The 15 Best ETL Tools in 2026, Compared

The ETL market looks different than it did a year ago. Fivetran merged with dbt Labs, Salesforce now owns Informatica, and Snowflake and Databricks both ship their own ingestion tools. If you last compared ETL tools in 2025, some of your shortlist has changed owners.

This guide compares 15 ETL tools for data and engineering teams in 2026. It covers what each tool does well, where it falls short, and who it fits. The comparison table comes first. Details on each tool follow.

Full disclosure: Matia makes an ETL platform, and we included ourselves. We’ve tried to be straight about every tool on the list, including ours.

ETL Tools Compared at a Glance

An ETL tool moves data out of the systems where it’s created and into a warehouse or lake where it can be analyzed. ETL stands for extract, transform, load. Most modern tools actually run ELT: they load raw data first and transform it inside the warehouse, usually with dbt.

A typical pipeline pulls from three kinds of sources:

• Production databases like Postgres, MySQL, and MongoDB, usually through change data capture (CDC)
• SaaS tools like Salesforce, HubSpot, Stripe, and Zendesk, through their APIs
• Files and event streams from S3, Kafka, and similar systems

It then loads that data into Snowflake, BigQuery, Databricks, Redshift, or a data lake on a schedule or continuously.

The hard part is keeping those pipelines running. APIs change, schemas drift, and large databases take hours to sync. Most teams run separate tools for ETL, data quality, and cataloging, so when something breaks there’s no single place to look. Gartner predicts that by 2028, data management markets will converge into a single market to reduce technology complexity (Gartner, June 2025).

Types of ETL Platforms

Manage cloudETL

The tradeoff is control. You depend on the vendor’s connector quality, logging, and support when something breaks.

Warehouse-native ETL

Snowflake and Databricks now offer their own ETL tools: Snowflake Openflow and Databricks Lakeflow Connect. They’re billed through your existing warehouse contract and keep data inside that platform’s governance.

Connector coverage is still narrower than dedicated ETL vendors, and they tie your pipelines to one warehouse. They make the most sense if you’re fully committed to that platform.

Open source ETL tools

Open source tools like Airbyte have no license fee for the self-hosted version. You get full access to the code and can build custom connectors.

You also take on hosting, upgrades, and on-call when a connector fails. Teams often start here and move to a managed tool once pipelines become business-critical.

Enterprise and On-prem platforms and ETL tools

Informatica, IBM DataStage, and Qlik Talend run on-prem, in the cloud, or both. They’re built for regulated industries, legacy systems, and complex governance requirements.

Expect longer implementations, specialized skills, and higher costs than cloud-first tools.

Real-time streaming

Streaming tools like  Striim deliver changes in seconds instead of minutes or hours. They’re the right fit for fraud detection, operational dashboards, and apps that react to data as it changes. Most analytics workloads don’t need sub-second latency, and streaming costs more to run.

How to choose an ETL tool

Check connector depth

A vendor claiming 500 connectors doesn't tell you a lot. You need to check whether the connectors you need cover the tables and fields you use. Many vendors offer “lite” connectors that sync only a subset of an API.

Before you sign, list your top 10 sources and check each one: which tables are supported, whether CDC is available for your databases, and how schema changes are handled.

Sync speed on your largest database

Small SaaS sources rarely cause problems. Your largest production database usually does. Ask how the tool handles the initial load of a multi-terabyte Postgres or MySQL database, and whether it can sync tables in parallel.

Run a proof of concept on your real data. Benchmarks on sample datasets won’t show you where your pipeline breaks.

What happens when something breaks

Pipelines fail. The question is how fast you find out and how fast you fix it. Check for:

• Table-level logs you can read without opening a support ticket
• Alerts in Slack or your incident tool
• Detection of schema changes and data quality issues at the source
• Whether a bad sync can be stopped before it reaches downstream tables and tools
• Support response times, in writing

Security and  compliance

Confirm SOC 2 Type II at minimum, plus GDPR or HIPAA if they apply to you. Check for VPC or private network connections, role-based access, and audit logs. For regulated data, ask where data is processed and whether it’s stored in transit.

Calculate total cost

License fees are one line in the budget. Add the engineering hours to maintain pipelines, the cost of the other tools you’ll need alongside it (reverse ETL, observability, catalog), and what it would cost to migrate off later.

