Top 21 ETL Tools: Features, Pricing, Use Cases

ETL tools move data from the systems where it is created – ERPs, CRMs, databases, SaaS apps – into the systems where it is analyzed, cleaned up and made consistent along the way. The market in 2026 spans free open-source frameworks, managed ELT services, hyperscaler-native services, and heavyweight enterprise suites, and the differences in cost and operating model are enormous.

This guide compares the top 21 ETL tools by what each one actually does best, how it is priced, and where it fits, so you can shortlist in minutes instead of weeks.

What changed in ETL for 2026

Top 21 ETL tools at a glance

ETL or ELT: which of these tools is which

The two acronyms describe when transformation happens, and that single choice determines which tools on this list are even candidates. Classic ETL transforms data in a dedicated engine before loading it. By contrast, ELT loads raw data into the data warehouse first and transforms it there, using the warehouse’s own compute.

Most tools marketed as “ETL tools” in 2026 are in fact ELT tools. Furthermore, several entries below are neither: they are orchestrators or processing engines that teams reach for when building pipelines. Consequently, knowing which category a tool sits in prevents most shortlisting mistakes.

Two names in that table are not numbered entries below, because neither is an ETL tool in its own right. Specifically, Databricks is a lakehouse platform whose Delta Live Tables and LakeFlow features handle pipeline work inside a much larger analytics product. Similarly, dbt transforms data that something else has already loaded, which is precisely why Fivetran acquired it. Nevertheless, both appear on ETL shortlists constantly, so it is worth knowing what they are before comparing them to the tools below.

1. Peliqan: the all-in-one data platform

Peliqan combines what usually takes four separate tools: ETL/ELT pipelines, a built-in data warehouse, transformation tooling, and activation. Instead of wiring a connector service to a data warehouse to a BI layer, you connect your sources and get queryable, governed data in minutes. It is an all-in-one data platform aimed at teams that want outcomes rather than infrastructure projects.

Key features of Peliqan

Use cases for Peliqan

Teams use Peliqan to replace an assembled data pipeline stack with one platform: consolidating ERP, CRM and accounting data for reporting, building customer 360 views, powering embedded analytics for SaaS products, and feeding AI agents.

Furthermore, its low-code approach means data engineers go faster and analysts self-serve instead of filing tickets.

Best for: teams that want ETL, a data warehouse, transformations, BI and AI access in one platform with fixed, transparent pricing instead of consumption-based surprises.

2. Skyvia

Skyvia is a no-code data integration platform with 200+ connectors across SaaS apps, databases, file storage, and data warehouses. Specifically, it covers ETL/ELT, reverse ETL, data sync, and workflow automation in one platform, with visual pipeline design, transformations, monitoring, and volume-based pricing.

However, its breadth is both the selling point and the caveat. Each capability is competent rather than best-in-class, which suits teams consolidating several small jobs onto one bill more than teams with one demanding pipeline. Moreover, import and export run on a schedule rather than continuously, so sub-hourly freshness needs a different tool.

Best for: data-driven teams that value fast no-code setup and need ETL/ELT, reverse ETL, data sync and workflow automation in one platform. Watch out for: scheduled rather than streaming refresh.

3. Airbyte

Airbyte is the most widely deployed open-source ELT platform, and the one you are most likely to meet in a modern data stack. Its catalog runs past 600 connectors, the largest of any tool here, built by a combination of the core team and a very active community.

The architecture is what makes it distinctive. Connectors are containerised and versioned independently, so upgrading one source does not disturb the rest. Furthermore, a no-code Connector Builder and a Python CDK let teams add a missing source in hours rather than waiting on a vendor roadmap, which is the single most common reason teams pick it. Additionally, PyAirbyte extends the same connectors into Python workflows for teams that would rather orchestrate syncs in code.

