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.

What should you consider when choosing an ETL tool?

When evaluating ETL tools, there are several key factors to consider:

• Connectors and data sources: Look for tools that support the data sources and destinations you use.
• Ease of Use: Consider the user interface and how easy it is to set up and manage pipelines. Some tools are more developer-focused, while others offer no-code or low-code options.
• Transformation and Capabilities: Evaluate how the tool handles data transformations. Does it support SQL, visual transformations, or custom code? Make sure it fits your team's skill set.
• Scalability: Ensure the tool can handle your current data volume and scale as your business grows. Look for features like auto-scaling and performance optimization.
• Pricing: Understand the pricing model and how it aligns with your budget. Some tools charge based on data volume, number of connectors, or users.

Try to take advantage of free trials or demos to get a hands-on feel for the tool before committing.

Quick Comparison Table

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

📝 G2 reviews

Pros:

• ETL + reverse ETL in one
• User-friendly and easy to set up
• Flat monthly pricing model
• 200+ connectors (Shopify, HubSpot, etc.)
• AI-powered metric creation
• Lineage, orchestration, and workflow
• Advanced transformation and SQL modeling capabilities

Cons:

• Limited deep customization for complex pipelines
• Focused on cloud data warehouses
• Requires some technical knowledge around data warehousing and SQL

Pricing: $79 / 5M Active Rows

2. Airbyte

Airbyte is a widely used open-source platform for data integration, especially favored by teams building a modern data stack. It stands out for its extensive library of ELT connectors and the flexibility to create custom connectors within the platform. Unlike many no-code ELT tools, Airbyte allows for deeper customization—but this capability is best suited for data professionals with strong coding skills, as building custom pipelines requires programming expertise.

🔗 airbyte.com

📝 G2 Reviews

Pros:

• 550+ connectors
• Open-source + managed cloud version
• Capacity-based pricing (2025)
• Python SDK & low-code connector builder

Cons:

• Self-hosted version requires require more maintenance
• More suited for advanced teams
• The quality of connectors can vary because of open-source
• High dependence on community

Pricing: $2.50/credit (one million rows = 6 credits; 1 GB = 4 credits)

3. Fivetran

Fivetran is a cloud-based ELT platform that automates data integration from various sources into data warehouses like BigQuery, Snowflake, and Redshift. With reliable, pre-built connectors and minimal setup, it efficiently handles data extraction and loading, making it ideal for teams seeking a low-maintenance, quick-to-deploy solution without heavy engineering effort.

🔗 fivetran.com

📝 G2 reviews

Pros:

• Fully automated
• Schema drift handling
• Wide variety of connectors
• Robust security protocols
• Detailed and helpful documentation
• Near real-time replication capabilities

Cons:

• Complex and expensive pricing model
• Depends on external tools for data transformations (e.g., DBT)
• Doesn't support data transformations pre-load
• No AI assistant or advanced automation features
• Steep learning curve for DBT beginners

Pricing: Usage-based, starting $500 for 1 million MARs (no fixed base)

4. Hevo Data

Hevo is a cloud-based data integration platform that automates the movement of data from various sources, including SaaS applications, databases, cloud storage, and streaming services, into data warehouses. It helps organizations streamline their analytics and operational workflows by preparing data for use without requiring complex custom infrastructure. With its no-code interface and automation features, Hevo reduces the need for engineering effort, making data integration faster and more efficient.

🔗 hevodata.com

📝 G2 Reviews

Pros:

• Supports both ELT, ELT, and reverse ELT
• Plenty of fully maintained connectors
• Great for non-technical users
• Simple UI that's easy to work with
• Affordable pricing

Cons:

• Limited features for more advanced use cases
• Limited custom scheduling features
• Only 50 connectors are available on the Free plan
• Lack of flexibility when wanting to edit pipelines
• Error messages and status codes could be better

Pricing: $299 / 5M rows

5. Estuary

Estuary Flow is a real-time ETL/ELT and data integration platform for both batch and streaming pipelines. It provides sub-100ms latency using Change Data Capture (CDC), supports automated schema evolution, and allows users to build entire pipelines with low- or no-code connectors in minutes. It can target data warehouses (e.g., Snowflake, BigQuery), BI tools, and operational systems for analytics, operations, and AI use cases.

🔗 estuary.dev

📝 G2 Reviews

Pros:

• Real-time data sync
• Change Data Capture (CDC) support
• Easy UI for event workflows
• Automatic schema evolution with exactly-once delivery guarantees.
• 200+ no-code connectors for databases, SaaS apps, and message queues.

