ETL (Extract, Transform, and Load) tools help teams extract data from sources, transform it into a usable format, and load it into a data warehouse or data lake. Choosing the right tools affects data freshness, reliability, cost, and how much engineering time you spend maintaining data pipelines.

This guide offers brief, balanced overviews of 14 top ETL tools. You’ll learn what each is best for, standout capabilities, limitations, typical company fit, and pricing.

We’ll also cover key factors to consider when evaluating your needs. Use it to narrow your shortlist and understand the trade-offs before you run a proof of concept.

Top 14 ETL tools

Now that you understand what they are, let's take a look at the top ETL tools on the market.

1. Fivetran

Best for: Teams that want an automated data integration solution with deep connector coverage and minimal pipeline maintenance.

Standout features:

• 700+ pre-built connectors across a wide range of data warehouses, SaaS apps, business intelligence tools, relational databases, SAP data services, PostgreSQL, MySQL, Google BigQuery, Azure Event Hubs, and more.
• Fully managed data pipelines with automated schema handling and change data capture (CDC)
• Data transformations through dbt
• Reverse ETL tools
• Role-based access controls

Limitations / trade-offs:

• No in-pipeline data cleaning or business logic;

Ideal company / skill level:

• Mid-market to enterprise; data teams that prefer low-ops pipelines and need to connect to multiple cloud-based solutions.

Pricing:

• Free 14-day trial
• Usage-based pricing model, billed by MAR.

2. Qlik Talend Cloud

Best for: Organizations that need hybrid (cloud + on-premises) data integration with granular control over jobs and data quality.

Standout features:

• Talend Studio and Talend Cloud cover batch/stream pipelines, data quality, and governance; they can be deployed in the cloud or on-premises.
• Broad connector library across databases, SaaS apps, files, and APIs.
• Now part of Qlik; available as Qlik Talend Cloud with a unified platform.

Limitations / trade-offs:

• The free Talend Open Studio was retired on January 31, 2024; most use cases now require paid licenses.
• More setup/ops overhead than fully managed ELT tools; advanced work may require developer skills.

Ideal company / skill level:

• Mid-size to large teams with mixed environments and in-house data engineering expertise.

Pricing:

• 14-day free trial of Talend Cloud
• Ongoing pricing is quote-based (contact sales).

3. Matillion

Best for: Data teams that want low-code design with pushdown ELT on cloud warehouses (Snowflake, Amazon Redshift, BigQuery, Databricks, Synapse).

Standout features:

• Visual jobs plus SQL/code-optional transformations; pushdown optimization.
• Two product paths: Data Loader for SaaS/database ingestion (including some CDC) and Matillion ETL for fuller transform/orchestration.
• Run as part of the Data Productivity Cloud with centralized admin and observability.

Limitations / trade-offs:

• Credit-based, consumption pricing; you also pay for cloud compute.
• More configuration/ops than fully managed ELT services.

Ideal company / skill level:

• Mid-size to enterprise teams with data warehousing expertise that want control over orchestration and transformation.

Pricing:

• 14-day free trial with complimentary credits
• Ongoing usage billed via credits (marketplace or direct).

4. Integrate.io

Best for: Teams that want low-code pipelines with fixed-fee pricing and strong e-commerce/SaaS coverage. (Plus optional reverse ETL and API generation).

Standout features:

• Low-code ETL/ELT with optional CDC and reverse ETL; platform bundles with Xplenty (ETL/Reverse ETL), DreamFactory (instant API generation), and FlyData (CDC/ELT).
• Broad connector catalog spanning common SaaS, databases, and e-commerce tools like Shopify, BigCommerce, and Magento.
• Field-level encryption using your own AWS KMS keys for sensitive columns.

Limitations / trade-offs:

• Fixed monthly fee may exceed the needs/budget of very small teams.
• Low-code design reduces coding needs, but complex logic and ops still require data engineering time.

Ideal company / skill level:

• Ops/analytics teams at small-to-mid market companies that prefer visual pipeline design and strong SaaS/e-commerce connectors.