There are three ways to run it. Self-hosted Airbyte Open Source is genuinely free and the reason for its adoption curve, though you carry the Kubernetes footprint, upgrades and failure handling yourself. By contrast, Airbyte Cloud removes that operational burden on credit-based pricing. Finally, Airbyte Enterprise adds SSO, multi-tenancy and support.

Nevertheless, the honest trade-offs are connector maturity and total cost of ownership. Community connectors vary widely: the top hundred are solid, the long tail ranges from excellent to abandoned, and certification status is worth checking before committing a pipeline to one. Consequently, self-hosting is free in licensing and rarely free in engineering time, so compare it against managed options on the fully loaded number rather than the sticker.

Best for: teams that need a long tail of connectors, want to build their own, or have a hard requirement to keep data movement inside their own infrastructure. Watch out for: community connector quality and the operational cost of self-hosting.

4. Meltano

Meltano is an open-source ELT framework built around the Singer taps-and-targets standard, managed entirely as code. Pipelines live in version control, run in CI/CD, and extend to 600+ community connectors of varying maturity. There is no visual interface to speak of – that is the point. Consequently, it appeals to engineering teams that treat data integration tooling like software: tested, reviewed, reproducible.

Compared with Airbyte, which solves a similar problem, Meltano is lighter to run and more opinionated about code, whereas Airbyte offers a UI and a larger maintained catalog. In practice, teams that already manage infrastructure as code tend to prefer Meltano; teams that want analysts to add a source themselves tend not to.

Best for: engineering-led teams that want free, code-first ELT and accept connector maintenance as part of the deal. Watch out for: Singer tap quality varies widely; budget time for testing.

5. Matillion

Matillion’s Data Productivity Cloud provides visual, drag-and-drop pipeline design with pushdown execution: transformations compile to SQL that runs inside Snowflake, BigQuery, Redshift or Databricks rather than on Matillion’s own compute. Recent releases lean heavily into AI-assisted pipeline building with its Maia copilot. Pricing is credit-based, which scales smoothly but needs monitoring on spiky workloads.

Pushdown is the thing to understand before adopting it. Because the work happens in your data warehouse, performance and cost move onto that bill, which is efficient when the warehouse is already sized for it and expensive when it is not.

Best for: teams standardized on one cloud data warehouse that want visual ELT development with pushdown execution inside the data warehouse. Watch out for: warehouse compute costs rising as pipelines grow.

6. Fivetran

Fivetran is the reference for fully managed ELT: 700+ connectors that handle schema drift, incremental syncs and API changes automatically. After completing its merger with dbt, it now spans ingestion and transformation in one vendor, alongside its Census acquisition for reverse ETL. However, the trade-off is cost: consumption pricing based on monthly active rows becomes a serious line item at scale, and totals are hard to predict. We compare the approaches in detail in Peliqan vs Fivetran.

The MAR model deserves scrutiny because it charges by rows changed, not rows of value. Specifically, a chatty source that updates timestamps on every record can cost more than a source carrying your actual reporting data.

Best for: data teams with budget that want zero-maintenance ingestion into an existing data warehouse. Watch out for: MAR-based costs that grow with data volume, not with value.

7. Stitch

Stitch is a lightweight ELT service, also Singer-based, that replicates 130+ sources into cloud data warehouses with minimal setup. Once acquired by Talend and now under Qlik, its development pace has visibly slowed, and it remains extract-and-load only: transformations happen downstream in your data warehouse. Row-based pricing keeps entry costs low for small volumes.

For new builds in 2026, however, the comparison to make is against Airbyte, which is free self-hosted, more actively developed and carries a far larger catalog. Stitch still earns its place where a simple managed replication job already runs reliably and nobody wants to touch it.

Best for: small teams needing simple, cheap replication today. Watch out for: an uncertain roadmap under Qlik ownership.

