10 Best Databricks ETL Tools to Consider in 2025

Building reliable ETL pipelines in Databricks requires the right tools. Without them, teams often face issues like slow data processing, complex integrations, and maintenance overhead.

The right ETL tool can simplify workflows, reduce errors, and speed up analytics. In this blog, we discuss the 10 best Databricks ETL tools worth considering in 2025.

Let’s get started.

Short on time? Here are our top 3 picks.

What Are Databricks ETL Tools?

What Are Databricks ETL Tools?

Databricks ETL tools help you move, clean, and prepare data inside the Databricks environment. These tools work with core components like Apache Spark, Delta Lake, Auto Loader, and Workflows to build fast, scalable pipelines.

While Databricks offers plenty of built-in features, it does not provide all the tools a team may need. Hence, teams that need more flexibility look for Databricks ETL tools, like Hevo, as it allows for:

• No-code ETL processes
• Pre-built connectors
• Drag-and-drop UI
• Automation

Many tools integrate directly with Databricks, while others work alongside it as standalone platforms.

Learn More: Top 10 Databricks Competitors: Best Alternatives for 2025

Top 10 Databricks ETL Tools

Explore the top 10 Databrocks ETL tools to pick the most suitable one for your Databricks ETL needs:

1. Hevo Data

Hevo helps you connect any data source to Databricks Lakehouse with a few clicks and load data in minutes. You can do it from any type of data source, such as databases, cloud applications, marketing platforms, analytics platforms, and many more. And you can use the data for any purpose, from data analytics to reporting and decision making.

Hevo can help you improve your Databricks analytics in three key ways:

1. Complete automation: Extract, transform and load your data from multiple sources to Databricks without writing any code as Hevo automates end-to-end data flows.
2. Accurate and fast analytics: Get analytics-ready data with Hevo in minutes and run SQL queries on your Databricks Lakehouse.
3. 12x better price performance: Enjoy 12x better price/performance with the Databricks multicloud lakehouse architecture than traditional cloud warehouses.

Key features

• Partner Connect setup: Allows quick Databricks onboarding through Partner Connect, reducing configuration to just a few clicks.
• Multi-cloud platform support: Connects with cloud platforms across AWS, Azure, or GCP, ensuring flexibility no matter where your infrastructure resides.
• Change data capture (CDC): Utilizes log-based CDC for many databases, minimizing source system load and ensuring real-time data freshness in Databricks.
• Delta Table support: Provides native support for Databricks Delta Tables, boosting performance for storage, queries, and analytics.

Pros

• Scales efficiently for large enterprise-level datasets.
• Transparent and predictable pricing.
• Enterprise-grade security and anomaly alerts.

Cons

• Advanced transformations may need custom SQL or scripts.
• Real-time latency may vary by cloud provider performance.
• No on-premise deployment option.

Pricing

Hevo offers a transparent subscription pricing structure without hidden costs.

• Free plan: Supports up to five users with a limit of 1M events each month.
• Starter: Starts at $239/month for 5M events, scaling to 50M monthly with SSH/SSL for as many as 10 users.
• Professional: From $679/month for 20M events, scaling to 100M per month with reverse SSH for unlimited users.
• Business Critical: Custom pricing beyond 100M events, built for enterprise-scale use cases

You can also avail a 14-day trial.

Learn More: 20 Best ETL Tools You Should Know About in 2025

Hevo is serving entire ETL(s3, mongo, postgres, sheets, drive, webhook etc. sources to databricks/redshift destination) in our organisation, we are able to provide near real time curated data to our customers(analyst and product team) to do analysis. The best part of Hevo is its extreme level customer support which is available 24*7 in case of any production issue. Also hevo provides a lot of sources and destinations. The api integration with hevo make it feasible to automate things as well from observability point of view. With hevo, we are able to provide data in destination(redshift/deltalake) with our SLA of 1 hr.

Verified User

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2. Azure Data Factory

Azure Data Factory acts as the orchestration engine for Databricks. It ingests data from multiple sources, loads it into a data lake, and triggers Databricks for transformation using Spark.

It features a visual interface and over 90 connectors, allowing you to build, schedule, and manage scalable hybrid pipelines. This separation of data movement and processing ensures reliable, secure, and cost-effective workflows.

It’s an end-to-end solution for teams seeking Databricks for analytics, machine learning, and advanced data transformations.