Model your costs on your real data volumes, including growth. Usage-based pricing that looks affordable at 10 million rows can look very different at 500 million. For a deeper look at pricing models, see our ETL pricing guide.

1. Matia

Full disclosure: this is our list, so we put ourselves first. Matia is unified data infrastructure for data and engineering teams. ETL, reverse ETL, catalog, and observability run in one platform, so data moves, gets monitored, and gets sent back to business tools without stitching four vendors together.

Matia is built for engineers. It isn’t a low-code tool, and it doesn’t replace dbt for transformation. It works alongside dbt and monitors your dbt runs.

Key Features:

• ETL with 150+ connectors, including CDC for Postgres and MongoDB
• Reverse ETL that can stop a sync when an anomaly is detected, so bad data doesn’t reach Salesforce or HubSpot
• Observability at the point of ingestion, including schema change and data quality detection
• Catalog with table-level and column-level lineage, from source to warehouse and back out to your tools‍
• Monitors on dbt runs, connected to the syncs that feed them

Strengths:

• Postgres replication speed. Parallel syncs cut one customer’s 8TB Postgres sync from 5 days to 20 hours
• Table-level logs and WAL monitors, so debugging doesn’t start with a support ticket
• Backward compatible with Fivetran, so teams can migrate without rebuilding downstream models
• Support response times under 5 minutes
  One vendor for ETL, reverse ETL, observability, and catalog. Ramp reduced costs by 40% after consolidating onto Matia
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Ideal Use Cases:

• Teams replicating large Postgres or MongoDB databases where sync time matters
• Teams paying for separate ETL, reverse ETL, and observability tools and looking to consolidate
• Teams migrating off Fivetran who want to keep their existing dbt models
• Data teams preparing data for AI who need lineage and quality checks before data reaches models

Pricing: Custom pricing based on data volume and feature requirements. Contact us for detailed pricing information.

2. Fivetran

Fivetran is the best-known managed ETL tool, with a large SaaS connector library and little setup. It has also grown well beyond ETL. It bought Census for reverse ETL and Tobiko Data (the team behind SQLMesh) in 2025, then completed its merger with dbt Labs in June 2026. The combined company operates as Fivetran + dbt Labs.

Key features:

• Managed connectors that need little configuration
• Broad SaaS coverage across major business applications
• Change data capture for databases
• Transformation through dbt and reverse ETL through Census, now under the same company

Strengths:

• Low maintenance for standard SaaS sources
• One of the largest connector libraries on the market
• Mature documentation and a large user community
• dbt is widely used for transformation, and it now comes from the same vendor

Limitations:

• Pricing changed in 2025. Model your costs on current pricing before renewing
• Putting ETL, transformation, and reverse ETL with one vendor increases switching costs later
• Batch-based, so not built for sub-second streaming
• Debugging visibility is limited compared to tools with table-level logs
• Many connectors are Lite connectors, built for specific use cases with fewer endpoints. Check that the tables you need are supported before committing
• ETL, dbt, and Census started as separate products. The merger closed in June 2026, so check how they’re packaged and priced together today

Best for: SaaS-heavy stacks that need standard connectors with little setup, and teams already standardized on dbt who want one vendor for ETL and transformation.

Teams that move from Fivetran to Matia see about 30% better cost efficiency. PayIt cut costs 38% in its first month off Fivetran. See Matia vs Fivetran or estimate your savings with the Fivetran savings calculator.

3. Airbyte

Airbyte is the most widely used open source ETL tool. You can self-host it for free or use Airbyte Cloud. Its connector catalog includes official connectors plus a large set built by the community, and its connector builder lets teams create custom connectors without starting from scratch.

Strengths:

• No license fee for the self-hosted version
• Full access to the code, so you can fix or extend connectors yourself
• Connector builder for long-tail and internal APIs
• Active community

Limitations:

• Community connectors vary in quality and maintenance
• Self-hosting means your team owns upgrades, scaling, and on-call
• Large database syncs and CDC often need tuning to run reliably
• Observability and alerting are basic compared to dedicated tools

Best for: teams with strong engineering capacity who want control over their connectors, or need sources no managed vendor supports.

Pricing: free self-hosted. Airbyte Cloud and enterprise editions are paid. Check current plans on Airbyte’s site.

4. Informatica

Informatica is the long-standing enterprise standard for data integration, data quality, and master data management. Salesforce completed its acquisition of Informatica in November 2025, and Informatica now operates as a Salesforce subsidiary.