8. Apache Airflow

Airflow is not an ETL tool in the strict sense – it is the open-source standard for orchestrating pipelines written as Python DAGs. Airflow 3.0 modernized the architecture with a new UI, DAG versioning and event-driven scheduling. You write the extract and transform logic; Airflow schedules, retries and monitors it. Furthermore, many teams run it alongside dedicated ingestion tools, or connect outputs onward – Peliqan ships an Apache Airflow connector for exactly that pattern.

The common mistake is adopting Airflow expecting connectors. It has none in the ELT sense. Consequently, pairing it with Airbyte or a managed ingestion tool, and reserving Airflow for scheduling and dependencies, is the pattern that works.

Best for: engineering teams orchestrating complex, custom pipeline logic. Watch out for: you are building and maintaining the actual ETL code yourself.

9. Integrate.io

Integrate.io packages low-code pipeline building, 60-second CDC replication and API generation under a flat-fee pricing model – a deliberate contrast to consumption-priced rivals. The visual designer covers common transformation needs without code, and fixed pricing makes budgeting straightforward for predictable workloads.

Nevertheless, the flat fee is both the reason teams shortlist it and the reason some rule it out. The entry point sits above what a small team would pay on a consumption plan, so the economics favour steady, substantial volume over occasional syncs.

Best for: mid-market teams that value cost predictability over the deepest connector catalog.

10. Oracle Data Integrator

ODI is Oracle’s ELT workhorse: declarative mappings with pushdown execution on the target database, tight integration with Oracle databases, EBS and OCI services. It remains a solid choice inside Oracle-standardized enterprises, less so anywhere else – the skills pool and ecosystem are Oracle-specific, and modern cloud data warehouse patterns often fit newer tools better.

Best for: enterprises with significant Oracle estates and existing ODI expertise.

11. IBM InfoSphere DataStage

DataStage is IBM’s enterprise ETL engine, known for parallel processing that moves very large volumes on schedule. It now lives inside Cloud Pak for Data, adding containerized deployment and governance integration. Implementations are substantial projects with matching license and consulting costs, which is the price of its scale and reliability in regulated industries. Building a modern data warehouse on it from scratch in 2026 is rare; running mission-critical legacy volume on it is common.

Best for: large enterprises with high-volume, compliance-heavy workloads already invested in IBM.

12. AWS Glue

Glue is AWS’s serverless ETL service: Spark jobs without cluster management, a Data Catalog for metadata, crawlers for schema discovery, and Glue Studio for visual job building. Pay-per-use DPU pricing means you pay for compute consumed, nothing idle. It shines when your stack is AWS-native (S3, Redshift, Athena) and your team is comfortable with Spark; conversely, it frustrates when you want simple SaaS connectors or a low-code Python approach without Spark overhead.

Best for: AWS-centric teams with Spark skills processing large datasets into Redshift or S3.

13. Azure Data Factory

ADF is Azure’s pipeline service: 90+ connectors, visual pipeline authoring, mapping data flows for transformations, and per-activity pricing. Microsoft’s center of gravity is shifting toward Fabric Data Factory, but ADF remains the standard for Azure-hosted integration today, especially for hybrid on-prem-to-cloud scenarios via self-hosted integration runtimes. Complex logic tends to accumulate across many pipeline activities, so keep transformations centralized where possible.

Best for: organizations standardized on Azure, particularly with hybrid connectivity needs. Watch out for: the migration question as Microsoft invests in Fabric.

14. Informatica PowerCenter

Informatica remains the enterprise data management heavyweight: ETL, data quality, master data management and governance in one (large) platform, now under Salesforce ownership following the acquisition. PowerCenter is the legacy on-prem engine; IDMC is the cloud successor with consumption-based IPU pricing. For enterprises managing complex SaaS and on-prem integration estates with strict governance requirements, it is still a default – at a matching price point.

Best for: large enterprises where governance, lineage and MDM matter as much as pipelines. Watch out for: integration roadmap questions post-acquisition.