Pricing:

• 14-day free trial
• Core plan listed at $1,999/month with fixed-fee, unlimited pipelines/connectors.

5. Snaplogic

Best for: Enterprises that need one iPaaS for app integration + data pipelines, with a large connector library and AI-assisted design.

Standout features:

• Visual ETL/ELT and application integration on a single platform with Snap Packs (pre-built connectors).
• AI helpers (AutoSuggest, formerly Iris; SnapGPT) that recommend next pipeline steps and speed design.
• Supports data warehousing ELT and reverse ETL; deploys across cloud and connects to on-prem via Snaplex.

Limitations / trade-offs:

• Pricing is quote-based and enterprise-oriented; some premium Snap Packs carry additional fees.

Ideal company size / skill level:

• Mid-to-large teams with integration engineers who need to connect many SaaS/ERP apps and data stores.

Pricing:

• Request-based packages; free trial available.
• Marketplace listings show enterprise tiers start around $125,000/year.
• Premium Snap Packs are priced separately.

6. Pentaho Data Integration

Best for: Teams that want a visual/ETL orchestration tool they can run on-premises or in hybrid environments, with an option to start on a free developer build.

Standout features:

• Pentaho’s Spoon GUI for drag-and-drop pipelines and jobs; broad connectors across databases, files, and APIs.
• Deploy on-prem, cloud, or hybrid; integrates with the wider Pentaho analytics platform.
• Two editions: Developer Edition (formerly Community Edition) and Enterprise Edition with support and additional features.

Limitations / trade-offs:

• More configuration and operational overhead than fully managed ELT services.
• Some enterprise capabilities (data security/governance, advanced admin) require the paid edition.

Ideal company / skill level:

• Mid-size and enterprise teams with in-house data engineering that need on-premises/hybrid control.

Pricing:

• 30-day free trial
• Enterprise pricing is quote-based.
• Developer Edition is free.

7. Singer

Best for: Engineering teams that want a free, code-driven way to build and run custom extract/load pipelines.

Standout features:

• Open-source spec that lets taps (extractors) and targets (loaders) interoperate via newline-delimited JSON over stdout/stdin.
• Large community ecosystem; connectors are discoverable via Meltano Hub, with SDKs to speed new tap/target development.

Limitations / trade-offs:

• Requires CLI/Python skills; no managed hosting or GUI out of the box.
• Connector quality and maintenance vary by project/community; some taps/targets may be unmaintained.

Ideal company / skill level:

• Startups to enterprises with data engineers who prefer open standards and need custom sources/destinations.

Pricing:

• Free and open source (build/run on your own data infrastructure).

8. Apache Hadoop

Best for: Organizations with established on-premises big-data stacks that need distributed storage/compute and control over data locality.

Standout features:

• HDFS (Hadoop Distributed File System) for distributed storage, YARN for resource management, and MapReduce for batch processing.
• Scales across commodity hardware; data-local processing reduces network I/O.
• Broad Apache ecosystem (eg, Hive, Pig, HBase, Oozie) for SQL, scripting, NoSQL, and scheduling.

Limitations / trade-offs:

• Significant cluster administration and tuning; higher ops overhead than managed cloud services.
• Optimized for batch, not interactive/real-time workloads; many teams now favor cloud object storage + Spark-style engines.
• Requires strong Java/DevOps skills; upgrades and migrations can be complex.

Ideal company / skill level:

• Large enterprises or public-sector teams with in-house data engineering or on-premises or hybrid mandates.

Pricing:

• Open source (no license fees).
• Infrastructure, support subscriptions, and staffing costs apply.

9. Dataddo

Best for: Smaller teams that want no-code ETL/ELT and reverse ETL with predictable, flow-based pricing and quick connector turnaround.

Standout features:

• No-code pipelines for ETL/ELT and reverse ETL; supports dashboarding tools (Tableau, Power BI, Looker Studio), and warehouse/app destinations.
• Flow-based billing and a limited free plan (weekly syncs, capped rows).
• New connectors on request, typically delivered within around 10 business days.