Strengths:

• Broad platform covering integration, data quality, MDM, and governance
• Runs in the cloud, on-prem, or hybrid
• Large partner and systems integrator network
• Proven at very large scale

Limitations:

• High cost and long implementations that often need specialized consultants
• More platform than most cloud-first teams need
• Teams outside the Salesforce ecosystem should ask how the roadmap will change under Salesforce ownership

Best for: large enterprises with complex governance requirements, legacy systems, or existing Informatica investments.

Pricing: consumption-based. Contact Informatica.

5. Snowflake Openflow

Openflow is Snowflake’s own data loading service, launched in June 2025 and built on Apache NiFi. It handles batch and streaming data, including unstructured files from sources like SharePoint and Google Drive for AI use cases. You can run it in your own AWS VPC or let Snowflake manage it.

Strengths:

• Billed and governed inside your Snowflake account
• Handles unstructured data for Snowflake Cortex AI use cases
• Batch and streaming in one service

Limitations:

• Built for Snowflake. It’s not the right fit if you use more than one warehouse or plan to switch
• Connector coverage is narrower than dedicated ETL vendors, and some connectors are still in preview
• NiFi-based flows take time to learn
• No reverse ETL or cross-stack observability

Best for: Snowflake teams that need lightweight connectors with minimal data volume.

Pricing: Snowflake charges for data loading, and in the bring-your-own-cloud model you also pay your cloud provider for compute.

6. Databricks Lakeflow Connect

Lakeflow Connect is Databricks’ managed data loading service. It lands data in Delta tables governed by Unity Catalog and runs on Databricks Workflows. Generally available connectors include Salesforce, Workday, ServiceNow, Google Analytics, SQL Server, and PostgreSQL, with more in preview.

Strengths:

• Data lands directly under Unity Catalog governance
• CDC for SQL Server, PostgreSQL, and MySQL
• No separate vendor contract if you already run on Databricks

Limitations:

• Only loads into Databricks
• Connector list is short compared to dedicated ETL vendors, and several are still in preview or beta
• No reverse ETL or observability outside Databricks

Best for: teams fully committed to Databricks with mostly mainstream sources.

Pricing: billed as Databricks usage.

7. Matillion

Matillion is best known for visual, drag-and-drop transformation that runs inside your warehouse. Matillion pushes the work down to Snowflake, Databricks, BigQuery, or Redshift.

Strengths:

• Visual transformation builder for teams that prefer it to SQL
• Pushes compute down to the warehouse
• Good fit for analysts and mixed-skill teams

Limitations:

• Data loading is lighter than transformation. Many teams pair it with a separate ETL tool
• Visual pipelines can be harder to version, review, and test than dbt code
• Credit-based pricing takes work to forecast

Best for: teams most focused on visual transformation.

Pricing: credit-based.

8. Qlik Talend and Stitch

Qlik acquired Talend in 2023, and with it Stitch. Talend is a broad integration and data quality suite. Stitch is a simpler ELT service for moving SaaS and database data into a warehouse.

Strengths:

• Talend covers integration, data quality, and governance in one suite
• Stitch is quick to set up for basic ELT
• Fits teams already using Qlik for analytics

Limitations:

• Talend has a steep learning curve and longer implementations
• Stitch has limited transformation and monitoring
• Check each product’s roadmap, since both now sit inside Qlik’s broader portfolio

Best for: mid-market and enterprise teams already invested in Qlik.

Pricing: subscription.

9. Rivery

Boomi acquired Rivery in December 2024. Rivery offers CDC, ELT, orchestration, and reverse ETL in one SaaS product, and it’s now part of Boomi’s integration platform.

Strengths:

• CDC, ETL, and reverse ETL in one tool
• Visual setup
• Fits teams already using Boomi for application integration

Limitations:

• Roadmap now tied to Boomi’s iPaaS priorities. Ask how it’s packaged and sold today
• Less depth for very large databases
• Observability is basic

Best for: mid-market teams already using Boomi.

Pricing: usage-based. Contact Boomi.

10. Hevo Data

Hevo is an ELT tool you can set up without writing code. It targets smaller teams that need data in a warehouse without dedicated data engineers.