Cons:

• Smaller community
• Requires event-oriented thinking
• Still growing connector catalog; niche or very new APIs may require custom work.
• Premium pricing model can be expensive for small teams.

Pricing: $0.50/GB consumed + per-connector fee

6. Matillion

Matillion is a modern data integration tool built for the cloud, helping businesses create and run ETL/ELT pipelines with ease. It offers a user-friendly, low-code interface to connect data from multiple sources and process it within cloud data platforms like Snowflake, BigQuery, and Redshift. With built-in features for scheduling, automation, and monitoring, Matillion supports complex data workflows and is ideal for companies managing large-scale data operations.

🔗 matillion.com

📝 Gartner Reviews

Pros:

• dbt integration
• Large number of connectors
• On premise options
• Has both ELT and ETL capabilities

Cons:

• Usage-based pricing can spike
• Higher learning curve for small teams
• Requires a large upfront investment and implementation

Pricing: $2.00 per credit

7. Segment

Segment, acquired by Twilio in 2020, is a leading Customer Data Platform (CDP) that helps businesses collect and unify customer data in real time. It builds unified profiles using event data from web, mobile, and servers, enabling audience segmentation and integration with marketing, analytics, and CRM tools. With strong privacy controls and support for reverse ETL and data warehouses, Segment enables personalized, data-driven engagement at scale

🔗 segment.com

📝 G2 Reviews

Pros:

• Real-time data integration capabilities
• Pre-built and maintained connectors for popular data sources
• Has advanced features for managing customer data
• Easy to setup and use

Cons:

• Quickly becomes very expensive
• Not suitable for only use for ELT
• Heavily skewed toward sales and marketing platforms
• Custom integration or customization is really hard to do

Pricing: $120/month 10,000 visitors

8. Keboola

Keboola is a cloud-based data platform for building and managing complete data pipelines. It supports both code and no-code workflows, offers 700+ data connectors, and includes built-in features for versioning, auditing, and cost monitoring. Designed for flexibility and scale, it's ideal for teams that need customizable, end-to-end data operations in one place.

🔗 keboola.com

📝 G2 Reviews

Pros:

• A very wide range of features
• Option to build custom components with API first approach
• Transformations that support both ELT and ETL
• Good academy to learn

Cons:

• Complex UI
• Orchestration features are lacking
• Can quickly become expensive with the credit consumption pricing model
• Smaller user base

Pricing: Custom pricing

9. Qlik Talend

Talend’s data integration tool is part of the broader Talend Data Fabric platform, which also includes data quality, governance, and API integration. Supporting both ETL and ELT, it’s well-suited for hybrid data environments. Designed for large enterprises, Talend is best for experienced users and mature data teams.

🔗 talend.com

📝 Gartner Reviews

Pros:

• Large number of connectors
• Robust feature set
• Lots of advanced features for larger enterprises and data teams
• Has both ELT and ETL capabilities
• On premise options

Cons:

• Expensive for small businesses,
• Steep learning curve for non-technical users
• Requires a large upfront investment and implementation

Pricing: Custom pricing

10. Meltano

Meltano is an open-source data integration platform like Airbyte, enabling businesses to build and manage data pipelines. It offers numerous connectors for databases, APIs, and logs, along with strong transformation and orchestration features. Meltano integrates smoothly with cloud data warehouses, making it a flexible choice for modern data teams.

🔗 meltano.com

📝 G2 Reviews

Pros:

• CLI-first + version-controlled
• Open-source & modular
• Dev-friendly for custom pipelines
• Offer an SDK to more easily build Singer taps and targets

Cons:

• Steep learning curve for non-devs
• Requires manual deployment
• Limited data transformation capabilities (only through deep integration with DBT)
• Requires high maintenance

Pricing: Free (self-hosted), custom (managed), paid support packages

11. Rivery

Rivery is a SaaS ELT data integration platform that simplifies loading data from various sources, including custom APIs, into your data warehouse. While it doesn’t support real-time transformations during loading, it offers strong post-load transformation capabilities to prepare data for analysis. With its easy-to-use interface and automation features, Rivery helps teams streamline their data workflows without extensive coding.