Limitations / trade-offs:

• Feature depth and scalability may trail heavier enterprise platforms; complex logic can still require engineering time.
• The free plan has row and frequency limits; some use cases will need paid tiers.

Ideal company / skill level:

• Small business ops/analytics teams or SMBs that want a visual setup and BI/App syncs without managing infrastructure.

Pricing:

• Free tier (3 flows, 100k rows/month, weekly syncs).
• Paid plans start at $99/month with higher flow/row limits and more frequent syncs.

10. AWS Glue

Best for: AWS-centric teams that want serverless, Spark-based ETL with built-in cataloging and visual job authoring.

Standout features:

• Serverless ETL with Glue Studio (visual editor) and notebooks; build jobs in Python (PySpark) and run on managed Spark.
• Data Catalog and crawlers for automatic schema discovery and metadata management across S3, JDBC sources, Redshift, and more.
• Broad connector options, including AWS Marketplace connectors for non-native sources.

Limitations / trade-offs:

• DPU(data processing unit)-based pricing can be harder to forecast
• Cold start latency

Ideal company / skill level:

• Teams with data engineering skills operating primarily on AWS (S3, Redshift, RDS/Lake Formation) who want to avoid cluster management.

Pricing:

• Pay per second (1-minute minimum) based on Data Processing Units.
• $0.44 per DPU-hour for Glue for Spark jobs.
• Other features (eg, crawlers) are priced separately.

11. Azure Data Factory

Best for: Azure-first teams that want a serverless, code-free + code-based integration service with hybrid connectivity via self-hosted runtimes.

Standout features:

• Visual authoring and monitoring for ETL/ELT pipelines; code options via Data Flows and notebooks.
• Three Integration Runtime options (Azure, self-hosted, Azure-SSIS) to reach cloud and on-premises data architecture; lift-and-shift existing SSIS packages.
• Large connector catalog across databases, files, SaaS, and Azure services.

Limitations / trade-offs:

• Complicated pricing model. Costs can be harder to forecast.
• Networking features differ by runtime. (eg. Private Link not supported on the global Azure IR; use managed VNET or self-hosted.)

Ideal company / skill level:

• Teams operating primarily on Azure (SaaS + Azure data stores) with data engineering capacity to design and tune pipelines.

Pricing:

• Pay-as-you-go consumption; charges for pipeline/orchestration runs, data movement (DIU-hours), Data Flow compute, and optional managed VNET. Examples for better understanding of pricing here and here.

12. Google Cloud Dataflow

Best for: Engineering teams that want fully managed, autoscaling batch/stream processing on Google Cloud using Apache Beam.

Standout features:

• Executes Apache Beam pipelines; SDKs for Java, Python, and Go, with portability to other runners (Apache Flink/Spark).
• Serverless autoscaling for batch and streaming; options like Streaming Engine, Dataflow Shuffle, and FlexRS for cost/perf tuning.
• Broad I/O connector ecosystem via Beam to Google Cloud technology and third-party systems.

Limitations / trade-offs:

• Requires coding in Beam; steeper learning curve than low-code ELT tools.
• Costs vary with job duration, autoscaling behavior

Ideal company / skill level:

• Mid-to-large teams building real-time or large-scale batch pipelines within a Google Cloud stack.

Pricing:

• Pay-as-you-use based on worker resources and features (eg. DPUs/streaming units, shuffle). New customers typically get $300 in Google Cloud credits.

13. Stitch

Best for: Teams that want a simple, SaaS ELT service with row-based pricing and a quick way to pipe common SaaS/DB sources into a warehouse.

Standout features:

• Cloud ELT is built on the Singer spec; a broad catalog of prebuilt source connectors and common warehouse destinations.
• Managed pipelines with scheduling, logging, and REST Connect API for automation.
• 14-day free trial to test sources/destinations.

