10 Best Databricks ETL Tools to Consider in 2026

You’ve got Databricks. Now you need data in it, reliably and fast, without babysitting pipelines at 2am. 

The problem is not a shortage of tools. Most either require too much code, become expensive at scale, or quietly drop rows. The data integration market reached $5.9 billion in 2024 and grew 9.8% year-over-year. (Gartner, 2025). And most of that growth is teams replacing tools that couldn’t keep up. 

In this post, we compare the 10 best Databricks ETL tools in 2026 on native Databricks support, connector depth, ease of setup, and pricing, so you can make the right decision without reviewing multiple vendor websites. 

Short on time? Here are our top 3 picks.

Quick Tabular Comparison of the Top Databricks ETL Tools [2026 Updated] 

Tool	Best For	Key Features	Limitations	Pricing (Starting)
Hevo Data	No-code real-time pipelines	150+ connectors, real-time sync, schema mapping, built-in transformations	Supports cloud-only deployment	Free plan available, paid starts at $239/month
Azure Data Factory	Azure ecosystem users	Visual pipeline builder, native Azure integration, orchestration	Complex setup for beginners, limited real-time capabilities	Pay-as-you-go
Matillion	Native Databricks ELT	Push-down transformations, cloud-native, orchestration	Can get expensive with heavy usage	Usage-based pricing
Rivery	SaaS data pipelines	ELT + reverse ETL, automation, scheduler	Pricing can scale quickly with usage	Usage-based pricing
Talend	Enterprise data integration	Data quality, governance, batch + real-time pipelines	Steep learning curve, complex setup	Custom pricing
Fivetran	Fully managed ELT	Automated connectors, high reliability, minimal maintenance	Expensive at scale, limited transformation flexibility	Usage-based pricing
Airbyte	Open-source flexibility	300+ connectors, self-hosted or cloud, customizable pipelines	Requires engineering effort to maintain and scale	Free (open source), paid plans start at $29/month
Integrate.io	Low-code ETL workflows	Drag-and-drop pipelines, API integrations, transformations	Limited flexibility for highly custom use cases	Custom pricing
Prophecy	Spark-based pipelines	Visual interface for Spark, Databricks-native workflows	Requires familiarity with Spark concepts	Custom pricing
Informatica	Large-scale enterprise ETL	Advanced transformations, governance, AI-driven automation	High cost and implementation effort	Consumption-based pricing

Pricing depends on data volume, connectors, and deployment type. Always check the vendor’s official website for the most accurate details. 

What Are Databricks ETL Tools?

Databricks is built for large-scale data processing, but it is not a plug-and-play data pipeline tool. It does not offer ready-made connectors for your CRM, ad platforms, or production databases. That is where Databricks ETL tools come in.

These tools fill the gaps. They extract data from source systems, transform it, and load it into Delta Lake or a Databricks SQL warehouse.This makes the data usable for your team.

According to Databricks, modern data teams are increasingly adopting automated pipeline orchestration to reduce operational overhead, with 99.95% uptime for their managed workflows. 

The best ETL tools handle all this without forcing analysts to build Spark jobs from scratch. 

Depending on the tool, you get some combination of:

• Pre-built connectors to 150 to 2,000+ sources
• Visual or no-code pipeline builders
• Automated schema handling and error recovery
• Real-time CDC or batch ingestion into Databricks

Teams often choose dedicated ETL tools when Databricks native features like Auto Loader, Delta Live Tables, or Workflows fall short in connector coverage, usability, or maintenance. 

If you are still evaluating whether Databricks is the right platform for your stack, explore Databricks competitors before committing. 

For a broader category overview, check out our guide to cloud ETL tools.

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

How I Chose the Databricks ETL Tools ?

With dozens of tools claiming native Databricks support, narrowing the list required more than reading feature pages. Here is the methodology behind this guide.

Community research

We reviewed discussions across Reddit and the Databricks Community Forum to identify which tools practitioners actually use in production, not just which ones have the loudest marketing presence.

G2 and Capterra review analysis

We analyzed verified user reviews focusing on three recurring pain points: reliability at scale, support quality, and pricing transparency. Tools with fewer than 30 reviews were excluded unless they showed a clear specialist fit for Databricks workloads.

Conversations with existing customers

We spoke with Hevo customers and prospects who had evaluated multiple tools. Their firsthand accounts of setup friction, maintenance overhead, and support responsiveness shaped how we ranked usability and reliability.

