It is 2026, and there are more ETL tools in the world than one person can mentally map. There are dozens of options with overlapping functionality. Figuring out what fits your goal and stack is like figuring out what a T wrench is when you’re a kid helping your dad in the garage.
So we did what any practical team would: compiled a list of the top ETL tools in 2026. It turned out to be too long, so we broke the list down by category. Then that turned out to be too much, so we only picked the top three in each category.
How do we know they are the best? We’ve seen people using and talking about them in meetups. We’ve used many of them ourselves. We ranked them internally, then applied industry trends to suss out which ETL tools will be considered the most useful in 2026. If you want to know how the categories work, read the first part of this series: ETL tools, a straightforward explainer.
Who is this going to help?
This is for the many people we know doing research in the industry. The ETL ecosystem is massive today. Just look at the MAD ecosystem. Any guide helping navigate the ETL ecosystem in 2026 is welcome.
Our goal is to help you by sharing our favourite tools, which trends matter, and where things are headed. Obviously, it is not an endorsement of every tool, but rather a helpful hint at where to go.
In a rush? Here’s the TL;DR.
A list of top ETL tools, in no particular order:
Tower
AWS Glue
Google Cloud Data Fusion
Microsoft SQL Server Integration Services (SSIS)
Informatica PowerCenter
Azure Data Factory
dltHub
Airbyte
dbt
Oracle Data Integrator
Estuary
Fivetran
Why is Tower in the list?
Tower is the platform where you can deploy your ETL pipelines. Not just an orchestrator, Tower unifies Python-native data flows, manages data lakehouse infrastructure, and gives you observability, scheduling, and control from a single interface.
Why does this matter? Because what breaks most ETL flows is broken orchestration.
Flaky orchestration, a mixed-up plate of spaghetti DAGs, and infra that requires its own infra slow teams down. They prevent innovation and lock you into technical debt that is hard to get out of.
Tower solves that. You can run open-source pipelines (like dltHub, dbt, or Airbyte), transform with Polars or DuckDB, schedule, isolate environments, and keep production safe. You can even have a managed lakehouse if you don’t want to build your own.
You get to bring what you already have, and fill the gaps with Tower - now, instead of worrying about the right tools, you worry about how good your data is. You can be faster than before, more efficient than before, and less anxious about broken pipelines than ever before.
Types of ETL Tools
ETL tools come in many flavors. Some are open-source and composable. Others are proprietary and can lock you into one vendor’s ecosystem. Some are for batch jobs, others for real-time streaming.
Open-source ETL tools
These are flexible, community-driven, and often code-first. Great if you want transparency and control.
Real-time ETL tools
Designed to move and transform data in motion.
Estuary
Kafka Connect
Debezium
Batch ETL and ELT tools
These focus on moving and transforming large amounts of data on a schedule.
Informatica PowerCenter
SSIS
AWS Glue
Hybrid ETL/ELT tools
Tools that blend batch and streaming, extraction and transformation, often in a SaaS wrapper.
Fivetran
Azure Data Factory
Google Cloud Data Fusion
Pros and cons of the top ETL tools
Tower
Pros:
Python-native data flows with rich observability.
Runs dltHub, Polars, DuckDB, dbt, sqlmesh, and many other OSS libraries
Manages Iceberg lakehouse infrastructure
Dev/prod environment isolation, scheduling, and versioning built in
Cons:
Best for teams already comfortable with Python
Newer platform; not a legacy vendor
AWS Glue
Pros:
Deep integration with the AWS ecosystem
Serverless compute for batch ETL.
