Best ETL Tools (2026): Top 10 Compared

Fivetran sells the extract and load half of a pipeline as a managed service. A buyer picks one of more than 700 managed connectors, supplies credentials and a destination, and Fivetran handles schema discovery, incremental updates and schema drift without pipeline code.

Transformation happens after the load, inside the destination. Fivetran runs dbt Core projects on a schedule tied to the sync, and ships Quickstart data models that build analytics-ready tables for common sources without the buyer writing SQL. Transformation usage is metered as monthly model runs. Beyond the 5,000 free each month the pricing page lists $0.01 per model run for 5,001 to 30,000 runs, $0.007 for 30,001 to 100,000, and $0.002 above that.

Connection pricing is based on monthly active rows, meaning rows inserted, updated or deleted in a month rather than rows stored. The pricing page publishes median monthly list prices for named connectors at a company of 1 to 200 employees: $17.23 for Facebook Ads at 34,479 monthly active rows, $10.99 for Google Analytics 4, $44.12 for Google Ads and $423.78 for Marketo at 847,574 rows.

Above Standard, Enterprise adds enterprise database connectors and a hybrid deployment option, while Business Critical adds customer-managed encryption keys and private networking. Fivetran and dbt Labs are now one company.

Airbyte is an open-source data movement platform. A pipeline is configured as a source, a destination and a sync mode rather than written as code, and the same catalogue of more than 600 connectors is available whether the platform runs on the buyer's own infrastructure or on Airbyte Cloud. Change data capture, schema propagation, column selection and cron scheduling are listed across the plans.

When a source is missing, the Connector Builder produces a new connector without a Java or Python project. For teams that prefer code, PyAirbyte drives syncs from Python, a Terraform provider manages connections as infrastructure, and the platform documents integration with Airflow, Dagster and Prefect so extraction can sit inside an existing orchestration graph.

Airbyte does not transform inside the pipeline. It loads raw tables and leaves modelling to the warehouse, which in practice means dbt or SQL run by the orchestrator after the sync. Buyers who want the transform step inside the same product need a second tool.

On the data replication side, the self-managed open-source edition carries no licence fee, the hosted Standard plan starts at $10 a month and bills on data volume, Pro prices on compute capacity measured in Data Workers, and Enterprise Flex keeps data inside the buyer's own boundary under an Airbyte-managed control plane. A separate set of agent and Context Store plans is metered in agent operations.

dbt answers the transform half of the question and deliberately ignores the rest. Data must already be in the warehouse. From there an analyst writes a model as a SELECT statement in a .sql file, and dbt compiles it into the create-table or create-view statement the warehouse needs, works out the dependency graph between models, and runs them in the right order. Because models are plain files, the whole transformation layer lives in Git and moves through pull requests like application code.

The surrounding machinery is what distinguishes it from hand-written SQL scripts. Tests assert conditions such as uniqueness or non-null on a column and fail the run when they break. Documentation and lineage are generated from the same project and surfaced through dbt Catalog. The dbt Semantic Layer holds metric definitions so a downstream tool reads one definition of revenue.

Four plans are published. Developer is free for a single seat, 3,000 successful models a month and one project, with the browser IDE and job scheduling. Starter costs $100 per user a month and includes five developer seats, 15,000 models, 5,000 queried metrics, API access and basic Catalog and Semantic Layer. Enterprise is custom-priced and raises the limits to 100,000 models, 20,000 metrics and 30 projects while adding Mesh, Canvas and Insights. Enterprise+ adds PrivateLink, IP restrictions and unlimited projects.

AWS Glue is Amazon's serverless data integration service. Pipelines are built in Glue Studio, either visually as a graph of source, transform and target nodes or as PySpark or Scala code, and the generated job runs on an Apache Spark environment that AWS provisions and tears down. There is no cluster to size or keep warm. The service connects to more than 100 sources and lands data into data lakes, warehouses and lakehouses.