🔗 rivery.io

📝 G2 Reviews

Pros:

• Supports custom integrations though native GUI
• Has reverse ETL option
• Supports Python
• Has data transformation capabilities
• Great customer support

Cons:

• Lack of advanced error handling features
• Cannot transform data on the fly (ETL)
• Complex pricing model
• UI is lacking when working with larger complex pipelines
• Product documentation is lacking

Pricing: $0.75 per credit 100MB of data replication

12. Azure Data Factory

Azure Data Factory (ADF) is Microsoft’s cloud-based data integration service for creating ETL/ELT pipelines. ADF supports a drag-and-drop pipeline designer, over 90 built-in connectors for Azure, on-premises, and SaaS data sources, and can execute transformations via Azure Databricks, U-SQL, or stored procedures. It also includes features for data orchestration, monitoring, and hybrid data integration scenarios.

🔗 azure.microsoft.com

📝 Gartner Reviews

Pros:

• Scales well in Azure environments
• Rich native connectors
• SSIS support
• Good for hybrid cloud/on-premises
• Strong security and compliance

Cons:

• Complex interface
• Charges per pipeline activity, per DIU for data flows, and for data movement across regions
• Error messages can be vague
• Azure-specific quirks may require specialized knowledge
• UI can be slow when working with large pipelines

Pricing: Pay per activity run + data movement; starts ~$0.25 per DIU-hour for data flows

13. AWS Glue

AWS Glue is a fully managed, serverless ETL service provided by Amazon Web Services. It automatically discovers and catalogs metadata (Glue Data Catalog), generates ETL code in PySpark, and runs jobs on demand or schedules them. Glue integrates natively with AWS data stores (S3, Redshift, RDS, DynamoDB) and third-party sources via JDBC.

🔗 aws.amazon.com/glue

📝 G2 Reviews

Pros:

• Serverless, no infrastructure to manage; Glue provisions compute as needed (Apache Spark under the hood).
• Built-in Data Catalog for schema discovery, versioning, and integration with Athena and Redshift Spectrum.
• Supports Python (PySpark) and Scala ETL scripts with mapping and transformation APIs for complex logic.
• Deep integration with AWS ecosystem (CloudWatch monitoring, IAM for security, S3 triggers).

Cons:

• Cost can be unpredictable for long-running or high-concurrency jobs (billed per Data Processing Unit-hour).
• Debugging PySpark jobs in Glue requires jumping between AWS console logs and code; local testing is limited compared to local Spark.
• On-premises or multi-cloud data sources require additional setup (Glue has JDBC connectors but network config can be complex).

Pricing: $0.44 per DPUs-hour (development endpoints) + per-job costs

14. Skyvia

Skyvia is a cloud data platform that offers ETL, data replication, backup, and integration services via a web interface. It supports over 70 data sources (CRM, marketing, databases) and can load data into major data warehouses (Snowflake, BigQuery, Redshift) or cloud databases. Skyvia allows users to create simple ETL packages or schedule one-time and recurring data imports without coding.

🔗 skyvia.com

📝 G2 Reviews

Pros:

• Fast, no-code setup for loading data from 70+ sources to warehouses or cloud DBs.
• Handles incremental loads and can auto-detect schema changes for many sources.
• Built-in data replication (one-way sync) and backup options for cloud data.
• Free tier available (limited rows and sources) for basic usage.

Cons:

• No advanced transformation engine—only simple filters, mappings, and formula fields
• Pricing based on rows and connectors; high-volume loads can be costly.
• Support and community resources are limited compared to major ETL vendors.

Pricing: Free (limited); paid plans from $15/month for 10k rows

15. Portable.io

Portable.io is a cloud-based ETL service specializing in “long-tail” connectors - niche APIs that other platforms rarely support. It offers over 1,000 pre-built connectors, and if a required connector isn’t available, their team will build it on-demand at no extra cost. Portable focuses exclusively on extract-and-load into data warehouses, with a flat per-connector pricing model.

🔗 portable.io

📝 G2 Reviews

Pros:

• Unmatched connector breadth: 1,000+ connectors for niche and popular sources
• On-demand custom connector development at no additional cost
• Flat per-connector pricing; no volume-based fees
• Fully managed - Portable handles API changes, schema updates, and pipeline maintenance
• Set-and-forget simplicity with minimal configuration needed

Cons:

• EL-only (no in-platform transformations)
• Cloud-only SaaS (no on-prem option)
• No reverse ETL or activation features—it only loads to warehouses
• Some new connectors may require initial tuning if usage is low until fully hardened
• Limited scheduling granularity (mostly daily or on-demand syncs out of the box)

Pricing: Flat per connector (no volume fees)

16. Integrate.io

Integrate.io (formerly Xplenty) is a unified low-code/no-code data integration platform offering ETL, ELT, and reverse ETL in one solution. It provides a drag-and-drop UI for designing pipelines, supports on-prem agents for hybrid environments, and covers over 100 pre-built connectors for operational and analytical data flows.