Key features

• Hybrid data integration: Offers seamless integration between on-premise and cloud data sources, consolidating them into Databricks.
• Delta Lake Support: Provides native support for Delta Lake tables, enabling reliable batch and streaming data operations.
• Data Flows transformation capabilities: Allows code-free data transformations using Mapping Data Flows that use Spark to deliver high performance at scale.
• Dynamic content and expressions: Makes pipelines highly flexible by using parameters, expressions, and conditional logic to adapt data workflows dynamically.

Pros

• Automates retries and error handling.
• Integrates with Azure Key Vault for secure credential management.
• Simple SSIS package migration capability.

Cons

• Requires Azure knowledge for advanced configurations.
• Less control over the underlying compute.
• It can be complex for simple data tasks.

Pricing

Azure Data Factory’s pricing is based on a pay-as-you-go model, starting at $1 per 1,000 orchestration runs, $0.25 per DIU-hour for data movement, $0.005 per hour for pipeline activities, and $0.00025 per hour for external pipeline activities.

Charges differ by region, with additional charges for execution, debugging, and monitoring datasets.

Learn More: Azure Data Factory ETL Tutorial: Step-by-Step Guide

3. Matillion

Matillion is an ETL cloud-native tool for data ingestion and transformation. With over 150 connectors, it extracts data from multiple sources, applies transformations, and loads it into Delta Lake.

It helps professionals in industries like finance, healthcare, and retail automate data workflows, improving accessibility and usability of data for analytics.

Matillion is distinguished by its pushdown processing that uses Databricks’ native compute power by executing transformations at the data source. It also offers features that accelerate pipeline deployment and management across cloud environments.

Key features

• Databricks workflows integration: Connects with clusters and SQL warehouses for complete pipeline orchestration.
• Monitoring and logging capabilities: Provides built-in monitoring tools with real-time insights into pipeline performance.
• Version control integration: Connects with Git repositories to manage pipeline versions and track changes.
• Advanced data transformation components: Provides Lookup, Filter, and Aggregate components for code-free or SQL-based transformations.

Pros

• Offers Copilot AI to generate, optimize, and maintain pipelines.
• Enables parameterized tasks for dynamic data workflows.
• Supports major cloud platforms, such as Redshift and BigQuery.

Cons

• Pricing isn’t transparent.
• Limited on-premise support.
• Comes with a learning curve for advanced transformations and orchestrations.

Pricing

Matillion uses a credit-based pricing model, where costs are determined by virtual core (vCore) hours consumed. The Developer plan for individuals starts at $2.50 per credit, while the advanced plans follow a subscription model with a minimum monthly credit commitment.

It offers a 14-day free trial.

Learn More: Top 10 Matillion Alternatives and Competitors in 2025

4. Rivery 

Rivery, now a part of Boomi, is primarily an ELT platform. However, its competitive orchestration and transformation capabilities make it an effective ETL solution for Databricks. It supports overmore than 200 connectors and is known for its visual interface and CDC technology.

Organizations in retail, finance, and technology use Rivery to automate pipelines, handle real-time updates, and maintain consistent data quality.

Its integration with Boomi further centralizes pipeline management and monitoring, providing a scalable and user-friendly approach for teams seeking reliable Databricks ETL workflows.

Key features

• Reverse ETL and data activation: Pushes transformed Databricks data back into operational systems for real-time usage.
• API-first connectivity: Easily integrates with cloud apps and services using standardized APIs.
• Centralized monitoring dashboard: Provides a unified view of all pipelines, making performance tracking simple and efficient.
• Data lineage and governance: Provides visibility into data flow while ensuring compliance with organizational and regulatory requirements.

Pros

• Supports complex multi-step data transformations.
• Offers pre-built templates to accelerate development.
• Cloud-agnostic deployment across different providers.

Cons

• Pricing is hard to predict.
• Some advanced features have a learning curve.
• Limited customization for complex ETL logic.

Pricing

Rivery follows a usage-based pricing model, measured in Boomi Data Units (BDU). The Base tier starts at $0.90 per BDU credit, with custom pricing available for higher usage. It provides a 14-day free trial, including 1,000 usage credits.

Learn More: Top 5 Rivery Alternatives & Competitors in 2025

5. Talend

Talend is an integration platform with tools for data transformation and governance. Its Visual Studio interface simplifies building ETL pipelines while supporting 1,000+ connectors for diverse sources.

