What are the best ELT tools?

For years, Extract, Transform, Load (ETL) powered data pipelines have been the standard. However, as businesses evolved and began generating more data, ETL processes started to lag behind.

In ETL processes, data transformation occurs before storage. This can often become a bottleneck when dealing with continuously changing datasets.

Modern cloud data warehouses have changed this step by providing elastic compute power to run transformations at scale. This removes the need for external staging areas and heavy pre-processing. Teams can now load raw data immediately and reshape it within the warehouse.

This architectural shift gave rise to the Extract, Load, Transform (ELT) paradigm. But with more ELT tools available than ever, teams struggle to determine which one best fits their needs.

In this article, we’ll explain ELT, how it differs from ETL, common use cases, selecting the right tool, and the top ELT tools of 2025. We’ll also explore how dbt enhances the efficiency and reliability of ELT transformations.

What is ELT and why does it matter today?

ELT is a method of building data pipelines in which raw data is first collected and stored in a cloud warehouse. It’s then transformed into the form analysts and applications need for a specific use case. Unlike ETL, ELT delays data cleaning and restructuring until after the data is safely stored.

ELT’s main advantage is that it leverages the massive, scalable compute power of modern warehouses. It performs transformations directly within warehouses like Snowflake and BigQuery, where data lives.

These data warehouses handle large amounts of data using parallel processing, optimizers, and flexible storage. Data pipelines run faster, cost less, and are easier to manage than using an external engine.

This shift has made ELT important today:

• Warehouses now natively support semi-structured formats, such as JSON and logs. This means you can load data as it comes and decide later how to structure it, instead of mapping every field upfront.
• ELT pipelines complete faster and cost less by only reprocessing the changed records.
• Organizations reduce the risk of schema changes that break pipelines by using warehouse-native transformations.
• Teams can recreate historical transformations because raw data is loaded and stored first. You can reapply transformations at any time using updated definitions, ensuring consistent results every time.

ETL vs ELT: Understanding the key differences

Here is a table comparing the two methods:

Common use cases for ELT tools

Teams use ELT to quickly centralize data in a warehouse. This facilitates easier insight generation, streamlined reporting, and informed decision-making. Here are four key use cases:

1. Unifying retail and e-commerce data for personalization

Retailers often integrate web analytics, CRM data, and transaction records to create a comprehensive customer profile. Using ELT, these diverse data streams are consolidated into a single warehouse. There, they are cleaned, standardized, and enriched.

For example, by aligning customer identifiers across systems and calculating lifetime value or purchase frequency metrics. These transformations prepare the data for precise audience segmentation, targeted campaigns, and A/B testing.

2. Ingesting market feeds for near real-time trading insights

Trading firms must respond to exchange data quickly. ELT pipelines enable them to ingest raw feeds into a data warehouse. They then prepare the data for research, P&L, and risk analysis before the markets open.

3. Streamlining compliance and reporting in regulated industries

Financial services and insurance companies face strict requirements for governance, auditing, and data privacy. ELT helps by centralizing controls within the data warehouse. Masking, encryption, and data lineage can then be applied consistently.

4. Powering clickstream, IoT, and app analytics

Digital businesses often rely on streams of events generated by websites, apps, or connected devices. ELT enables them to capture these raw signals directly in the data warehouse. It then incrementally transforms them into insights about user behavior or product performance.

In short, ELT enables teams to bypass bottlenecks, maintain access to raw data, and transform it into business-ready insights on their own terms.

How to choose an ELT tool that fits your needs

ELT tools overcome limitations like inflexibility, slow data integration, extra servers, and high maintenance costs. They connect directly to modern data warehouses, scale efficiently, and reduce overhead.

Hence, choosing the right ELT tool depends on integration with your data sources and scalability. It should also align with your budget and security requirements.

Below are a few key points to consider when selecting the right tool.

Start with your data sources. Check whether the tool integrates with your key systems. Most tools support only mainstream platforms such as Salesforce, Google Analytics, and common databases like MySQL or PostgreSQL.

If you rely on niche apps or custom APIs, you may require a vendor with flexibility. For example, Fivetran now offers over 500 connectors. However, you may still need tools like Airbyte’s CDK for custom or less common APIs.

Consider your data volume and growth. Consider the volume of data you manage today and how it might increase over time. Upside processes 3 TB of daily data using Matillion ETL and Snowflake. It allows the pipeline to scale smoothly without requiring constant re-architecture. If your data is growing quickly, choose a tool that scales without requiring a rebuild.