🔗 integrate.io

📝 G2 Reviews

Pros:

• 100+ pre-built connectors covering both operational (reverse ETL) and analytical use cases
• Low-code visual pipeline builder with rich transformation expressions
• Supports hybrid deployments via secure agent for on-prem sources
• Unified platform for ETL, ELT, and reverse ETL
• Robust workflow orchestration and scheduling features

Cons:

• Cloud-only SaaS (no fully on-prem option)
• UI can feel complex initially due to breadth of features
• Less polished transformation debugging compared to dedicated tools like Matillion
• Pricing can be high for small teams; custom quotes required
• Documentation sometimes lagging on newer features

Pricing: Custom, based on connectors & volume

17. Dataddo

Dataddo is a no-code data integration platform built for business users and marketers, offering over 300 prebuilt connectors for ETL, ELT, reverse ETL, and dashboard integrations. It automatically handles API changes, monitors pipelines, and includes SmartCache to store data without needing a warehouse. With custom connector support, strong security, and simple pricing, Dataddo is a flexible, low-maintenance solution for fast, reliable data access.

🔗 dataddo.com

📝 G2 Reviews

Pros:

• No-code interface makes setup simple for non-technical users.
• Integrates with 300+ platforms, including many marketing and CRM tools.
• Onboarding and connector requests are generally well-handled.
• Offers competitive pricing, especially for small teams.

Cons:

• Some users report delays for complex issues.
• New or niche sources may not be instantly available.
• Cancelling or modifying plans can be frustrating.

Pricing: $99 / month for 3 data flows to sync data between any source and destination.

18. dltHub

Dlt is an open-source Python library for building data pipelines with a code-first approach. It provides pre-built connectors for many common data sources and handles schema inference, incremental loading, and retry logic automatically. Developers define pipelines in Python, making it highly flexible and embeddable in any orchestration environment.

🔗 dlthub.com

📝 Medium Review

Pros:

• Open-source and free to use
• High flexibility and control via Python code
• 60+ pre-built connectors with automatic schema evolution
• Built-in incremental loading and state management
• Embeddable in any orchestration (Airflow, Prefect, cron, etc.)

Cons:

• No graphical UI—code-first, so not accessible to non-developers
• Requires engineering effort to deploy and schedule (no managed SaaS)
• Limited built-in transformations compared to dedicated ETL tools
• Monitoring and observability must be built around code (no native dashboard)
• Smaller community and support compared to more established tools

Pricing: Free (open-source)

19. Rudderstack

RudderStack is an open-source Customer Data Platform (CDP) built for developers and data teams. It lets you collect and route customer event data from web, mobile, and server sources to various destinations like data warehouses, analytics tools, and marketing platforms. With support for real-time and batch processing, RudderStack integrates easily into modern data stacks and emphasizes a warehouse-first approach, giving teams greater control, flexibility, and data privacy compared to traditional CDPs.

🔗 rudderstack.com

📝 G2 Reviews

Pros:

• Developer-focused and highly flexible
• Reliable event capture and fast warehouse integration
• Robust support and onboarding for SMBs/mid-market
• Supports 200+ cloud destination

Cons:

• Less intuitive for non-tech users
• Reverse ETL and cohort building still lag competitors like Hightouch
• Documentation and alerts need improvement, some users report steep onboarding

Pricing: free for 250,000 monthly events, and starter plan at $200/month for 1 million events

20. Informatica

Informatica is an enterprise-grade data integration platform known for its strong ETL, data quality, and governance capabilities. It offers two main products: PowerCenter for on-premises data workflows, and the Intelligent Data Management Cloud (IDMC) for modern, cloud-native integration across hybrid environments. Supporting batch and real-time use cases, Informatica is recognized as a leader in Gartner’s Magic Quadrant and is widely used by enterprises for end-to-end data management.

🔗 informatica.com

📝 Gartner reviews

Pros:

• Enterprise-grade capabilities
• Strong data quality features
• Cloud integration; scalable.
• More than 1,200 pre-built connectors

Cons:

• Can be expensive for small to mid-sized businesses
• Complex features and architecture require specialized skills
• Can demand significant infrastructure and setup

Pricing: PowerCenter has enterprise licensing (six-figure annual contracts); Cloud is subscription-based (custom quotes); typically starts with apprx. $20k/year for base ETL usage.