Talend speeds up complex transformations with Databricks’ Spark engine and loads them into Delta Lake for analytics and machine learning.

It is a practical choice for teams seeking scalable and high-performance ETL workflows within the Databricks ecosystem.

Key features

• Support for Unity Catalog: Easy integration with Databricks Unity Catalog for secure data governance and control.
• Automated schema management: Handles schema changes dynamically to keep Databricks pipelines consistent and error-free.
• Real-time data streaming: Supports real-time data processing and streaming integration with Databricks for continuous pipeline execution.
• Hybrid and multi-cloud support: Runs ETL pipelines across multiple cloud platforms and on-premise environments.

Pros

• Integrates with major BI and data science tools natively.
• Extensive open-source community for user support and resources.
• Built-in job scheduling features for Databricks pipelines.

Cons

• Initial setup may require technical expertise.
• Unclear pricing.
• Customers report occasional performance lags with larger projects.

Pricing

Talend offers a subscription-based custom pricing spanning four tiers, with a 14-day free trial for Talend Cloud.

Learn More: Outpace Talend: 5 Talend Alternatives to Supercharge Your Data Integration in 2025

6. Fivetran 

Fivetran is a cloud-native platform that automates data movement into Databricks Lakehouse. It extracts and loads data from 700+ sources, including SaaS apps, databases, and ERP systems, directly into Delta Lake.

If you want to centralize data, support both full and incremental loads, and ensure accuracy and reliability, Fivetran is a strong choice. Plus, its integration with Databricks Unity Catalog adds governance and security for sensitive datasets.

Fivetran provides a fully managed solution with dynamic schema updates and easy syncs, speeding up time-to-insight for Databricks users.

Key features

• Support for open data formats: Compatible with Apache Iceberg and other open formats for easy integration with Databricks.
• Log-based Change Data Capture (CDC): Enables incremental updates to reduce load and keep Delta Lake tables synchronized with minimal latency.
• Comprehensive data privacy features: Provides specific controls, such as column hashing and blocking, to automatically anonymize sensitive data during the loading process.
• Custom connector SDK: Builds connectors for unique or unsupported data sources to expand ETL capabilities.

Pros

• Pre-built transformation templates.
• Strong error handling and retry mechanisms.
• Historical data backfill without downtime.

Cons

• Unpredictable pricing that gets expensive at scale.
• 24/7 customer support is limited to higher-tier plans.
• Mostly batch-focused.

Pricing

Fivetran calculates charges based on Monthly Active Rows (MAR) per connection, which are determined by the number of rows added and updated every month.

It offers a free plan with 500,000 MAR and 5,000 model runs per month. Paid plans start at $500 per month for the first million MAR.

It also offers a 14-day free trial.

Learn More: What is Fivetran? And Why Hevo Might Be the Better Pick for You

7. Airbyte

Airbyte is a leading open-source data movement platform for teams seeking a highly customizable and cost-effective ETL solution for their Databricks Lakehouse. It provides a comprehensive catalog of over 600 connectors, allowing you to move data from almost any source.

It offers flexible deployment options, including a self-hosted open-source version and a fully managed cloud service catering to different security and control requirements.

Its strong integration with Delta Lake optimizations and Unity Catalog security makes it a practical choice for scalable and governed data workflows.

Key features

• Embedded metadata tracking: Automatic tracking of schema changes, lineage, and data pipeline metadata for better governance.
• Custom connector development kit: Allows building or customizing connectors to ingest data from unique or unsupported sources without heavy coding.
• Standardized data protocol: Offers a uniform data format and protocol to ensure consistency across Databricks pipelines.
• Orchestration and API automation: Enables connectivity with tools like Airflow for automated scheduling and pipeline management. 

Pros

• Offers free self-hosted workflows.
• Extensive open-source community.
• Supports reverse ETL.

Cons

• Requires DevOps skills for self-hosted deployments.
• Lacks built-in visual transformation tools compared to competitors.
• Debugging on self-hosted workflows can be difficult.

Pricing

Airbyte’s pricing is characterized by a predictable subscription-based model.

• Open Source Edition: Free forever and self-hosted.
• Cloud: Cloud-hosted, starts at $10 per month.
• Teams: Cloud-hosted, custom pricing for advanced scalability and governance.
• Enterprise plans: Self-hosted with custom pricing and full infrastructure control.

