5 Best ETL Tools for Databricks Integration (2025 Guide)

Databricks has become one of the most popular platforms for building modern data architectures. Combining the flexibility of data lakes with the performance of data warehouses, it powers use cases across business intelligence, advanced analytics, and machine learning. With features like Delta Lake, Unity Catalog, and Databricks SQL Warehouse, it provides a unified environment for both structured and unstructured data.

But to get the most value out of Databricks, you need a reliable way to move data into it. That’s where ETL tools for Databricks integration come in. These tools make it possible to extract data from operational systems, transform it into the right schema, and load it into Databricks for analysis.

In this article, we’ll review the best ETL tools for Databricks. From real-time streaming platforms to enterprise-grade managed services, you’ll see how solutions like Estuary Flow, Fivetran, Airbyte, Matillion, and Informatica can help you integrate data into Databricks more efficiently.

What Makes ETL to Databricks Unique

Moving data into Databricks is not the same as loading it into a traditional data warehouse. Databricks is built on the lakehouse architecture, combining the low-cost scalability of data lakes with the structure and performance of warehouses. This unique foundation introduces both opportunities and considerations for ETL pipelines.

Delta Lake and Incremental Updates

Databricks uses Delta Lake as its storage layer, enabling ACID transactions, schema enforcement, and time travel. ETL tools should be able to handle incremental data updates rather than just full refreshes. Features like delta updates or CDC (change data capture) are critical for keeping Databricks tables in sync without unnecessary cost or latency.

Unity Catalog for Governance

Enterprises rely on Databricks not only for analytics but also for governance and compliance. Unity Catalog provides centralized access controls, data lineage, and auditing. Any ETL tool that integrates with Databricks must respect these structures, ensuring secure and compliant data flows.

SQL Warehouse and Cost Efficiency

ETL pipelines into Databricks often target a SQL Warehouse. Since compute usage directly affects cost, it is important to choose tools that offer efficient scheduling, delta ingestion, and the ability to reduce warehouse runtime. For example, some tools allow delayed or batched updates to save on compute charges.

Real-Time vs. Batch Considerations

Databricks supports both batch and streaming ingestion. The choice of ETL tool depends on whether you need real-time pipelines for analytics dashboards and ML features, or batch pipelines for periodic reporting. Streaming-first tools deliver fresher insights, while batch tools may be sufficient for slower use cases.

Criteria for Choosing ETL Tools for Databricks

Not all ETL platforms are equally suited for Databricks. Since Databricks combines a data lake with a warehouse and adds governance features, the right tool should align with both your technical needs and business goals. Here are the most important factors to consider:

Data Latency Requirements

• Real-time pipelines are ideal if you need up-to-the-second dashboards, fraud detection, or machine learning features.
• Batch pipelines may be enough if you only require daily or hourly reporting.

Choosing an ETL tool that supports the right ingestion model can significantly affect performance and cost.

Support for Delta Updates and CDC

Databricks thrives on incremental processing. Tools that support delta updates or change data capture (CDC) will keep your tables fresh without the overhead of full reloads. This reduces both compute usage and storage costs.

Governance and Security

With Unity Catalog at the center of Databricks governance, your ETL pipelines should integrate securely. Look for features like:

• Role-based access controls
• Token-based authentication
• VPC peering or PrivateLink options
• Compatibility with enterprise IAM and compliance requirements

Cost Efficiency

Since Databricks SQL Warehouses are metered, ETL tools should help optimize costs. Useful features include:

• Scheduled syncs and auto-stop support
• Delta updates that avoid redundant processing
• Scalable pricing models that won’t balloon as data grows

Ease of Setup and Maintenance

Engineering bandwidth matters. Some ETL tools require extensive setup and monitoring, while others are no-code or fully managed. Consider whether your team has the expertise to maintain open-source pipelines or prefers a tool that handles scaling and reliability automatically.

Want to skip the setup hassle? Try Estuary Flow free → and see how quickly you can stream data into Databricks.

Best ETL Tools for Databricks Integration

1. Estuary Flow

Estuary Flow is a real-time ETL and data streaming platform that makes it easy to move data into Databricks without writing custom code. Unlike batch-first tools, Flow is designed for continuous data syncs with exactly-once delivery and schema enforcement, ensuring your Databricks environment stays up-to-date and reliable.