21. CloverDx

CloverDX is an enterprise-grade ETL/ELT platform that emphasizes flexibility, automation, and scalability in designing complex data workflows. It supports both code-based and GUI-driven development, making it suitable for both developers and data engineers. It is also known for its transformation, data quality, and data migration capabilities. CloverDX helps to deliver a seamless onboarding process for clients, saving hours of manual work with automated conversion from any file format.

🔗 cloverdx.com

📝 Gartner Reviews

Pros:

• Metadata-driven: automatic handling of schema drift and impact analysis across pipelines.
• Visual Graphical Data Mixer for building data flows, with reusable subgraphs and components.
• Supports both batch and streaming ingestion, with connectors to databases, cloud storage, Hadoop, and REST APIs.
• Built-in scheduling, monitoring dashboards, alerting, and role-based access control.

Cons:

• High licensing costs make it less suitable for smaller teams or startups.
• Designer IDE can feel heavy and less intuitive for simple tasks; learning curve for new users.
• Less community presence than open-source tools, so third-party resources and tutorials are limited.

Pricing: Subscription or perpetual licensing (custom quotes, typically $20k+ annually)

22. Microsoft SQL Server Integration Services (SSIS)

SQL Server Integration Services (SSIS) is Microsoft’s enterprise ETL tool for automating data workflows such as data warehouse loads, file transfers, and data cleansing. It is primarily on-premises, included with SQL Server (starting at $3,945 for Standard), but can also run in the cloud via Azure Data Factory, with pricing from $1.16/hour using Azure Hybrid Benefit. SSIS supports a wide range of sources (databases, files, APIs) and offers robust features like control and data flow design, transformations, error handling, and job scheduling through SQL Server Agent. Development is done in Visual Studio using SQL Server Data Tools (SSDT).

🔗 microsoft.com

📝 Trustradius reviews

Pros:

• Works natively with SQL Server, Azure, and Power BI (ideal for Microsoft-centric environments)
• Perform complex transformations to prepare high-quality, analytics-ready data
• Integrate advanced logic using C# or VB.NET for tailored data processing
• Built-in features for logging, error capture, and process recovery ensure reliable performance

Cons:

• Comes with only 20+ connectors; others (e.g., Oracle, SAP BI) require separate downloads
• Limited or no native support for non-Windows platforms or cloud-native workflows
• The SSIS GUI (in Visual Studio) can be unintuitive for new users, and it may require strong understanding of SQL Server and the SSIS architecture
• Designed for batch ETL processes; real-time data integration is not natively supported

Pricing: Pricing is based on the chosen VM size and licensing model (pay-as-you-go or Azure Hybrid Benefit)

24. IBM InfoSphere DataStage

IBM DataStage (part of IBM InfoSphere Information Server) is a high-performance ETL and data integration platform that supports parallel processing and massive data volumes. It provides a visual design interface (DataStage Designer) to build data flows, along with features for metadata management, data lineage, and enterprise governance. DataStage can run on-premise or on cloud (via IBM Cloud Pak for Data) and integrates with IBM’s data quality and master data management solutions.

🔗 ibm.com

📝G2 reviews

Pros:

• Parallel processing engine for high-throughput ETL, optimized for large data volumes.
• Robust metadata management, data lineage, and governance via InfoSphere platform integration.
• Supports on-premise, virtualized, and containerized (Cloud Pak) deployments for flexibility.
• Extensive transformation library (data cleansing, lookups, joins) and connectivity (files, databases, mainframes, Hadoop).

Cons:

• It offers slightly fewer than 100 connectors and has limited native support for modern SaaS applications
• IBM InfoSphere DataStage carries a significant cost, with pricing typically tailored to the organization's size and needs
• While powerful, DataStage is complex and may require substantial ramp-up time, especially for smaller teams or users unfamiliar with enterprise-grade ETL tools
• Setting up and managing DataStage can be time-intensive, often requiring specialized expertise to configure and maintain performance across large, multi-platform environments

Pricing: Enterprise licensing (custom quotes, usually six-figure annual)

25. Oracle Data Integrator (ODI)

Oracle Data Integrator (ODI) is a powerful ELT platform that pushes data transformations to the target database for faster, scalable integration. It supports many data sources and is ideal for organizations using Oracle technologies, offering competitive pricing and strong Oracle ecosystem integration. Compared to tools like Informatica and Talend, ODI excels in efficient data processing but has fewer built-in governance features. It supports on-premises, cloud, and hybrid deployments, making it flexible for various environments. Overall, ODI is well-suited for enterprises needing robust, scalable ELT solutions within Oracle infrastructures.

