Top ETL & ELT Tools to Power Your Data Pipelines in 2025

TL;DR:

ETL (Extract–Transform–Load) and ELT (Extract–Load–Transform) tools are the backbone of modern data analytics. They connect your scattered apps, databases, and systems, then clean and shape data into analytics-ready formats. Whether you’re a startup moving SaaS data into a data warehouse, or a bank managing petabytes in hybrid clouds, choosing the right ETL tools save you from hidden costs, manual maintenance, cumbersome scripting, and pipeline fragility.

In this guide, we explore the best ETL and ELT tools of 2025—covering cloud-native favorites, open-source platforms, legacy workhorses, and streaming solutions.

What is ETL and ELT?

Think of ETL like the pipes and filters of your business data. ETL stands for extract, transform, load. It’s a three-step process that moves data from its original source to a destination, usually a data warehouse or data lake:

• The extract stage pulls information from different places, whether that’s a CRM, a SaaS application, or a legacy database. 
• The transform stage cleans, standardizes, and enriches that information so that identifiers like “customer_id” look the same across every system. 
• The load stage then moves this structured data into a central repository such as Snowflake, BigQuery, Redshift, or Databricks, where it can finally be put to work.

In modern cloud data stacks, ELT has become the default approach. Instead of transforming data before loading it, ELT loads raw data directly into the warehouse and then transforms it using the warehouse’s own computing power. This model is faster, more scalable, and more flexible than traditional ETL, making it the standard for most modern teams. Platforms like Coalesce take this a step further by combining ELT with automation, governance, and AI-driven transformations, ensuring pipelines are not only fast but also trustworthy and easy to manage at scale.

Why ETL tools matter in 2025?

The rise of SaaS applications, Iot devices, mobile apps, and other sources has left companies with data scattered across dozens of systems—Salesforce for sales, HubSpot for marketing, NetSuite for finance, and MySQL or Postgres for product data. Each becomes its own silo, making it hard to get a complete view of the business.

Legacy Approach: Custom Scripts

In the past, teams relied on custom scripts to stitch data together. But scripts are brittle, break whenever schemas change, and demand constant maintenance. This wastes engineering time and slows down analytics, leaving leaders with incomplete or outdated information.

Modern Approach: ETL and ELT Tools

Modern platforms automate this process. They come with prebuilt connectors to hundreds of apps and databases, adjust automatically when schemas change, and support both batch and real-time synchronization. Many also include monitoring, governance, and quality checks, ensuring that pipelines deliver consistent, trustworthy data at scale.

How We Chose the Tools

The tools featured here are not an arbitrary list. We looked at which vendors appear most often in analyst research, which ones spark the most conversation in the data community, and which have visible traction in enterprise adoption. Together, they represent a mix of established enterprise players, fast-moving cloud-native platforms, and open-source projects that engineering-led teams rely on.

What This Guide Covers

We 13 top ETL/ELT tools in 2025, grouped into four categories: modern cloud-native tools built for automation and scale, open-source options designed for teams that want maximum flexibility and control, legacy platforms, and real-time streaming solutions.

What are the best ETL tools of 2025?

Modern cloud-native & open-source ELT tools

1. Fivetran Automated data extraction and loading at scale

Best for: Teams that want to automate data movement from any source to any destination with minimal maintenance.

Fivetran is one of the most recognized ELT providers, known for its reliability and breadth of coverage. With more than 500 managed connectors, it simplifies centralizing data from SaaS apps, databases, and other systems. Pipelines automatically adjust to schema changes, reducing engineering overhead.

Fivetran automates raw data extraction and loading into the warehouse, scaling naturally with warehouse compute. Many organizations also pair Fivetran with Coalesce using its native integration to synchronize data replication and transformation workflows without the need for third-party orchestration tools.

2. Coalesce Scalable data transformation and AI-powered cataloging

Best for: Teams that want to manage complex data pipelines at enterprise scale using visual or code-first development, automation, and AI.

Coalesce is a modern data development and governance platform designed to help data teams deliver trusted, discoverable data 10x faster with scalable data transformation and AI-powered cataloging solutions.

The platform empower all data pros—ranging from data engineers and architects to data scientists and analysts.—through an intuitive visual interface and modular development approach, without sacrificing the power and control of a CLI.. Coalesce currently supports Snowflake, Databricks, and Microsoft Fabric data platforms.

By pairing an intuitive GUI with flexible code-first capabilities, Coalesce helps data teams ramp up quickly and maximize their impact by accelerating data preparation and eliminating rote tasks with bulk editing capabilities and automatically generated, optimized SQL.

Get started with Coalesce today or take an interactive, virtual tour of the platform.

3. Airbyte Open source & flexible

Best for: Engineering-led teams that want flexibility and cost control.

Airbyte is the leading open-source alternative in the ELT space, offering more than 300 connectors and a framework to build new ones. It can be self-hosted for maximum control or run as a managed cloud service. This flexibility makes it especially attractive to startups and developer-heavy teams.

Because Airbyte focuses on moving data in, many companies use it in combination with transformation platforms like Coalesce to handle modeling, governance, and documentation inside the warehouse.

4. Estuary Real-time streaming ELT

Best for: Companies that need real-time data and analytics.