Airbyte offers a 14-day free trial.

Learn More: 11 Best Airbyte Alternatives & Similar Tools for 2025

8. Integrate.io

Integrate.io is a cloud-based data integration platform that simplifies pipeline creation for both technical and non-technical users. It supports over 150 connectors, including SaaS apps, databases, and cloud storage.

It handles batch and real-time workflows with 220+ transformation functions to clean and enrich data efficiently. Additionally, its pipelines are optimized for Databricks’ Spark engine and Delta Lake, ensuring fast and reliable processing.

The Unity Catalog integration helps you create scalable and managed Databricks ETL workflows.

Key features

• Advanced data masking and anonymization: Configurable masking and anonymization rules to protect sensitive fields during ETL, ensuring compliance with privacy regulations.
• Pipeline versioning and rollback: Allows maintaining multiple pipeline versions with easy rollback, enabling quick recovery from errors or unintended changes.
• Native reverse ETL support: Provides easy movement of processed Databricks data back into CRM, marketing, or operational systems for real-time analytics.
• Visual data lineage tracking: Offers a graphical lineage view showing each transformation step, source, and dependency for auditing and governance purposes.

Pros

• Clear and predictable pricing.
• Supports complex data transformations without extensive coding.
• Enables scheduled and event-driven pipeline execution.

Cons

• It might be expensive for small businesses.
• Fewer advanced analytics features.
• Limited on-premise flexibility.

Pricing

Integrate.io offers an easy-to-understand fixed-fee pricing model with a custom plan for enterprise services. Its Integrate.io Core plan is priced at $1,999/month.

You also have an option for a 14-day free trial.

Learn More: 10 Best Data Consolidation Tools to Consider in 2025

9. Prophecy

Prophecy is a low-code platform for building enterprise-grade ETL pipelines directly on Databricks. It focuses on accelerating Spark development by automatically generating optimized Spark code from visual workflows.

Teams in finance, healthcare, and retail use Prophecy to standardize data engineering practices, reduce manual coding, and enforce testing and CI/CD pipelines.

Its integration with Databricks’ core components, along with unique features like AI-assisted pipeline suggestions and auto-generated documentation, helps you maintain quality and speed up development.

Key features

• Dynamic schema evolution handling: Allows pipelines to automatically adapt to changing source schemas without breaking workflows.
• Multi-environment deployment management: Provides the ability to deploy and manage pipelines across development, test, and production Databricks environments.
• Automated data quality validation: Allows real-time checks on incoming data to ensure accuracy, completeness, and consistency in data workflows.
• Compiler-based architecture: Ensures workflows run efficiently at scale by translating visual pipelines into production-ready Spark jobs.

Pros

• Provides reusable workflow templates to accelerate development.
• Enables collaboration between data engineers and analysts on the same workflow.
• Optimizes job scheduling and orchestration directly on Databricks clusters.

Cons

• It might come with an initial learning curve.
• Limited support for niche or uncommon data sources.
• Expensive for smaller teams.

Pricing

Prophecy’s pricing isn’t publicly disclosed and follows a custom model, typically including a platform fee plus per-user, per-year costs. A 21-day free trial is available.

10. Informatica

Informatica delivers enterprise-grade data integration and governance through its Intelligent Data Management Cloud (IDMC). It features over 300 connectors that natively push complex processing to Databricks’ SQL-based ELT for efficient workflows.

The platform ensures trusted analytics by integrating its data catalog and governance tools with Unity Catalog for consistent lineage and policy enforcement.

Informatica integrates deeply with Databricks and supports new features, such as Managed Iceberg Tables.

Key features

• AI-powered data mapping: Offers automated schema recognition and transformation suggestions through the CLAIRE AI engine, while GenAI Recipes and Mosaic AI connectors accelerate AI-driven development on Databricks.
• Serverless elastic scaling: Provides on-demand resource allocation that automatically adjusts to workload size for cost-effective performance.
• Real-time streaming support: Allows ingestion and processing of continuous data streams for low-latency analytics on Databricks.
• Advanced data quality rules: Ensures reliable insights by applying customizable validation and cleansing rules across datasets.

Pros

• Strong security compliance.
• Provides enterprise-level metadata management.
• Offers shared workspaces and governance controls for collaboration support.

