Extract, transform, load tools, or ETL tools, are a core part of modern data architecture. Indeed, ETL solutions have been around for at least 40 years. Without them, it's nearly impossible to reliably bring data from all your other business tools together in a central database.
ETL, or sometimes ELT (extract, load, transform) nowadays, is therefore the heart of data integration.
In this article, we'll present a list of what we consider to be the best ETL tools on today's market, and discuss what to look for when choosing one that will be relevant for your organization in 2024 and beyond.
We'll start with buying considerations. Click to skip straight down to the tools.
What to Consider When Choosing an ETL/ELT Tool
At a glance, a modern, future-proof ETL/ELT tool should:
- Be able to connect a wide range of sources and destinations.
- Provide a centralized overview for governing all integrations.
- Offer inbuild data quality and logging solutions.
- Do transformations (or integrate with a dedicated transformation tool).
- Be highly customizable.
- Be fully scalable.
- Be transparently priced.
- Be Soc 2 Type II certified, among other security criteria for data tools.
- Bonus: Offer other integration types aside from ETL or ELT.
- Bonus: Have a user-friendly interface.
Any tool that meets these criteria is worth investigating. Let's narrow it down even further by asking the following questions.
Click the arrows to unravel details.
What Data Integration Types Do You Need Now and in the Future?
The market is currently filled with specialized integration tools for every use case—event-based messaging tools, ETL and ELT tools, database replication tools, and reverse ETL tools.
Traditionally, having these specialized tools for different use cases has made sense. Now, this is changing.
To ensure that you don't have to keep adopting more specialized tools in the future that support new integration types, it makes the most sense to have a single, end-to-end integration tool that supports all key integration types.
As data and analytics become a bigger and bigger part of everyday work, it will become less and less practical to have several niche tools.
For example, when data teams want to integrate data from multiple eshops in a data warehouse, they won't want to use one tool for ETL or ELT, another for reverse ETL, and another to fire events in between apps and a CRM for the sales team. It will be much easier if they can just use a single tool to manage all integrations, regardless of where they sit in a data infrastructure.
How Willing Is the Vendor to Develop New Connectors and How Fast?
Future-proofing is not just about enabling multiple integration types; it's also about willingness to develop new connectors quickly. A service may have all the connectors you need today, but what if you start using services they don't support?
Will you have to build and maintain new connectors yourself? Will they build it for you, but in six months? You want a vendor who is willing to build what you need, when you need it.
How Customizable/Flexible Is the Data Integration Solution?
Out-of-the-box solutions are absolutely essential for any organization that doesn't want to accept the Sisyphean task of building and maintaining its own data integration solutions. But, let's face it—organizations are going to want stuff customized. How willing is the vendor's support team to work with you to implement custom transformations or connect custom datasets, including from proprietary tools?
Read some online reviews or, better yet, sign up for a free trial and talk to support directly.
Does the Tool Enable Direct Connection of Apps to BI Tools?
If you're searching for an ETL tool, you're likely on a data team. But what about the people on your business teams who want to connect, let's say, Facebook Ads data directly to Looker Studio?
More and more, line-of-business professionals will want to make direct connections like this, so that they can analyze data independently for ad hoc insights.
In addition to ETL, reverse ETL, database replication, etc. for data teams, it would be well to choose a data integration tool that lets non-technical teams serve themselves.
Tools that tick this box will inevitably have a no-code interface and be easy to use.
What Data Quality Solutions and Observability Mechanisms Does the Tool Offer?
Data quality is a consistent roadblock to the success of data initiatives, especially AI initiatives. And it will only become a bigger problem as we continue to collect more and more data.
To handle this challenge, most organizations that take data quality seriously will need a dedicated data quality solution on top of their database(s). Still, ETL and ELT tools (ETL tools in particular) can do much to reduce data quality challenges by solving essential quality problems right at the extraction level.
Format harmonization (which only ETL tools can do), anomaly detection, rule-based filtering, detailed error notifications—these are some of the data quality features that will save processing costs and troubleshooting in downstream systems.
Observability goes hand in hand with data quality because it helps streamline problem-solving processes and, in general, makes managing data easier. Make sure you choose a data integration tool that gives you access to detailed logs and lets you manage all data connections via a central view.
Top ETL Software for 2024 and Beyond
Every software here does more than just ETL or ELT and—depending on the needs of a given organization—can be an invaluable component of a future-ready data stack.
Here is an overview of which tools offer which integration types.
Now, let's dive into the details.
1. Dataddo
Dataddo is a fully managed, no-code data integration platform that connects cloud-based applications, BI tools, data warehouses, and data lakes. The platform offers ETL/ELT, reverse ETL, and data replication functionality, as well as an extensive portfolio of connectors, and a full suite of inbuilt data quality mechanisms.
