Best ETL Tools in 2025
Choosing an ETL tool in 2025 isn’t just about moving data — it’s about syncing teams, scaling infrastructure, and enabling fast decisions. Whether you're building pipelines as a data engineer or looking to streamline reporting as an analyst, the ETL space is full of great tools — each with their own strengths.
What is ETL?
ETL stands for Extract, Transform, Load — a core data process used to bring together information from different systems and make it usable for reporting, analysis, and automation. It’s a foundation of modern data infrastructure and one of the most important building blocks for any business working with data.
Here’s how it works:
- Extract: First, data is pulled (or extracted) from various source systems. These can be anything from marketing platforms like HubSpot or Meta Ads to e-commerce tools like Shopify or Stripe. At this stage, the data is often messy, siloed, and structured in different ways depending on the tool.
- Transform: Once the raw data is extracted, it needs to be cleaned, restructured, and transformed. This could mean joining tables, renaming columns, fixing data types, or calculating new fields. The goal is to turn inconsistent data into something usable and trustworthy — often using tools like SQL or visual transformation layers.
- Load: Finally, the transformed data is loaded into a centralized data warehouse, such as Snowflake, BigQuery, or Redshift. Once it’s there, it becomes a single source of truth that teams can use for dashboards, reports, or powering automations.
Another version of this process is often called ELT, which flips the last two steps — bringing raw data into the warehouse first, then doing all the transformations inside the warehouse itself. This approach is common with today’s cloud-native tools because it’s faster, more flexible, and easier to scale.
Many companies also go a step further by enabling reverse ETL, which sends data back from the warehouse into everyday business tools. This means sales teams can see enriched customer data in their CRM, marketing teams can create better segments, and finance teams can automate reporting — all using the same trusted data.
In short, ETL helps you go from raw, scattered information to reliable, actionable insights. And the right ETL tool can save hours of manual work, reduce errors, and enable better decisions across the business.
Below you'll find a complete list of the top ETL tools in 2025, along with pros, cons, fresh reviews, and a final comparison to help you choose the right one.
Top ETL tools listed
1. Weld
Weld is a modern data platform built for teams that want to move fast without sacrificing clarity. It combines both ETL and reverse ETL in a single interface — letting you sync data from 200+ tools like Shopify, HubSpot, and Stripe into your warehouse, then push clean, modeled data back into business tools.
Weld stands out with a fixed monthly pricing model, minimal engineering setup, and an intuitive UI designed for both data teams and business users. It’s a great option for companies that want to get up and running quickly, without managing complex infrastructure.
🔗 weld.app
Pros:
- ETL + reverse ETL in one
- Flat monthly pricing model
- 200+ connectors (Shopify, HubSpot, etc.)
- AI-powered metric creation
Cons:
- Limited deep customization for complex pipelines
- Focused on cloud data warehouses
2. Airbyte
Airbyte is an open-source ETL platform with hundreds of prebuilt connectors and flexibility for teams who want to self-host or build their own.
Pros:
- 550+ connectors
- Open-source + managed cloud version
- Capacity-based pricing (2025)
- Python SDK & low-code connector builder
Cons:
- Self-hosted version requires dev resources
- UI less polished than Fivetran
3. Fivetran
Fivetran is a fully managed platform that automates the entire data pipeline and is widely used by enterprise teams who value ease over flexibility.
Pros:
- Fully automated
- 500+ prebuilt connectors
- Schema drift handling
Cons:
- Expensive, per-connection MAR pricing
- Limited transformation flexibility
📝 G2 Reviews
4. Hevo Data
Hevo is a no-code data pipeline tool that's great for startups and growing teams wanting real-time syncs with minimal setup.
Pros:
- Real-time syncing
- No-code setup
- Easy onboarding
Cons:
- Limited transformations
- Smaller connector library
5. Estuary
Estuary focuses on real-time, event-driven pipelines and is ideal for modern data engineering teams building around streaming use cases.
Pros:
- Real-time data sync
- Change Data Capture (CDC) support
- Easy UI for event workflows
Cons:
- Smaller community
- Requires event-oriented thinking
6. Matillion
Matillion is an ELT platform with a drag-and-drop interface and strong support for Snowflake, BigQuery, and other enterprise warehouses.
Pros:
- Visual UI for data pipelines
- dbt integration
- Scalable for enterprise workloads
Cons:
- Usage-based pricing can spike
- Higher learning curve for small teams
7. Segment
Segment is a customer data platform with strong event tracking, ETL-lite features, and native support for marketing and product analytics stacks.
Pros:
- Great for behavioral data
- 300+ integrations
- Built-in user identity resolution
Cons:
- Not a full ETL solution
- Expensive at scale
8. Keboola
Keboola is a modular data operations platform with pipeline orchestration, transformations, and Git-based version control.
Pros:
- Git integration
- Transformations + orchestration built-in
- Modular architecture
Cons:
- Less intuitive for beginners
- Smaller user base
9. Talend
Talend is one of the most mature enterprise data integration tools on the market, with both open-source and commercial offerings.
Pros:
- Great governance features
- Hybrid deployment options
- Enterprise-ready
Cons:
- UI feels dated
- Higher complexity
10. Meltano
Meltano is an open-source ELT platform built for engineers and GitOps workflows, powered by Singer taps and focused on developer control.
📝 G2 Reviews
Pros:
- CLI-first + version-controlled
- Open-source & modular
- Dev-friendly for custom pipelines
Cons:
- Steep learning curve for non-devs
- Requires manual deployment
11. Rivery
Rivery offers both ETL and reverse ETL with strong orchestration and support for SQL + Python transformations.