Cons

• Uncertain pricing. 
• Steep learning curve for advanced capabilities.
• Complex architecture compared to lightweight modern data integration tools.

Pricing

Informatica uses a custom, consumption-based pricing with costs depending on your usage. The pricing details aren’t publicly available, but the platform offers a demo and a 30-day trial for its Cloud Data Integration tool.

Learn More: Top 10 Informatica Alternatives & Competitors in 2025

How to Choose the Right Databricks ETL Tool?

Choosing the right ETL tool for Databricks is not just about features. It is all about the fit. Hence, there are several factors you need to consider, like your data sources, team skills, and scaling.

Here are seven key factors to help you decide what works best for your setup.

1. Native integration with Databricks

Since you are going to work with Databricks, check if the tool connects directly with Databricks through supported APIs, Unity Catalogue, or Delta Lake.

Native integration ensures better performance and fewer compatibility issues.

2. Support for Delta Lake and DLT

This is another vital element to factor in. Look for tools that support Delta Lake features like ACID transactions and schema enforcement.

Also, ensure that they can work with Delta Live Tables for real-time, declarative pipelines.

3. Connector availability

Make sure the tool supports your data sources. No matter if they are cloud storage (S3, Azure), SaaS apps (Salesforce, HubSpot), or databases (Postgres, MySQL).

This will save you a lot of time, effort, and IT headaches as you need to build connectors if you already don’t have them.

4. No-code vs. code-first flexibility

Often, you need to pick a tool that works best for your teams. Hence, look for a tool that matches the skill levels of your team.

This means you can choose a tool that offers visual pipeline building, code support (Python/SQL), or both.

This helps balance ease of use with control.

5. Data quality and monitoring features

Data quality is vital when it comes to bringing data to Databricks, as you need to use it for insights.

Hence, it is vital to choose a tool that can help you ensure the quality without another tool.

Look for features, like built-in data validation or lineage tracking, that can help catch errors early and address them.

6. Scalability and cost efficiency

Many businesses often think in terms of what they need right now. But as a business that’s trying to grow, you should consider your future needs, as well.

Hence, your tool must be able to handle large data volumes without slowing down or getting expensive.

Look at pricing models, compute usage, and how well it scales with Databricks clusters.

Learn More: What is ETL? Guide to Extract, Transform, Load Your Data

What Are the Advantages of Using Databricks for ETL?

Databricks offers several key advantages that make it ideal for building efficient ETL workflows.

• Unified workspace: It provides a single environment for engineering, analytics, and collaboration, eliminating the need to switch between multiple tools.

• Scalability: Databricks automatically scales compute resources up or down based on workload demands, ensuring consistent performance while optimizing costs.

• Unified batch and streaming: It supports both batch processing and real-time streaming workflows on the same platform without rebuilding infrastructure.

• Performance optimization: The platform features intelligent query optimizations, caching, and the Photon engine to accelerate pipeline execution and improve overall performance.

• Integration flexibility: It integrates easily with cloud storage, databases, and hundreds of data sources through native connectors and APIs.

All these benefits make Databricks an ideal tool for ETL.

Learn More: Setting Up Databricks ETL: 2 Comprehensive Methods

Why Should You Choose Hevo?

Databricks is powerful for analytics and machine learning, and there is no doubt about it. But building and managing ETL pipelines inside it can get complex fast. This is especially the case at scale.

Hence, you need a tool that simplifies data orchestration, reduces manual work, and works reliably with Databricks.

Hevo is built to do exactly that. It offers a no-code, fully managed ETL platform that integrates smoothly with Databricks and handles the heavy lifting behind the scenes.

While you could find numerous Databricks ETL tools, Hevo stands apart by offering:

1. Native Databricks integration: Connect directly with Databricks Lakehouse, Delta Lake, and Unity Catalog.
2. No-code pipelines: Set up production-grade ETL pipelines without writing or maintaining code.
3. Real-time data sync: Keeps your Databricks workspace up to date with live data from 150+ sources.
4. Automatic schema mapping and error handling: Reduce pipeline failures and manual fixes for faster and efficient ETL processes.

With Hevo, you spend less time managing pipelines and more time using your data.

Sign up for a 14-day free trial of Hevo Data to make faster decisions, just like Favor Delivery has been able to.

Frequently Asked Questions on Databricks ETL Tools