The 10 Best ETL Tools for Databricks in 2026

Databricks has become one of the most influential platforms in modern data engineering and analytics, now serving more than 60 percent of Fortune 500 companies. Its Lakehouse architecture gives organizations a unified environment for large-scale processing, advanced analytics, and machine learning.

But even the most powerful analytics engine needs high-quality data to operate effectively. That’s why data teams depend heavily on ETL and ELT tools to gather information from different systems, clean and structure it, and deliver it into Databricks in a usable format.

For analysts and engineers, these tools are the bridge between raw, chaotic data and the curated, trusted data sets that Databricks relies on. Strong ETL and ELT processes don’t just give you better insights; they also ensure accuracy, compliance, and smooth operations across your entire data stack. 

In this article, we’ll explore the best ETL tools for Databricks and show you why choosing the right one can have a huge impact on both your performance and your business.

What exactly is an ETL tool for Databricks?

An ETL tool for Databricks helps organizations extract data from multiple sources, transform it into a usable format, and load it into Databricks for analytics, processing, or advanced workloads. 

These ETL tools are designed to work naturally with Databricks’ Lakehouse architecture, unifying data pipelines across cloud storage, streaming sources, databases, and enterprise applications. Because Databricks is often used for large-scale analytics and data analysis for machine learning, ETL tools play a critical role in preparing raw, messy, or unstructured data. 

They handle the ETL data transformation steps that keep data clean, well-modeled, and optimized for downstream processing. This includes normalization, deduplication, enrichment, schema mapping, and format conversions that make data sets ready for Delta Lake and Apache Spark workloads.

Together, ETL tools and Databricks create a powerful foundation for insight generation. ETL pipelines deliver clean, structured, and reliable data directly into the Databricks Lakehouse, where teams can analyze it at scale and build advanced models. 

Databricks, in turn, enhances the value of ETL outputs by allowing analytics and engineering teams to quickly explore, work together, and put data into action. This combination transforms scattered raw data into a strategic asset, speeding up new ideas, improving forecasting, and enabling data-driven decisions that move the business forward.

Benefits of using an ETL tool for Databricks

Using an ETL tool alongside Databricks strengthens the entire data and analytics ecosystem. ETL platforms automate much of the heavy lifting involved in preparing and moving data, allowing Databricks to focus on advanced analytics and machine learning. 

When these tools work together, organizations gain faster performance, better governance, and a scalable foundation that supports modern use cases such as ETL streaming and real-time information.

Higher-quality data for analytics and modeling

ETL tools cleanse, standardize, and enrich data before it reaches Databricks. This reduces noise, eliminates duplicates, and makes sure the data sets used for analytics and machine learning are consistent and trustworthy. The result is more accurate reporting and more reliable predictive outputs. 

A recent article in Techloy highlighted how ETL tools, especially when used with AI, increase data quality for platforms like Databricks. AI-powered ETL tools can identify issues such as strange distribution shifts, mismatched formats sneaking into the pipeline, and early signs of data corruption before the data is ever uploaded into a platform like Databricks. 

Faster data pipelines through increased data automation

With built-in data automation, ETL tools handle transformations, scheduling, orchestration, and dependency management without constant human intervention. This frees Databricks to use its compute power for analytics, processing, and model training, significantly improving time-to-insight.

Easy integration across multiple data sources

ETL data migration tools connect to databases, cloud storage, SaaS applications, legacy systems, and streaming platforms. They consolidate these feeds into unified data sets that Databricks can immediately work with, while eliminating manual integration and reducing engineering bottlenecks.

Better scalability for large or growing data workloads

As data volumes expand, ETL tools can scale automatically across ingestion, transformation, and pipeline orchestration. This means Databricks always receives clean, structured data—whether workloads involve batch processing, near-instant analytics, or ETL streaming.

Stronger governance and compliance oversight

ETL tools offer lineage tracking, validation frameworks, and role-based controls that help organizations stay compliant with data privacy and security requirements. When paired with Databricks, companies gain both powerful analytics and end-to-end oversight of how data moves through the environment.

Reduced engineering burden and operational complexity

Because ETL tools handle most transformation tasks automatically, engineers spend far less time writing custom scripts or troubleshooting pipelines. This lets them focus on higher-impact work inside Databricks, such as improving and refining performance, managing Delta Lake, or experimenting with new machine learning workflows.