15. Talend

Talend built its name on open-source ETL, but Talend Open Studio was retired in January 2024 – the free on-ramp is gone. What remains under Qlik is Talend Cloud / Qlik Talend Data Integration: a commercial suite combining pipelines, data quality and governance with a large connector catalog. It is capable and mature, though teams that chose Talend specifically for open source now face a commercial decision and should factor in broader data management needs when re-evaluating.

For those teams, the natural migration path is Airbyte or Meltano rather than another commercial suite, since the original reason for choosing Talend was usually the open-source licence.

Best for: organizations wanting integration plus data quality in one commercial suite.

16. Qlik Data Integration (Compose)

Qlik Compose has been folded into the broader Qlik Talend Data Integration portfolio, where its strengths persist: change data capture from operational databases (the former Attunity technology) and automated data warehouse build-out that generates modeling and loading code. Combined with Qlik’s analytics stack, it targets end-to-end data automation from source CDC to dashboard.

CDC is the reason to look at it. Specifically, log-based capture from production databases with minimal load is genuinely hard, and this is one of the few tools here that does it properly.

Best for: teams needing low-latency CDC replication, especially into Qlik analytics environments.

17. Pentaho Data Integration

Pentaho Data Integration (Kettle) offers visual drag-and-drop ETL with a genuinely free community edition, now stewarded by Hitachi Vantara with commercial editions layering on support and enterprise features. It handles classic batch ETL and file-heavy workflows well and pairs with Pentaho’s reporting-oriented heritage. The ecosystem is quieter than it once was, but for budget-constrained visual ETL it still delivers.

Best for: teams wanting free, visual, self-hosted ETL and willing to accept a smaller community.

18. Google Cloud Dataflow

Dataflow is GCP’s managed runner for Apache Beam, unifying batch and streaming in one programming model with automatic scaling. It is the strongest option here for true streaming ETL – clickstreams, IoT, event processing – where latency matters. Like Glue, however, it assumes engineering capacity: Beam pipelines are code, and the learning curve is real.

Best for: GCP teams with streaming workloads, Beam skills, and data landing in BigQuery.

19. Microsoft SSIS

SQL Server Integration Services ships with SQL Server licenses, making it effectively free for on-prem Microsoft shops. It is mature, well-documented and deeply understood by a generation of DBAs. Nevertheless, it is firmly a previous-era tool: Windows-bound, weak on SaaS APIs, and Microsoft’s investment has moved to ADF and Fabric. Existing SSIS estates run reliably for years; new cloud-facing projects rarely start here.

Best for: on-prem SQL Server environments with existing SSIS skills and packages.

20. Hevo Data

Hevo is a no-code ELT platform with 150+ connectors, automatic schema mapping and near-real-time replication, priced on event volume with a free tier for small workloads. It positions as the approachable alternative to Fivetran for lean data teams, trading some connector depth and enterprise features for simplicity and lower entry cost. Consequently, teams often pair it with a separate data cloud strategy as they scale.

Best for: startups and mid-market teams wanting quick, no-code pipelines without enterprise pricing.

21. SAS Data Management

SAS Data Management serves organizations already committed to SAS analytics: integration, quality and governance tooling that feeds SAS’s statistical and AI workloads natively. Where advanced analytics teams live in SAS, keeping data preparation in the same ecosystem reduces friction. Outside that context, however, its licensing model and proprietary stack make it a hard sell against modern alternatives.

Best for: analytics-driven enterprises with existing SAS investments in regulated sectors.

Choosing the right ETL tool

Conclusion

The ETL tools market in 2026 rewards clarity about your own situation: cloud ecosystem, team skills, data volumes and budget model. Hyperscaler services fit single-cloud engineering teams; managed ELT fits funded data teams with an existing data warehouse; open source fits teams that trade time for license savings; enterprise suites fit governance-heavy scale. Ultimately, if what you actually want is working, governed, AI-ready data without assembling a stack, an all-in-one platform gets you there fastest – see everything Peliqan includes.

Not sure where your setup fits? Talk to us and we will map it with you.