Pros:
- Code + no-code hybrid
- Supports reverse ETL
- Flexible pricing
Cons:
- UI can be overwhelming
- Smaller community
12. Azure Data Factory
ADF is Microsoft’s cloud-native ETL tool built for Azure-first teams with extensive integrations across the Microsoft ecosystem.
Pros:
- Scales well in Azure environments
- Rich native connectors
- SSIS support
Cons:
- Complex interface
- Azure-specific
13. AWS Glue
AWS Glue is Amazon’s fully managed serverless ETL platform, ideal for structured and semi-structured data within the AWS ecosystem.
Pros:
- Serverless and auto-scaling
- Strong Glue Data Catalog
- Deep AWS integration
Cons:
- Hard to debug
- Steep learning curve
14. Skyvia
Skyvia is a no-code integration tool best suited for data replication, backup, and small-team reporting needs.
Pros:
- Easy to use
- Cloud-based
- Affordable plans
Cons:
- Basic transformation support
- Limited monitoring
15. Portable.io
Portable.io focuses on long-tail connector support and makes it easy for teams to integrate niche apps quickly.
Pros:
- Fast connector deployment
- Handles obscure data sources
- Lightweight setup
Cons:
- Smaller user base
- Less built-in transformation logic
16. Integrate.io
Formerly Xplenty, Integrate.io is a visual ETL tool that balances ease of use with flexibility across sources and destinations.
Pros:
- Visual pipeline designer
- Supports scheduling and triggers
- Connects to most popular warehouses
Cons:
- Limited transparency in pricing
- Users report UI limitations
17. Dataddo
Dataddo is a no-code data integration platform geared toward business users and marketers.
Pros:
- Simple setup
- Affordable pricing
- Reverse ETL support
Cons:
- Not made for deep engineering use cases
- Less control over pipeline logic
18. dltHub
dltHub is a Python-native ELT tool that makes it easy to build maintainable pipelines directly in code.
📝 G2 Reviews
Pros:
- Built for Python teams
- Code-first with open-source backing
- Great for custom setups
Cons:
- No GUI
- Still maturing
🔍 Comparison Summary (2025)
| Tool | 🧩 Connectors | 🏠 Self-hosted | 🔁 Reverse ETL | 🧪 Open-source | ✅ Best For | ⭐ Notable Strengths |
|---|---|---|---|---|---|---|
| Weld | 200+ | ❌ | ✅ | ❌ | Sync + activate data, no-code teams | Flat pricing, AI-powered metrics |
| Airbyte | 550+ | ✅ | ❌ | ✅ | Custom setups, open-source users | Connector builder, large OSS community |
| Fivetran | 500+ | ❌ | ✅ | ❌ | Enterprises needing automation | Fully managed, reliable connectors |
| Hevo Data | 150+ | ❌ | ❌ | ❌ | Simple ETL setups, marketing teams | Real-time sync, intuitive UI |
| Estuary | 100+ | ❌ | ❌ | ❌ | CDC/streaming-first use cases | Streaming pipelines, fast ingestion |
| Matillion | 100+ | ❌ | ❌ | ❌ | ELT in Snowflake, Azure, AWS | UI + code workflows, enterprise scale |
| Segment | 300+ | ❌ | Limited | ❌ | Behavioral/customer event data | CDP-first, great for identity resolution |
| Keboola | 200+ | ✅ | ✅ | ❌ | Complex governance-heavy teams | GitOps, branching, automation |
| Talend | 900+ | ✅ | ❌ | ✅ (partial) | Enterprises with legacy integrations | Data quality tools, governance |
| Meltano | 300+ (Singer) | ✅ | ❌ | ✅ | Engineers, version-controlled pipelines | Dev-first, command-line + config-based |
| Azure Data Factory | 90+ | ❌ | ❌ | ❌ | Microsoft-native workflows | Deep Azure integrations |
| AWS Glue | 50+ | ❌ | ❌ | ❌ | Serverless Spark pipelines | Scales with AWS, supports large jobs |
| Skyvia | 80+ | ❌ | ✅ | ❌ | Quick setup for small teams | Easy dashboards, SQL & no-code UX |
| Portable.io | 1,000+ | ❌ | ❌ | ❌ | Long-tail SaaS source coverage | Custom connector requests in 48 hours |
| Integrate.io | 100+ | ❌ | ✅ | ❌ | No-code users, mid-size orgs | Drag-and-drop pipeline builder |
| Dataddo | 100+ | ❌ | ✅ | ❌ | Marketers + analytics teams | Visual UI, connectors to BI tools |
| dltHub | Custom/code | ✅ | ❌ | ✅ | Python engineers | Lightweight, fast, CLI-focused |
FAQ
What is the difference between ETL and ELT?
ETL (Extract, Transform, Load) transforms data before loading it. ELT loads raw data into the warehouse first, then transforms it. ELT is more common with modern cloud data stacks.
Is open-source better than managed ETL tools?
It depends on your team. Open-source (like Airbyte or Meltano) offers control and flexibility but needs engineering time. Managed tools (like Weld or Fivetran) offer speed and simplicity.
Which ETL tools support reverse ETL?
Weld, Rivery, Dataddo, and Fivetran support reverse ETL — pushing data back into tools like HubSpot, Salesforce, or Google Sheets.
Which tool is best for streaming data or CDC?
Estuary and AWS Glue are strong choices for real-time use cases and Change Data Capture.
How do I know which type of pricing fits me the best?
Finding the best pricing model for your needs can be challenging and is based on many factors, such as the volume of your data, syncing frequency, budget or monthly active rows.