Supports Python and Spark
Cons:
Complex to debug
Steep learning curve for new users
Google Cloud Data Fusion
Pros:
Built-in connectors and visual pipeline design
Integrates with BigQuery and Google Cloud services
Cons:
GUI-heavy, less code-first
Limited control over lower-level transformations
SSIS
Pros:
Deep integration with Microsoft SQL Server
Battle-tested in enterprise environments
Cons:
Windows-only
Not cloud-native or Python-friendly
Informatica PowerCenter
Pros:
Rich feature set for data integration and quality
Long-standing enterprise vendor
Cons:
Cost
Vendor lock-in
Azure Data Factory
Pros:
Tight integration with the Azure ecosystem
GUI-based pipeline creation
Cons:
Limited custom logic unless you integrate with other tools
Azure-centric
dltHub
Pros:
Python-native, open-source ELT
Schema management, incremental loading
Hundreds of prebuilt connectors
Cons:
Requires orchestration and a runtime to run in production
Code-first is not so suitable for GUI-oriented developers, although work is being done on LLM-driven development
Airbyte
Pros:
Open-source, with cloud and self-hosted options
Large and growing connector library
Cons:
Requires infrastructure to manage at scale
Not transformation-focused
Oracle Data Integrator
Pros:
Strong for Oracle-to-Oracle integrations
Metadata-driven architecture
Cons:
Oracle lock-in
Legacy feel
Estuary
Pros:
Designed for real-time, event-driven data
Strong for CDC use cases
Cons:
Newer in the market
May require tuning for complex workflows
Fivetran
Pros:
Managed connectors and auto-scaling
No-code UI, minimal maintenance
Cons:
Expensive at scale
Closed source, limited control over logic
Trends in the ETL tool landscape in 2026
With the pros and cons out of the way, let’s quickly address what is actually changing. There are a few trends we, as a team, have noticed over the past year, with some having a big effect on the industry, while others are just emerging.
ETL consolidation
Earlier this year, Fivetran made headlines by merging its systems with dbt. This move followed their decision to acquire SQLMesh, transforming Fivetran from an orchestrator into an end-to-end ETL solution. Serhii Sokolenko, the cofounder of Tower, wrote a post about this and its implications across the industry.
In that article he discusses a couple of reasons why this could be happening. At its core, it seems to be a business decision. Acquire a company with many users, centralize, and build a data warehouse for an end-to-end solution. This could mean an expensive change for SQL native users. Many are already feeling the stress.
It's too early to draw conclusions, but it's looking more and more like alternatives like dlt might become more attractive to users.
Growing Python ecosystem
Tower is part of the growing Pythonic ecosystem. Python has been the main language for data engineering for a long time. With it becoming the main language for working with LLMs and data science, the appetite for new tools in the space has increased. The ability to write, test, and deploy in Python without juggling SQL-first transformation tools, external orchestrators, or JVM-based frameworks is in high demand.
For example, dltHub offers hundreds of connectors and helps users build pipelines with Python. This is also where we fit in: we want you to be able to execute your Python-native pipelines with strong observability, scheduling, and environment isolation. All of this is to ensure you stay fast in your data processes and don’t have blockers when building.
Iceberg lakehouses are becoming the standard.
Iceberg continues to solidify itself as the default open table format for lakehouse architectures. Many teams, including ours, have standardized on Iceberg due to its reliability, versioning, schema evolution, and broad engine support. Tools like Lakekeeper are examples of the ecosystem maturing, making governance and lifecycle management significantly easier.
However, the lakehouse landscape is far from settled. At this year’s Small Data SF conference, Ducklake made waves. It is a data lake format from DuckDB, and can be used not just as an analytics engine but as a potential foundation for smaller-scale or embedded lakehouse-style architectures.
This is a trend worth watching closely as teams look to unify local development, cloud pipelines, and lakehouse storage patterns.
Conclusion
ETL tools in 2026 reflect the complexity of the stacks we now build. It is often just as complicated to understand what is needed as to start building the infrastructure itself. We decided to write this guide because data engineers deserve better guidance, just like they deserve better data infrastructure.
Picking the right tools isn’t about features; it’s about fit. Tower exists to make it easier to fit. Whether you go full open source or mix in best-in-class vendors, Tower gives you orchestration, observability, and a clean way to run data flows on top of the lakehouse stack you already use, while future-proofing for plans you haven’t executed yet.
More tools don’t have to mean more duct tape. With Tower, you can ship ETL that works now and in the future.