Key features:
- Pricing: Fixed (pipeline/flow-based). Free plan available (for direct app -> BI tool connections).
- Supported integration types:
- ETL/ELT
- Database replication
- Reverse ETL (app -> app)
- Event-based integration (app -> app)
- Apps -> BI tools (end-to-end)
- Headless data integration
- Data quality features: Transformations, rule-based filter, monitoring, detailed logs (thanks to Dataddo connector).
- Encryption: In-transit and at-rest.
- Time to add new connectors: 10 business days.
- Security: SOC 2 Type II certified, ISO 27001 compliant.
2. Hevo
Hevo is a complex cloud data integration platform specializing in ETL to a select number of popular data warehouses. Additionally, it provides capabilities for database replication and supports reverse ETL to Salesforce and HubSpot CRMs only.
Key features:
- Pricing: Variable (event-based). Free plan available.
- Supported integration types:
- ETL/ELT
- Reverse ETL (only to HubSpot and Salesforce)
- Database replication
- Data quality features: Transformations, monitoring. Logs via Amazon CloudWatch Logs.
- Encryption: In-transit and at-rest.
- Time to add new connectors: Unknown.
- Security: SOC 2 Type II certified, ISO 27001 compliant.
For additional info, see a detailed comparison of Hevo and Dataddo.
3. Integrate
Integrate is tailored for ecommerce companies, and therefore features a no-code interface, but also offers powerful tools for data specialists. It is an end-to-end data integration platform, yet it lacks direct application-to-BI tool connections—a common requirement for ecommerce professionals who need ad hoc insights.
Key features:
- Pricing: Fixed (based on tiers & connectors used).
- Supported integration types:
- ETL/ELT
- Reverse ETL (app -> app)
- Database replication
- Headless data integration
- Data quality features: Transformations.
- Encryption: In-transit and at-rest (premium feature).
- Time to add new connectors: Unknown.
- Security: SOC 2 Type II certified, ISO 27001 compliant.
4. Fivetran
Fivetran is a cloud-based integration solution with a key distinction from the solutions above—it focuses only on ELT and data replication (no ETL). For transformations, it integrates closely with dbt—a transformation tool that sits on top of warehouses. It's therefore suitable for companies that need to get all their data to a warehouse fast, and that have the engineering resources to clean it once it gets there.
Key features:
- Pricing: Variable (row-based). Free plan available.
- Supported integration types:
- ELT
- Database replication
- Headless data integration
- Data quality features: Notifications, a "history" mode for analyzing how data has evolved, log data kept for 1 week.
- Encryption: In-transit and at-rest.
- Time to add new connectors: Must be coded by users.
- Security: SOC 2 Type II, ISO 27001 certified and/or compliant.
For additional info, see a detailed comparison of Fivetran and Dataddo.
5. Airbyte
Like Fivetran, Airbyte specializes in ELT. Unlike the other data integration tools in this list, Airbyte is open-source, and therefore designed for data engineers and data scientists. However, there is also a managed version called Airbyte Cloud, which is more expensive.
Key features:
- Pricing: Variable (credit-based). Free plan available.
- Supported integration types:
- ELT
- Database replication
- Headless data integration
- Data quaity features: Basic transformations (thanks to integration with dbt), notifications, logs.
- Encryption: In-transit, depending on connector.
- Time to add new connectors: Must be coded by users.
- Security: SOC 2 Type II, ISO 27001 certified and/or compliant.
For additional info, see a detailed comparison of Airbyte and Dataddo.
6. Informatica
Informatica's Intelligent Data Management Cloud is a heavy-duty, end-to-end enterprise data integration solution that supports a diverse range of data integration use cases. It can do just about anything, except connect SaaS apps directly to BI tools (at least not out of the box).
Key features:
- Pricing: Various models (consumption-based).
- Supported integration types:
- ETL/ELT
- Database replication
- Reverse ETL (app -> app)
- Event-based integration (app -> app)
- Headless data integration
- Data quaity features: Transformations, profiling, pre-built rules, notifications, logs.
- Encryption: In-transit and at-rest.
- Time to add new connectors: Informatica offers a DIY connector builder tool.
- Security: SOC 2 Type II, ISO 27001 certified and/or compliant.
Summing Up: Choosing an ELT Solution
If you've read this far, it's a given that you need an ELT tool to centralize all your organization's data. But it's also a given that you'll eventually need a tool that supports other types of integration, like data replication and reverse ETL. You'll therefore want to pick an ETL tool that's capable of "growing with you"; a tool with flexible integration functionalities, a large portfolio of connectors, and whose engineers are willing to take requests for new connectors and functionalities.
For best effect, you'll also want it to have inbuilt data quality mechanisms that help eliminate as many problems as possible before the data flows downstream.
Finally, no matter how great an ETL tool really is, it's no good in the long run if you can't afford it. So, get one with predictable pricing!
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