🔗 oracle.com

📝 G2 reviews

Pros:

• ODI supports a wide range of data sources and targets, including both Oracle and non-Oracle technologies
• ODI utilizes Knowledge Modules (KMs) that enable reusable integration strategies, simplifying development and maintenance
• ODI provides robust error monitoring capabilities, aiding in the identification and resolution of issues during data integration processes

Cons:

• ODI integrates best with Oracle technologies; setup can be less intuitive for non-Oracle environments and requires additional configuration (e.g., drivers for Snowflake or adapters for applications)
• While cost-effective for large deployments, the licensing and implementation costs can be a barrier for smaller projects or organizations with limited budgets
• ODI is primarily designed for batch data integration and may not suit real-time or near real-time data integration needs

Pricing: Oracle Data Integrator (ODI) pricing is roughly $30,000 per processor or $900 per user, plus extra for updates and support.

26. SAP Data Services

SAP Data Services is an enterprise-grade data integration, ETL, and data quality platform. It’s part of SAP Business Technology Platform and functions on-premises or in private cloud/IaaS environments. SAP Data Services can integrate and transform structured/unstructured data from SAP and non-SAP sources (e.g. relational databases, files, big data platforms). It is a proprietary solution with licensing costs that can vary based on deployment scale and specific requirements. For detailed pricing information, prospective customers are encouraged to contact SAP directly or consult with authorized SAP partners.

🔗 sap.com

📝 Gartner Reviews

Pros:

• It is deeply integrated with SAP systems, making it ideal for data migration and integration within the SAP ecosystem
• While some find the initial setup complex, the user interface is generally considered user-friendly, especially for those with some technical expertise
• Can handle large data volumes and is designed to scale with growing business needs

Cons:

• While cost-effective for large deployments, the licensing and implementation costs can be a barrier for smaller projects or organizations with limited budgets
• While versatile, its extensibility may be limited compared to other open-source tools, especially in non-SAP environments

Pricing: SAP Data Services pricing varies by deployment but typically starts around $150 per quarter for shared servers, with higher costs for dedicated or cloud-based options.

27. Google Cloud Data Fusion

Cloud Data Fusion is a fully managed, no-code/low-code data integration service. It’s built on the open-source CDAP platform and helps users design, build, and manage ETL/ELT pipelines through a visual interface. It’s ideal for integrating data from multiple sources without needing deep coding skills. Its visual, user-friendly interface supports both technical and non-technical users. Its key advantages include pre-built transformations, reusable components, and support for real-time data processing. Compared to other ETL solutions, it stands out for its intuitive design, scalability, and collaborative features.

🔗 cloud.google.com

📝 Gartner Reviews

Pros:

• Foster teamwork and maintain data quality standards with a centralized platform that enables shared pipeline creation, version control, and access management
• Handle growing data volumes and evolving business needs seamlessly with a scalable architecture that accommodates both batch and real-time processing
• Seamlessly integrates with Google Cloud Services such as BigQuery, Google Cloud Storage, and Pub/Sub
• Pricing is usage-based, allowing flexibility as data demands grow

Cons:

• Reliance on pre-built plugins and connectors can restrict flexibility for highly customized or niche use cases
• Despite having a visual interface, concepts and more complex data transformations can be initially challenging for those unfamiliar with data pipelines or Apache Beam
• The pricing model can become expensive for large-scale or complex deployments, especially with large-scale data operations

Pricing: pricing includes development costs from $0.35 to $4.20 per hour depending on edition, plus additional charges for the Dataproc clusters used during pipeline execution.

28. Stitch

Stitch is a cloud-based ETL tool that simplifies data integration by extracting data from various sources and loading it into a warehouse or cloud storage. It features an easy setup, 130+ pre-built connectors, and a real-time data pipeline. Automatic schema mapping reduces manual work and errors. Its intuitive interface suits both technical and non-technical users. Stitch enables fast, reliable, and scalable data movement.