Evaluate pricing and security. Some tools charge per row processed, others per connector, and some offer flat-rate pricing. Select the model that best matches your usage patterns. Ensure the tool supports your compliance requirements, such as GDPR, HIPAA, or SOC 2. Features like role-based access control, encryption, and audit trails should align with your organization’s policies and procedures.

Evaluate the transformation capabilities of ELT tools. Some offer built-in no-code transformations, while others integrate with frameworks like dbt for version control and data modeling. Consider real-time incremental transformations and testing features to ensure reliability as your pipelines scale.

The best ELT tools in 2025

The ELT ecosystem has expanded, providing open-source options to fully managed solutions. Here are the leading ELT tools of 2025 for modern data pipelines:

1. Airbyte

Airbyte is an open-source ELT platform that provides hundreds of connectors for databases and SaaS apps. It is widely used for flexibility and the ability to build or customize their own integrations.

2. Airflow

Apache Airflow is an orchestration framework, not a connector, but a core ELT stack component. It schedules, monitors, and manages complex data workflows, making it ideal for engineering teams operating at scale.

3. Fivetran

Fivetran is known for its automated, fully managed connectors. They ensure continuous data flow into cloud warehouses with minimal maintenance. It is often chosen by teams that value reliability and offloading connector maintenance.

4. Matillion

Matillion is a cloud-native ELT platform that integrates seamlessly with major data warehouses, including Snowflake, BigQuery, and Redshift. It is designed for enterprises seeking powerful data transformations through a visual interface. It also delivers strong performance at scale.

5. Hevo Data

Hevo Data focuses on no-code data pipelines with real-time capabilities, often adopted by growing businesses. It’s used to quickly centralize SaaS, database, and event data without heavy engineering effort.

6. Stitch

Stitch is a cloud-based ELT service that focuses on data replication into warehouses. It is widely used for its simplicity and ease of setup. It’s a great fit for teams requiring a straightforward data movement without managing infrastructure.

7. Weld

Weld is an ELT platform designed for both business teams and engineers. It emphasizes simplicity by integrating data pipelines, transformations, and reporting into a single environment. This makes it an attractive all-in-one solution for companies.

8. Meltano

Meltano is an open-source platform that builds on the Singer standard for connectors. It is popular among technical teams, as they want to run ELT pipelines. This tool gives them full control in their own environments.

9. Mage AI

Mage AI is an open-source tool for building and managing ELT workflows. It features a modular, notebook-style interface that supports Python, SQL, and R. Additionally, it facilitates machine learning pipelines, encompassing data ingestion, transformation, and model training. The Pro version provides AI-powered automation to further streamline these processes.

Leveling up ELT transformations with dbt

Once data is stored in a cloud warehouse, teams still need a way to manage those transformations as code. Testing, version control, and lineage ensure the models remain clean, reliable, and trusted by analysts.

dbt serves as your data control plane for data, compiling SQL models and running them in the warehouse. Using dbt enables:

Automatic documentation & lineage. dbt automatically generates documentation and a clear dependency graph for every dataset. It builds a Directed Acyclic Graph (DAG) that illustrates how every piece of data flows from the source to the final table. It also generates documentation, allowing you to easily trace any record.

Build reliable data pipelines with automated testing. dbt introduces data tests and schema tests to validate data directly. It checks unique IDs, expected value ranges, and foreign key matches. These checks enable catching problems early, preventing them from propagating downstream and causing issues later.

Collaborate with confidence using version control. dbt provides version control through Git, so every model is stored as modular SQL code. You can create branches, open pull requests, review changes, and merge them safely.

Teams can also create dedicated development environments to test changes and ensure stability safely. This adds confidence when updating transformation logic, as every update is tracked, tested, and reproducible before deploying to production.

dbt also integrates seamlessly with the best ELT platforms, such as Fivetran, Airbyte, and Matillion. This enables automated data ingestion with warehouse-native transformations in a single, streamlined workflow.

Conclusion

If you have experience working with an ELT workflow, you understand how much it streamlines your work. However, being effective involves more than just using this workflow. It requires understanding your organization’s needs and selecting the right ELT tool to meet them.

With ELT tools, you can use dbt to establish a structured framework that transforms data into reliable models. Together, they create a workflow that is both practical and scalable.

• Agility: ELT with dbt makes it easier to adjust as data sources and business needs change.
• Efficiency: Automating data ingestion and transformations reduces manual work and speeds up analysis.
• Reliability: dbt’s testing, version control, and documentation ensure that pipelines remain consistent and transparent.

Get started with dbt today and transform your raw data into reliable, actionable insights.