue, IBM InfoSphere DataStage, and Oracle Data Integrator.

5. How do I choose the best ETL tool for my organization?

To choose the best ETL tool for your organization, identify your data integration needs and requirements. Then, evaluate ETL tools based on their features, functionality, scalability, performance, support, and pricing to determine which tool best meets your needs.

6. What is the typical cost of an ETL tool?

The cost of an ETL tool can vary widely depending on the vendor and licensing model. Generally, ETL tools come in three pricing models - a one-time license fee, a subscription, or a per-user license. Additionally, it is highly recommended to consider the upfront cost and ongoing costs such as maintenance and support fees. You can also check out these open-source ETL tools.

7. Can ETL tools be used for real-time data integration?

Yes, ETL tools can be used for real-time data integration. Such tools integrate data in real time, like data from streaming tools, flight tracking software, stock market apps, and so on.

8. How important is support and training for ETL tools?

Support and training can be crucial for ensuring the successful implementation and use of an ETL tool. It's important to evaluate the quality and availability of support and training resources when comparing different ETL tools.

9. Can ETL tools be used with cloud-based data?

Yes, ETL tools can be used with cloud-based data. Look for an ETL tool that allows you to collect data from a cloud service, load it into a data warehouse, and optimize data according to your needs.

10. What is the role of ETL tools in data warehousing?

ETL tools are often used in data warehousing to extract data from various sources, transform it into a common format, and load it into a data warehouse for analysis and reporting. ETL tools can help streamline the data integration process and ensure data quality and consistency.

ETL: The Future of Modern Data Management

  • The future of modern data management is all about deriving useful business intelligence and insights from the huge volumes of data generated daily. From social media to website visits, every single online interaction can contribute greatly to the growing data repositories.

  • And it's not just that, besides the on-premise databases, companies are keen on gathering all kinds of market data, from demographics to IoT data, to gather as many insights as they can to predict what will work, what will not, and to forecast market trends as early as possible.

  • With the advancements in real-time data collection and analysis aided by cloud technology and machine learning, data management has grown beyond its initial scope. It has become an inseparable part of business decision-making.

  • The data stack required for such advanced data operations consists of data sources, cloud-based ETL tools, data warehousing solutions, and advanced analytical applications. As you can see, ETL tools form the backbone of the modern data stack and must be selected carefully for the optimized performance of your data teams.