🔗 stitchdata.com

📝 G2 Reviews

Pros:

• Stitch is built on the Singer framework, enabling users to tap into a wide range of open-source connectors. This offers flexibility for integrating data across platforms such as Meltano, Airbyte, and Estuary
• Stitch retains encrypted logs for up to 60 days, allowing users to monitor data flows and troubleshoot issues
• For those in the Qlik ecosystem, Stitch integrates seamlessly with other Qlik products, delivering a unified and streamlined data management experience

Cons:

• Stitch supports just over 140 data sources and 11 destinations, which is lower compared to other platforms. While there are over 200 singer taps in total, their quality levels vary
• Price can quickly rise quickly from basic plan to advanced ($1,250+ per month) and premium ($2,500 per month).
• It can also pose restrictions on the volume of data that can be processed, which can be a concern for businesses dealing with large datasets

Pricing: $100 / 5M rows

29. Qlik Replicate

Qlik Replicate (previously known as Attunity Replicate), is a data replication and ingestion tool that automates the process of moving data between various databases, data warehouses, and big data platforms. It supports both snapshot and incremental replication, including real-time transactional and batch-optimized replication. Qlik Replicate utilizes log-based change data capture technology and provides a user-friendly interface for managing data replication tasks.

🔗 qlik.com

📝 Gartner Reviews

Pros:

• High-performance CDC with minimal source impact; supports heterogeneous sources and targets.
• Automated schema change handling—table/column additions in source auto-reflected in target.
• GUI-based configuration for tasks, monitoring dashboards, and robust error handling.
• Cloud-native or on-prem installations; integrates with Qlik’s broader ecosystem (e.g., Qlik Sense).

Cons:

• No built-in ELT/transformations—only replication. Users need a separate tool for data transformations.
• Enterprise pricing (per-core licensing) can be high, particularly for large-scale replication across many tables.
• Learning curve for setting up advanced replication scenarios (e.g., multi-target replication, filters).

Pricing: Subscription/perpetual license (custom quotes; six-figure enterprise costs)

30. Striim

Striim is a real-time data integration and complex streaming platform known for its low-latency, enterprise-grade features, and comprehensive integrations. Striim uses Change Data Capture to move data in real-time and handle analytics. Over time, it has grown to support many connectors for different use cases. It is well-known for its CDC features and strong support for Oracle databases. Striim competes with tools like Debezium and Estuary, especially in scalability. It is a top choice for environments that need both real-time and batch data processing.

🔗 striim.com

📝 G2 Reviews

Pros:

• Similar to other top vendor like Estuary, Striim is built to handle large-scale data replication, making it a suitable choice for organizations with extensive data processing needs
• Striim combines stream processing with data integration, enabling organizations to handle both data replication and real-time analytics within the same platform, as well as perform incremental batch replication, loading snapshots and syncing changes at scheduled intervals

Cons:

• It’s stream processing platform makes it more challenging to learn compared to other ETL/ELT tools. It also operated by Tungsten Query Language which is not as user-friendly as SQL
• Build and script the entire CDC process yourself, which can be powerful but also more complex and time-consuming, writing custom TQL custom scripts adds complexity

Pricing: Custom pricing

31. Apache Kafka

A distributed Apache Kafka is a distributed, open-source event streaming platform built for high-throughput, low-latency data pipelines and real-time processing. It enables applications to publish, subscribe to, and store event streams reliably at scale. Kafka is commonly used with ETL/ELT tools for ingesting and transporting data to warehouses or analytics systems. With features like replication, partitioning, and built-in stream processing (Kafka Streams), it offers strong scalability and fault tolerance, making it a key component in modern data architectures.

🔗 kafka.apache.org

📝 Trustradius reviews

Pros:

• Widely used across industries for real-time analytics, event streaming, monitoring, and data integration - good choice for managing streaming data
• Uses horizontal scaling, and is powerful in handling BigData
• Kafka replicates data across nodes, ensuring reliability even in case of hardware failure.

Cons:

• Deploying and managing Kafka clusters demands considerable technical expertise
• Kafka's distributed architecture can lead to increased management effort and operational costs at scale
• Kafka handles streaming well, but needs tools like Kafka Connect, or custom code with Kafka Streams to do data extraction, transformation, and loading tasks

Pricing: Open source and free to use

32. Debezium

Debezium is an open-source Change Data Capture tool originated from RedHat. It is a set of distributed services that capture row-level changes in databases so that applications can see and respond to those changes. Debezium records in a transaction log all row-level changes committed to each database table. The primary use of Debezium is to enable applications to respond almost immediately whenever data in databases change.