Improved collaboration across data teams

Standardized pipelines and consistent data sets help analysts, engineers, and data scientists work from the same source of truth. This alignment reduces rework, prevents siloed versions of data sets, and allows more productive collaboration across all Databricks-related projects.

Key features to look for in a Databricks ETL tool

Choosing the right ETL tool for Databricks can significantly improve how your organization ingests, transforms, and puts data to work. The most effective tools support Databricks’ Lakehouse architecture, streamline complex workflows, and provide the flexibility needed for both batch and ETL streaming pipelines. Below are the essential features to prioritize when evaluating an ETL platform.

Strong ETL data modeling capabilities

A high-quality ETL tool should make ETL data modeling intuitive and scalable. This includes defining schemas, building reusable transformation logic, supporting slowly changing dimensions, and maintaining compatibility with Delta Lake. Effective modeling means data is structured correctly before reaching Databricks, which improves downstream analytics and machine learning performance.

Reliable data management and governance

Look for tools that offer version control, lineage tracking, validation, and automated documentation. These data management features reduce the risk of inconsistencies and make it easier to audit data pipelines. Easy integration with Databricks Unity Catalog is also a major advantage for organizations focused on security and compliance.

Native support for ETL streaming workflows

Databricks excels at processing real-time and near-real-time data, so your ETL tool should support ETL streaming out of the box. This includes handling message queues, event streams, IoT feeds, and continuous ingestion from cloud platforms. Strong streaming support means Databricks can deliver timely information without getting a person involved.

Flexible transformation options for complex workloads

Transformations should support SQL, Python, and visual pipeline builders, giving teams the freedom to work the way they prefer. Tools with built-in transformation libraries reduce development time and keep processing consistent and reliable as data volumes grow.

Advanced orchestration and workflow automation

Your ETL tool should offer dependency management, scheduling, error handling, and automated retries. These orchestration features mean Databricks receives data reliably and consistently, even in large-scale or multi-cloud environments.

Integrated data reporting and monitoring tools

Real-time visibility into pipeline performance is critical. Tools with built-in data reporting dashboards help teams monitor pipeline health, track latency, identify failures, and optimize resource usage. This level of transparency allows organizations to maintain high-quality, production-grade pipelines.

Convenient cloud connectivity and scalability

The best tools integrate easily with cloud storage, relational databases, SaaS platforms, and on-premises systems. They should also scale automatically as workloads increase, so performance is reliable no matter how complex or large your Databricks environment becomes.

Best ETL tools for Databricks in 2026

As more organizations adopt Databricks for unified analytics, machine learning, and large-scale processing, the need for reliable ETL platforms has grown significantly. The best ETL tools move data efficiently while also preparing it with the right data transformation, modeling, and quality controls before it reaches the Lakehouse. They streamline data integration, automate complex workflows, and allow stronger data reporting once the data is available in Databricks. Below are ten top ETL solutions for 2026 that complement Databricks’ speed, scalability, and versatility.

Domo

Domo offers an intuitive, end-to-end platform that simplifies ETL for teams of all skill levels. Its drag-and-drop Magic ETL tools enable rapid transformation, blending, and cleaning without requiring advanced coding. Domo integrates with Databricks through flexible connectors, allowing organizations to push curated data sets directly into the Lakehouse. 

With built-in governance, role-based access, automated alerts, and real-time dashboards, Domo not only supports ETL but also makes downstream data reporting easier and more collaborative across business teams.

Fivetran

Fivetran has become a go-to solution for automated ingestion, thanks to its large library of prebuilt connectors and hands-off pipeline management. It continuously syncs structured and semi-structured data into Databricks with minimal developer involvement. 

Fivetran shines in environments where fresh data is critical, as it offers near-instant updates and schema evolution handling. Its simplicity makes it a strong match for those who use Databricks and want predictable, maintenance-free data integration at scale.

Informatica Cloud

Informatica Cloud provides enterprise-grade integration and governance capabilities, making it one of the stronger tools on this list. It excels in complex, compliance-driven environments where metadata management, data quality, and lineage tracking are essential. 