Why Estuary Flow Stands Out

• Real-time pipelines: Stream data continuously from databases, SaaS apps, and event streams.
• Delta updates support: Flow’s Databricks connector supports both standard merge updates and delta updates, reducing latency and costs.
• Enterprise-ready: With deployment options like Private Deployment and Bring Your Own Cloud, plus secure connectivity via VPC peering, PrivateLink, and SSH tunneling, Flow meets the requirements of highly regulated industries.
• No-code experience: Build production-grade pipelines without heavy engineering work.
• Data integrity: Features like schema enforcement, backfills, and CDC ensure pipelines remain consistent even as data evolves.

Ready to build your first pipeline? Start streaming to Databricks with Estuary Flow today →

2. Fivetran

Fivetran is a fully managed ETL and ELT platform that is popular with enterprises looking for reliability and minimal maintenance. It offers a large ecosystem of pre-built connectors and strong automation features, making it a common choice for organizations that need to integrate SaaS applications, databases, and APIs into Databricks.

Key Benefits of Using Fivetran with Databricks

• Native Databricks integration: Supports Databricks SQL Warehouse and Delta Lake as destinations.
• Automation: Handles schema drift and connector updates automatically, reducing manual engineering effort.
• Enterprise ecosystem: Strong partnerships with cloud platforms and data tools make it easy to embed in existing enterprise stacks.
• Ease of use: Simple setup and fully managed pipelines allow teams to get started quickly.

While Fivetran offers strong automation and stability, it is primarily batch-oriented rather than streaming-first, and its Monthly Active Rows (MAR) pricing model can become expensive at scale. For many large organizations, though, the trade-off is worthwhile given the reduced maintenance and enterprise support.

3. Airbyte

Airbyte is an open-source ETL and ELT platform that gives data teams flexibility to build and manage their own pipelines. With hundreds of pre-built connectors and an active community, Airbyte makes it possible to move data from a wide range of databases, SaaS applications, and APIs into Databricks. It can be deployed either on-premises or in the cloud, and Airbyte Cloud offers a managed option for teams that prefer less operational overhead.

Key Benefits of Using Airbyte with Databricks

• Open-source flexibility: Full access to connector code, allowing customization and extension.
• Large connector ecosystem: Hundreds of source connectors maintained by Airbyte and the community.
• Deployment choice: Self-host for control or use Airbyte Cloud for convenience.

Airbyte is widely adopted for its flexibility and transparency, but pipelines are primarily batch-based, and managing large-scale workloads often requires extra engineering effort.

4. Matillion

Matillion is a cloud-native ETL and ELT platform designed for modern data warehouses and lakehouses, including Databricks. It focuses heavily on transformations and orchestration, providing a low-code interface that helps teams design complex pipelines without extensive custom coding.

Key Benefits of Using Matillion with Databricks

• Native Databricks support: Works directly with Databricks Delta Lake and Unity Catalog.
• Transformation-first approach: Powerful tools for data preparation, enrichment, and orchestration.
• Low-code interface: Drag-and-drop pipeline building makes it easier for analysts and engineers alike.
• Scalable cloud deployment: Built for cloud environments with integrations across AWS, Azure, and GCP.

Matillion is well-regarded for its transformation features and Databricks partnership, but it is generally batch-oriented and tends to be more expensive than open-source alternatives.

4. Informatica

Informatica is a long-established enterprise ETL and data integration platform trusted by organizations in highly regulated industries. It provides a broad range of features for governance, compliance, and data management, making it a common choice for companies that require strict security controls alongside large-scale ETL operations.

Key Benefits of Using Informatica with Databricks

• Enterprise-grade governance: Deep integration with Unity Catalog for lineage, access control, and compliance.
• Wide connector library: Support for hundreds of enterprise systems, from legacy databases to modern SaaS platforms.
• Advanced transformation capabilities: Built-in tools for complex ETL logic, data quality, and metadata management.
• Enterprise reputation: Decades of trust in finance, healthcare, and government sectors.

Although Informatica offers robust integration with Databricks, it is often complex to implement and comes with a higher cost compared to newer, more agile platforms.

Comparison Table: ETL Tools for Databricks

*Ratings from G2. Data reflects most recent available ratings at time of research.

Whether you need batch or streaming, Estuary makes it easy to integrate data into Databricks. Get started free →

Conclusion

Whether you prefer open-source flexibility, a fully managed enterprise platform, or a real-time streaming-first solution, these ETL tools provide multiple ways to integrate data into Databricks easily. The best fit depends on your specific needs for data latency, governance, cost efficiency, and ease of setup.

For most modern analytics and machine learning workloads where real-time pipelines and enterprise security matter, Estuary Flow offers a fast, secure, and reliable way to keep Databricks data always up to date.

Join companies already using Estuary for real-time pipelines. Read success stories or Register now.