The transform step is real Spark rather than a restricted expression language: joins, window functions, user-defined functions and custom Python libraries are all available. Glue DataBrew adds a separate no-code preparation surface for analysts. Crawlers scan sources, infer schemas and register tables in the Glue Data Catalog, which then serves as the shared technical metadata layer for Amazon Athena, Amazon Redshift, Amazon EMR and SageMaker Lakehouse, with Lake Formation supplying fine-grained permissions.

Billing is metered rather than subscribed. ETL jobs and interactive sessions cost $0.44 per DPU-hour, billed by the second; one DPU provides 4 vCPU and 16 GB of memory. The pricing page gives a worked example: a 15-minute job on 6 DPUs costs $0.66. Crawlers bill hourly by the second, DataBrew jobs per minute, and the Schema Registry carries no charge. Rates vary by region.

Matillion sells the Data Productivity Cloud, a platform in which both halves of a pipeline are designed on the same canvas. Orchestration pipelines handle extraction and loading from pre-built or custom connectors. Transformation pipelines then push work down into the cloud data warehouse, so the heavy processing runs on Snowflake, Databricks, BigQuery or Redshift compute rather than on Matillion's own infrastructure. Components can be dropped visually, and SQL or Python components are available where the visual set runs out.

Two things in the plan comparison are worth a buyer's attention. A built-in Git repository is included from the Developer tier, so pipelines are version-controlled by default rather than as an enterprise add-on. Real-time validation and sampling components then let a developer check a transformation against live data while building it, which shortens the edit-and-rerun loop that makes low-code pipeline work slow.

Maia is the vendor's agentic layer, described as an AI workforce that creates, manages and evolves pipelines across all three plans.

Developer covers one developer user, unlimited projects, pre-built connectors and the low-code canvas. Teams raises the count to five developer users and adds an audit log and a service-level agreement. Scale adds custom single sign-on, hybrid cloud deployment through a self-hosted agent, data lineage, streaming change data capture pipelines and 180-day log retention. Every plan draws on the same credit system, and no credit price is published.

Informatica is the incumbent in enterprise data integration, and its Cloud Data Integration service covers a wider set of execution patterns than most products on this page: classic ETL mappings, ELT pushdown into a cloud warehouse, Spark-based advanced data integration for large volumes, and a fully managed serverless option where no agent infrastructure is maintained. Change data capture and replication arrive through Cloud Mass Ingestion rather than as a separate purchase.

Pipelines are built in a low-code mapping designer. Sources and targets are connected, transformations are placed between them, and the advanced mapping designer adds parameterised and reusable mappings plus a task flow designer for complex orchestrations. CLAIRE, the platform's AI engine, suggests source datasets and next-best transformations during design and auto-tunes workloads at run time.

Pricing works differently from the rest of this list. Rather than seats or rows, Informatica sells Informatica Processing Units, a capacity unit consumed by whichever eligible cloud service the customer runs. Each service has its own scalar and metric published in the Cloud and Product Description Schedule: Advanced Data Integration, for example, is metered in compute units per hour. That structure lets a buyer shift capacity from an ingestion project to a data quality project without renegotiating.

The trade-off is forecasting. Administrators can restrict which users reach which service and set threshold alerts.

Integrate.io positions itself against the metered model that most of this category uses. Its stated aim is that the people closest to the data own the pipelines, so the builder is drag-and-drop with optional code, supported by a copilot and an MCP server. The same platform covers four pipeline shapes: ETL into a warehouse, ELT with transformation pushed down, change data capture, and reverse ETL back into business applications.

Transformation is handled inside the pipeline rather than delegated to a downstream tool, which matters for buyers whose destination is an application rather than a warehouse. That also makes the product a plausible replacement for a legacy on-premises ETL suite, and the vendor runs a migration path around exactly that: a migration tool moves existing pipelines, a solutions engineer finalises them, and the vendor states it will buy out the incumbent contract.