Estuary specializes in Change Data Capture (CDC), capturing updates as they happen and streaming them into warehouses or lakes with low latency. For industries like finance, logistics, or IoT, this enables real-time dashboards and operational decision-making.

While Estuary ensures fast delivery, teams often complement it with tools like Coalesce to structure and govern streaming data once it arrives in the warehouse. Discover how data teams can leverage Estuary + Coalesce to power real-time data pipelines at scale.

5. Rivery ELT + Reverse ETL

Best for: Business teams wanting fast, no-code pipelines.

Rivery combines ELT with reverse ETL, sending data not just into warehouses but also back into operational tools like Salesforce or Slack. Its prebuilt “data kits” provide templates for common pipelines, which help non-technical users get started quickly.

Organizations that need both operational syncs and enterprise-grade transformation often use Rivery alongside dedicated transformation solutions such as Coalesce.

6. Meltano Pipeline as code

Best for: Developer-heavy teams that prefer code-first workflows.

Meltano builds on the Singer ecosystem with a CLI-first approach. Pipelines are version-controlled in Git and integrate with dbt, making it a good fit for teams that want to treat ELT workflows with the same rigor as software development.

For organizations seeking to standardize transformations and metadata management at scale, Meltano can be complemented with Coalesce’s visual automation and governance features.

7. CData Sync Hybrid cloud replication

Best for: Startups and small teams.

CData Sync bridges on-premises and cloud systems, supporting over 250 sources and destinations. It is widely used in industries like finance and healthcare, where compliance and security matter. Unlike many SaaS-only ELT tools, it can run fully on-premises.

Once data is replicated, many enterprises bring in transformation layers like Coalesce to standardize, document, and govern pipelines across hybrid environments. Its key features are deployment flexibility, wide connector coverage, and support for both batch and incremental replication. CData Sync remains a reliable option for enterprises managing hybrid infrastructures.

Established ETL/ELT vendors

8. Talend & Stitch From Lightweight SaaS to Enterprise Integration

Best for: Startups and small teams.

Stitch, now part of Talend, is a SaaS-native tool designed for quick setup with more than 100 prebuilt connectors. It has long been popular among startups and small teams that need to centralize data without heavy engineering effort. For many, Stitch serves as a first step before adopting Talend’s broader suite.

Talend itself has been a leader in data integration for nearly two decades. Building on its open-source roots, the enterprise platform offers ETL, governance, data quality, and compliance features. Talend Studio provides a drag-and-drop pipeline builder, while Talend Cloud adds orchestration and monitoring.

The combined strength of Stitch and Talend is the ability to cover the full spectrum of needs—from quick, lightweight ingestion for small teams to robust enterprise integration with governance at scale.

9. Matillion Cloud Warehouse Native

Best for: Data engineers seeking fine-grained control over SQL pipelines.

Matillion is designed to run transformations directly inside cloud data warehouses such as Snowflake, Redshift, and BigQuery. Its low-code, drag-and-drop interface makes it approachable for analysts while still offering orchestration and monitoring features for enterprise teams. By leveraging warehouse compute, Matillion ensures pipelines scale with data volumes.

Its key features are deep warehouse integration, visual pipeline building, and enterprise-grade reliability. Many large organizations adopt it for its balance of usability and scalability.

Legacy ETL platforms

10. Informatica PowerCenter Enterprise ETL

Best for: Large enterprises with complex, legacy pipelines.

Informatica PowerCenter has been a cornerstone of enterprise ETL for decades. It is built to handle very large volumes of data and complex transformations, with strong metadata and lineage features. Although it is less cloud-native than newer options, it continues to power critical integrations in Fortune 500 companies.

Its strengths are robust metadata management, mature governance capabilities, and proven performance at scale. Informatica remains a go-to in industries with heavy compliance requirements.

11. Microsoft SQL Server Integration Services (SSIS) SSIS workflow design interface.

SSIS is Microsoft’s ETL platform and is tightly integrated with SQL Server. It provides a visual workflow builder and is widely used in environments where SQL Server is the central database. While often associated with on-premises deployments, Azure Data Factory provides a bridge into hybrid and cloud scenarios.

Its strengths are deep SQL Server integration, familiarity for Microsoft-heavy teams, and a pathway to extend into Azure for hybrid cloud adoption.

12. IBM InfoSphere DataStage High-throughput ETL

Best for: Enterprises needing high-throughput ETL

IBM DataStage is an enterprise ETL tool known for parallel processing and scalability. It supports both batch and real-time integration and comes with strong governance and lineage features. It is commonly used in industries like telecom and banking, where throughput and compliance are critical.

Its strengths are high-performance parallelization, enterprise reliability, and integrated governance features. DataStage is often chosen when processing scale and regulatory requirements are equally important.

Streaming & IoT-focused ETL tools

13. Apache NiFi Real-Time Flow Management

Best for: IoT and hybrid streaming use cases.

Apache NiFi is an open-source platform for managing real-time data flows. Its drag-and-drop interface makes it possible to design complex streaming pipelines without code, while features like provenance tracking and backpressure handling ensure reliability at scale. It is widely adopted in IoT and edge computing scenarios where data must be captured, processed, and routed instantly.

Its strengths are intuitive visual design, real-time streaming capabilities, and strong monitoring and reliability features. NiFi is particularly well-suited for hybrid environments where data moves across cloud, on-prem, and edge systems.