🔗 debezium.io

📝 Medium review

Pros:

• Built on Kafka Connect, Debezium integrates natively with Apache Kafka, making it easy for teams already using Kafka to incorporate CDC into their data pipelines
• In Debezium they are used to capture the current state of a database table in smaller, manageable chunks, rather than doing a full-table snapshot all at once. This is especially useful when dealing with large datasets
• Reliable change capture

Cons:

• Debezium relies heavily on Kafka and Kafka Connect, which require significant setup, maintenance, and expertise, especially in distributed or production environments
• Debezium is designed for CDC from databases to Kafka. If you need batch data loads, non-CDC sources, or direct integration with tools outside the Kafka ecosystem, you’ll need custom pipelines or third-party tools
• Debezium doesn’t support historical data replay out-of-the-box. If Kafka retention expires or snapshots are needed, custom logic must be built to backfill or "time travel" through data

Pricing: Open source and free to use

33. SnapLogic

SnapLogic is an Integration Platform as a Service (iPaaS) offering ETL, ELT, and application integration via a visual “Snap” architecture. It includes over 500 Snap connectors for SaaS, on-premises, and big data sources. Pipelines are designed in a drag-and-drop interface (Snap Studio) and executed on a managed platform with autoscaling. SnapLogic also provides AI-driven suggestion features (SnapLogic Iris) to accelerate pipeline creation.

🔗 snaplogic.com

📝 Gartner Reviews

Pros:

• 500+ Snap connectors covering SaaS, databases, big data, and on-prem sources.
• Visual pipeline designer (Snap Studio) with AI-driven suggestions (Iris) for mapping and transformations.
• Serverless execution with autoscaling and multi-cloud support (AWS, Azure, GCP).
• Supports real-time streaming (buses), batch, and IoT/edge integrations.

Cons:

• Premium pricing (connector-based, usage-based) can be cost-prohibitive for SMBs.
• Designer interface can become cluttered when pipelines grow large; performance may degrade.
• Limited offline or self-hosted options; fully SaaS-based.

Pricing: Subscription (connector & usage-based; starts ~$50k/year)

34. Singer

Singer is an open-source standard for building simple, composable ETL data pipelines. By using a standardized format for writing scripts to extract data from sources Taps – extract data from sources (e.g., APIs, databases) Targets – load data into destinations (e.g., data warehouses). Taps and targets communicate using a JSON-based format via stdin/stdout, making them interoperable and modular. It’s particularly popular among developers and data engineers who need a flexible, customizable solution for creating data pipelines.

🔗 singer.io

📝 Stackshare reviews

Pros:

• Around 200 prebuilt taps and targets available, covering popular databases, SaaS applications, and other data sources, which can be easily integrated into your ETL pipeline
• Given it’s open-source nature, Singer allows developers to create custom taps and targets or modify existing ones to fit their unique data integration requirements
• The constant support and drive from the community ensures ongoing development

Cons:

• Singer was popular during the time Stitch was successful, but after Talend (and later Qlik) acquisitions, it became one of several overlapping tools within Qlik’s portfolio
• Requires technical expertise to set up and maintain, as it is primarily command-line and code-based, debugging and error handling can be more complex because of its open-source nature
• Limited automation capabilities compared to modern managed ETL solutions, requiring more manual intervention and custom scheduling

Pricing: Free (open-source)

35. Coalesce

Coalesce is a low-code, cloud-native data transformation platform combining the power of native SQL with a visual, column-aware GUI. Designed for modern data teams, it automates complex workflows, metadata, and column lineage while enabling version-controlled, template-based development. Its intuitive interface boosts productivity and governance without sacrificing flexibility. Ideal for data engineers and architects, Coalesce accelerates data preparation for analytics and AI.

🔗 coalesce.io

📝 Gartner Reviews

Pros:

• Intuitive, low-code interface that empowers users to build and manage data pipelines with minimal coding, while still allowing full SQL access for advanced users
• It accelerates data transformation and delivery, reducing manual coding and boosting productivity
• Template-based architecture ensures pipelines scale effortlessly with data volume and organizational growth

Cons:

• Even though the platform is low-code, it requires basic understanding of data warehousing and SQL to take full advantage of the platform
• Coalesce currently works best with Snowflake (using other platforms—like Databricks or Microsoft Fabric Coalesce might not work as smoothly or at all yet) This can be a drawback for organizations that use a mix of data platforms

Pricing: Custom pricing, generally considered premium

FAQ

What’s the difference between managed vs self-hosted ETL tools?

Bellow is a comparison table highlighting the key differences between managed ETL tools and open-source (self-hosted) ETL tools:

What are the main use cases for an ETL pipeline?

1. Data Migration: Moving data between systems, especially during system upgrades or replacements.
2. Data Warehousing: Consolidating data from multiple sources into a central repository for analysis and reporting.
3. Data Integration: Combining data from different sources to provide a unified view.
4. Data Transformation: Cleaning, enriching, and transforming data to meet business needs.
5. Automating manual workflows: Reducing human error and saving time by automating repetitive data tasks.

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.