Integrate.io Core costs $1,999 a month and includes unlimited data volumes, unlimited pipelines, unlimited connectors, full platform access, a 60-second minimum pipeline frequency and 30-day onboarding. A second, quote-priced tier layers advanced security and compliance, team role-based access control, HIPAA and healthcare readiness, and tailored support on top of Core.

A team syncing three low-volume SaaS sources pays less on a consumption product, while a team moving large volumes on a tight frequency reaches the break-even point quickly.

Keboola is a hosted data platform that bundles extraction, transformation, storage and orchestration rather than selling the pieces separately. A pipeline starts with an extractor drawn from more than 700 connectors, lands data in Keboola Storage, and is then transformed in the buyer's chosen language. Flow Builder sequences the steps and handles scheduling and dependencies, so an orchestrator is not a separate purchase.

The transform step is unusually open for a hosted product. SQL and Python transformations are available on the free tier, and subscription projects add R and Julia with optional Spark and MLflow backends. Transformations run against a storage backend, which is Snowflake by default on the free plan and can be Snowflake, BigQuery or DuckDB on Enterprise.

Billing is measured in job time rather than rows or seats. The Free plan includes 120 minutes of compute in the first month and 60 minutes each month after that, with additional minutes at $0.14 each, bought by credit card. Subscription contracts convert the same idea into Time Credits measured against project usage. Purchased credits are non-refundable but do not expire while the project stays active.

Enterprise adds the governance layer: Data Catalog and data sharing between projects, tailored change data capture and streaming, development and production modes with Git-based continuous integration, private SaaS deployment in a virtual private cloud, and single sign-on.

Portable solves a narrow problem that stops a lot of pipeline projects: the source nobody else connects to. Its catalogue lists more than 1,500 prebuilt connectors, and the mix is weighted to long-tail applications alongside the obvious ones such as NetSuite SuiteAnalytics, Salesforce, PostgreSQL, HubSpot, Stripe and Zendesk. When the needed source is still missing, connector development is the service being sold rather than an exception to it, with the vendor researching, building and then maintaining the connector.

The pipeline itself is deliberately simple. A data flow is one source paired with one destination, run on demand, on a schedule, or triggered through the API depending on the tier. Loading is incremental and destinations cover warehouses, databases, lakes and files. Transformation is not part of the product: raw tables land in the destination and modelling is expected to happen there, typically in SQL or dbt.

Three plans are published, all billed monthly. Standard costs $1,800 for 6 enabled data flows, with Standard sources, warehouse or database destinations, hourly syncs and 9-to-5 priority support. Pro costs $2,800 for 15 flows and adds Pro sources, every destination type, 15-minute syncs, cron scheduling, developer API and webhook access and 24x7 premium support. Advanced costs $4,200 for 25 flows and adds the Advanced source tier. On Standard, the integration services package can be bought separately at $2,000 a month.

Dataddo is priced and packaged for the smaller end of this category, and its plan structure reflects a specific view of what a pipeline is for. One plan sends data from applications straight into dashboarding tools; the other sends data between any source and any destination, including warehouses. That split lets a team that only needs Google Ads data inside a reporting tool avoid paying for warehouse infrastructure it will not use.

Pipelines are configured as data flows: a source or set of sources paired with a destination. Data blending is included on the paid plans, so several sources can be combined before delivery, which is the product's answer to the transform question. Heavier modelling is expected to happen in the destination warehouse. A single control plane drives multiple data planes, so execution can sit in cloud, on-premises or hybrid environments.

Four tiers are listed. Free costs nothing and covers 3 data flows, 100,000 rows extracted a month and weekly syncs into dashboarding tools. Data to Dashboards costs $99 a month billed annually, or $129 billed monthly, for 5 flows, 10 million rows and hourly syncs. Data Anywhere costs the same and covers 3 flows but adds warehouse ETL and ELT, reverse ETL, database replication, five-minute syncs and SSH tunnelling. Enterprise is custom-priced and adds on-premises deployment and API access.