Strengths:

• Fast setup without writing code
• Predictable tiered pricing
• Good fit for standard SaaS sources

Limitations:

• Less control for complex or custom pipelines
• Not built for very large database replication
• Limited observability and lineage

Best for: small and mid-size teams with standard sources and limited engineering time.

Pricing: tiered by event volume.

11. Estuary

Estuary is built for real-time CDC and streaming. It captures changes from databases and SaaS sources and delivers them to warehouses, lakes, and other systems with low latency, while also supporting batch.

Strengths:

• Low-latency CDC from production databases
• Streaming and batch in one tool
• Can deliver the same data to several destinations

Limitations:

• Smaller connector catalog than the largest vendors
• Fewer configuration options for teams with complex pipeline requirements
• No reverse ETL or catalog

Best for: teams that need real-time streaming without deep configuration options.

Pricing: usage-based.

12. AWS Glue

AWS Glue is Amazon’s serverless data integration service. It runs Spark-based ETL jobs and includes the Glue Data Catalog, which many AWS teams use as their metadata store.

Strengths:

• Serverless, with no infrastructure to manage
• Works with S3, Redshift, Athena, and other AWS services
• Pay only for the compute you use

Limitations:

• Few pre-built connectors for SaaS sources outside AWS
• Spark jobs need engineering skill to build and tune
• Debugging and cost tracking can be slow

Best for: engineering teams running mostly on AWS.

Pricing: pay per compute hour.

13. Azure Data Factory

Azure Data Factory is Microsoft’s cloud data integration service, with a visual pipeline designer and connectors across Azure and many outside sources. Microsoft also offers Data Factory inside Microsoft Fabric, so check which one fits your roadmap.

Strengths:

• Close integration with Azure Synapse, Fabric, and other Azure services
• Visual pipeline designer
• Hybrid connectivity to on-prem sources

Limitations:

• Pricing has many components and is hard to forecast
• Complex transformations often need extra services
• Best value is inside the Azure ecosystem

Best for: teams running mostly on Azure.

Pricing: pay per activity and pipeline run.

14. IBM DataStage

IBM DataStage is an enterprise ETL platform built for high-volume, parallel processing. It runs on-prem, in the cloud, or hybrid, and is part of IBM’s Cloud Pak for Data.

Strengths:

• Proven at high volumes and complex transformations
• Parallel processing engine
• Strong enterprise support

Limitations:

• High licensing and infrastructure cost
• Steep learning curve and long implementations
• Fewer modern SaaS connectors than cloud-first tools

Best for: large enterprises with existing IBM investments and heavy on-prem workloads.

Pricing: license or subscription. Contact IBM.

15. Striim

Striim focuses on real-time streaming and database replication. It’s used where data has to move continuously, such as from Oracle or SQL Server into a cloud warehouse with minimal lag.

Strengths:

• Low-latency CDC, including from Oracle and SQL Server
• In-flight processing on streaming data
• Runs in the cloud or on-prem

Limitations:

• Costs more than batch tools
• Less suited to SaaS sources and batch analytics
• Smaller ecosystem than the largest vendors

Best for: enterprises that need continuous replication from operational databases.

Pricing: subscription. Contact Striim.

FAQ

What is the best ETL tool in 2026?

It depends on your sources and your team. For SaaS-heavy stacks with little engineering time, managed tools like Fivetran or Hevo are the simplest. For large Postgres or MongoDB databases, or teams that want ETL, reverse ETL, observability, and catalog in one platform, Matia is built for that. Teams fully committed to Snowflake or Databricks should also look at Openflow or Lakeflow Connect.

What’s the difference between ETL and ELT?

ETL transforms data before loading it into the warehouse. ELT loads raw data first and transforms it inside the warehouse, usually with dbt. Most modern cloud tools run ELT, though the industry still calls the category ETL.

What’s the best ETL tool for Postgres?

Look for CDC support, parallel syncs for large tables, and table-level logs. Initial loads on large databases are where most tools struggle. Matia’s parallel syncs cut Ramp’s business-critical resyncs from 3.5 days to 4 hours.

Is there a free or open source ETL tool?

Yes. Airbyte’s self-hosted version is free and open source. You pay instead in engineering time to host, upgrade, and support it.

What are the best Fivetran alternatives?

Teams leaving Fivetran usually compare Matia, Airbyte, Hevo, Estuary, and their warehouse’s native ETL tool. See our Fivetran alternatives guide and Matia vs Fivetran.

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