Informatica’s transformation engine supports advanced logic that prepares data for analytics, machine learning, and regulatory reporting inside Databricks. It’s particularly well-suited for enterprises with large data estates and strict governance requirements.

Talend

Talend delivers a versatile ETL suite that blends open-source flexibility with enterprise-grade features. It offers visual pipeline design, strong data quality tools, and an extensive transformation library. 

Talend’s unified environment helps organizations cleanse data, validate schema consistency, and automate transformation logic before loading it into Databricks. Its governance and monitoring features also appeal to teams that need reliable, auditable data pipelines.

dbt with Databricks

dbt has become a staple for analytics engineering teams, and its compatibility with Databricks amplifies its impact. dbt enables SQL-driven modeling, testing, documentation, and continuous deployment of transformation logic directly inside the Lakehouse. 

When paired with Databricks SQL or Spark, dbt creates a scalable, developer-friendly environment for production-grade data transformation pipelines. Its focus on modular design and version control makes it ideal for teams that want transparency and consistency in their models.

Databricks Delta Live Tables

Delta Live Tables (DLT) is Databricks’ native solution for declarative pipeline development, automation, and reliability. It simplifies pipeline creation by allowing engineers to define transformations while Databricks handles optimization, error recovery, and data quality enforcement. 

DLT is particularly strong for streaming workloads, incremental processing, and machine learning use cases. Its tight integration with Delta Lake ensures performance, reproducibility, and trustworthy data delivery.

Azure Data Factory

Azure Data Factory (ADF) is a cloud-native integration service that’s compatible with Azure Databricks. ADF provides orchestration for data ingestion, transformation, and movement across Azure services and external systems. 

With low-code pipeline design, a wide connector library, and integration with Azure Monitor, it supports scalable, enterprise-ready ETL operations. ADF is especially compelling for organizations already invested in Azure’s ecosystem.

AWS Glue

AWS Glue is a serverless ETL service built to work effortlessly with Databricks on AWS. It automates schema discovery, generates transformation code, and integrates tightly with S3, Redshift, and other AWS services. 

Glue’s flexibility supports both batch and streaming pipelines, making it a solid choice for high-volume ingestion. For teams operating Databricks inside AWS, Glue provides a familiar, cost-efficient foundation for data pipelines.

Hevo Data

Hevo Data specializes in live ingestion and minimal-configuration pipelines. It’s designed for operational simplicity, offering fault-tolerant architecture and transformation capabilities that ensure data is ready for analysis when it reaches Databricks. 

Hevo’s quick deployment and focus on immediacy make it ideal for teams that need fast results and don’t want to manage complex infrastructure.

Airbyte

Airbyte is an open-source integration platform that delivers broad connector support, modular pipelines, and significant customization options. It’s particularly appealing to engineering teams that want control over their environment and transformation logic. 

Airbyte integrates easily with Databricks and supports both batch and incremental loads, making it a cost-effective option for growing organizations or teams with unique pipeline requirements.

How Domo elevates ETL and ELT for Databricks users

Selecting the right ETL solution is essential for getting the most value out of Databricks, and the tools highlighted in this guide each bring unique strengths to the modern data stack. But what sets Domo apart? It’s our focus on putting accessibility first.

While many platforms require specialized engineering expertise, Domo offers low-code ETL tools that make data preparation far more approachable for teams across the organization. This is sometimes described as “ETL tools for everyone,” because Domo’s drag-and-drop ETL environment allows analysts, business adopters, and technical teams alike to build and maintain strong pipelines.

In the Databricks ecosystem, ELT is often used more frequently than ETL, since Databricks performs transformations efficiently inside the Lakehouse. Domo supports this approach as well. In fact, Domo is one of the few platforms that excels at both ETL and ELT, giving organizations full flexibility in how they design and optimize their data workflows. 

Whether you need to transform data before loading it into Databricks or prefer to reshape it inside Databricks itself, Domo fits conveniently into the pipeline. By combining Databricks’ advanced analytics and machine learning capabilities with Domo’s accessible data preparation tools, organizations can accelerate insights while empowering more people to work confidently with data.

Ready to modernize your Databricks pipelines? Explore how Domo’s low-code ETL and ELT capabilities can streamline your data workflows and help your entire team move faster.