Limitations / trade-offs:

• Focused on extract/load with simple data transformations only; complex business logic happens downstream (dbt/SQL).
• Row-based pricing can rise with high data volumes or frequent updates.
• Connector coverage/quality varies between native and community ecosystems.

Ideal company / skill level:

• Startup to mid-market teams that prefer low-ops ELT and can model data post-load in the warehouse.

Pricing:

• Standard plans start at $100/month (row-tiered); Advanced ($1,250/month, billed annually), and Premium ($2,500/month, billed annually). All plans include a 14-day trial.

14. Informatica PowerCenter

Best for: Large enterprises with mature, on-premises ETL estates that need tight operational control and are planning a staged migration to the cloud.

Standout features:

• Proven on-premises ETL suite with visual mapping, scheduling, and broad connectivity; long enterprise footprint.
• Clear modernization paths to Intelligent Data Management Cloud (IDMC) or PowerCenter Cloud Edition to reuse assets during cloud moves.

Limitations / trade-offs:

• The product is in support wind-down: PowerCenter 10.4 was moved to extended support in 2024, and PowerCenter 10.5 standard support ends March 31, 2026 (extended support thereafter). Plan for migrations.
• Higher infrastructure and admin overhead than managed cloud ELT; upgrades/migrations can be complex.

Ideal company / skill level:

• Enterprises with experienced data engineering/operations teams and regulatory or latency reasons to keep workloads on-premises while transitioning.

Pricing:

• Quote-based enterprise licensing.
• Informatica offers 30-day trials for its cloud data integration products (separate from PowerCenter).

Key factors to consider when evaluating ETL software tools

Choosing an ETL tool is a crucial business decision. Focus on factors that most impact reliability, cost, and team effort.

• Use case: Match the tool to your workloads and SLAs; avoid enterprise complexity if needs are simple.
• Data connectors: Confirm coverage for your sources/destinations and the effort to add new ones.
• Interface and usability: Who will build/operate the pipelines? Favor fast setup and clear debugging over vague “ease of use”
• Scalability: Assess performance at higher volumes and concurrency; look for schema evolution and job retries.
• Latency: If near-real-time processing matters, check CDC support, scheduling options, and typical end-to-end lag.
• Pricing and total cost: Consider licensing plus cloud compute, storage, and ongoing engineering time.
• Monitoring, data security, and compliance: Require run-level logs/alerts, role-based access, data encryption, and relevant certifications.

See how Fivetran fits into your stack

The right platform depends on your sources, latency needs, scale, and the time your team can dedicate to maintenance. Use the summaries above to narrow a shortlist, then validate with a small proof of concept focused on data freshness, failure handling, and total cost.

If you want fully managed ELT with broad connector coverage and automated schema handling with minimal overhead, consider Fivetran.
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Frequently asked questions (FAQ)

Q: What’s the difference between ETL and ELT tools?

A: ETL transforms data before loading it into a warehouse. ELT tools load raw data first, then transform it in-destination (SQL/dbt). ELT reduces pipeline fragility, scales with warehouse compute, and preserves raw fidelity for new questions—useful when sources change frequently.

Q: How should I estimate total cost?

A: Look beyond license fees. Include cloud compute/storage, data egress, connector maintenance, schema-change handling, observability, and on-call time. For row- or credit-based pricing, model typical daily change volume and burst scenarios. Run a 2 - 3 week POC (proof of concept) and compare real job logs against estimates.

Q: When does it make sense to build my own pipelines?

A: Build if you have unique sources, strict on-prem constraints, or specialized latency/SLA needs. Buy when you need breadth of connectors, faster time to value, and less maintenance. Many teams use a hybrid: managed ELT tools for common data sources, and custom code for edge cases.

Q: What should a good POC include?
A: Test one or two priority sources end-to-end. Measure setup time, initial/full load duration, incremental latency, failure behavior (retries, alerts), schema-change handling, and downstream model compatibility. Document costs and ops effort to align tool choice with your SLAs and budget.