Each tool was evaluated based on the following criteria: 

• Native Databricks integration and Delta Lake support
• Connector depth and freshness
• Ease of setup for non-engineering teams
• Pricing transparency and predictability at scale
• Monitoring, alerting, and error recovery capabilities
• CDC support for real-time ingestion

Tools that scored poorly on more than two criteria were cut from the final list.

Top 10 Databricks ETL Tools

1. Hevo Data

Hevo is a no-code ELT platform built for data teams that need reliable, automated pipelines into Databricks Lakehouse without the engineering overhead. It connects any data source to Databricks in minutes and keeps pipelines running with auto-healing and full visibility into every record in transit.

Most ETL tools force a tradeoff: either you get flexibility and write a lot of code, or you get simplicity and lose control. Hevo is built around three principles that sidestep that trade-off entirely:

• Reliability: Auto-healing pipelines detect and recover from failures without manual intervention. No silent row drops, no 2am alerts required.
• Transparency: Every pipeline is observable in real-time. Detailed logs, batch-level anomaly detection, and data lineage views mean you always know what your data is doing and why.
• Simplicity: Go from source to Databricks Lakehouse in under five minutes. No scripts, no YAML configs, no infrastructure to manage. Just a no-code UI and pre-built connectors for 150+ sources.

Many mid-market data teams start with Airbyte or Fivetran. Over time, reliability issues or unpredictable per-row pricing push them to look for alternatives. Hevo is where most of them land, because it offers the right balance of power, simplicity, and cost. 

Key Features

• Partner Connect setup: Onboard to Databricks through Partner Connect in a few clicks, with no manual configuration required.
• Multi-cloud support: Connects across AWS, Azure, and GCP so your pipelines work regardless of where your infrastructure lives.
• Change Data Capture (CDC): Log-based CDC minimizes load on source systems and keeps data fresh in Databricks in real-time.
• Databricks Delta Tables support: Native Delta Table writes boost performance for storage, queries, and downstream analytics.
• Databricks integration: Certified Databricks partner with deep native integration across the full Databricks ecosystem.

Pros

• Scales efficiently for enterprise-level data volumes
• Transparent, predictable pricing with no per-connector or per-row surprises
• Enterprise-grade security with anomaly alerts and full audit logs
• 24×7 customer support with fast response SLAs

Cons

• Advanced transformations may require custom SQL or scripts
• Real-time latency can vary depending on cloud provider performance
• No on-premise deployment option

Pricing

Business Critical: Custom pricing for 100M+ events, built for enterprise scale

Free plan: Up to five users, 1M events per month

Starter: From $239/month for 5M events, up to 50M monthly, SSH/SSL included, up to 10 users

Professional: From $679/month for 20M events, up to 100M monthly, reverse SSH, unlimited users

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 is Hevo the Best Databricks ELT Tool? 

Building pipelines into Databricks is straightforward. Keeping them reliable as your data volumes grow, your sources multiply, and your team scales is where most tools fall short.

Hevo is a no-code, fully managed ETL platform that handles the full pipeline lifecycle from ingestion to delivery into Databricks Lakehouse. It is built for data teams that want production-grade pipelines without the production-grade engineering effort behind them.

Key Advantages

• No-code pipeline setup: Connect sources and configure pipelines through a visual interface. No Spark knowledge required, no scripts to maintain.
• Real-time and batch ingestion: Move data into Databricks on your schedule, whether that means continuous streaming or scheduled batch loads.
• Auto-healing and fault tolerance: Hevo detects failures, retries automatically, and alerts your team. Pipelines recover without manual intervention.
• Change Data Capture (CDC): Log-based CDC syncs only what has changed, reducing load on source systems and keeping Databricks data fresh.
• Transparent pricing: Flat monthly pricing by event volume means your bill stays predictable as your data grows.
• Certified Databricks integration: Native Delta Lake writes and Partner Connect support, backed by an official Databricks partnership.

When Hevo is the Right Choice

• Your team is spending more time fixing pipelines than using data
• You need real-time or near real-time data in Databricks without building custom infrastructure
• Fivetran’s per-row pricing or Airbyte’s reliability has become a bottleneck
• You are consolidating multiple data sources into Databricks and need broad connector coverage fast

Sign up for a 14-day free trial of Hevo Data to see how it simplifies Databricks ETL. 

Also explore how Favor Delivery scaled their pipelines using Hevo. 

Frequently Asked Questions on Databricks ETL Tools