6 ETL Tools for Databricks Integration (2026 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 and native ingestion options for Databricks in 2026. You’ll see how platforms like Estuary, Fivetran, Airbyte, Matillion, Informatica, Skyvia, and Databricks Lakeflow Connect fit different ingestion needs, from batch ELT to real-time CDC and governed lakehouse pipelines.

How to Choose an ETL Tool for Databricks

Choosing an ETL tool for Databricks is not just about how many connectors a platform has. Databricks is a lakehouse platform, so the right tool should help you land data efficiently into Delta Lake, work cleanly with Databricks SQL warehouses, and avoid unnecessary compute costs.

Here are the main things to check before choosing a tool.

1. Native Databricks and Delta Lake Support

The tool should support Databricks as a first-class destination, not just treat it like another generic database. Look for support for Delta Lake tables, Databricks SQL warehouses, and update methods that avoid full reloads whenever possible.

This matters because Databricks pipelines can become expensive if every sync rewrites large tables or keeps compute running longer than needed. A good Databricks ETL tool should help you load only what changed and keep tables query-ready for analytics, dashboards, and machine learning workloads.

For example, Estuary’s Databricks connector supports materializing data into Databricks tables through a Databricks SQL warehouse and supports both standard and delta updates.

2. CDC and Incremental Update Support

For operational databases like PostgreSQL, MySQL, SQL Server, MongoDB, or Oracle, full refreshes are rarely the best option. They increase load on the source system, add latency, and create unnecessary processing inside Databricks.

A better option is change data capture, or CDC. CDC lets the tool capture inserts, updates, and deletes from the source and apply those changes to Databricks tables. If your use case depends on fresh customer data, product events, inventory changes, payments, or ML features, CDC support should be one of the first things you check.

This is especially important for teams moving data from databases such as PostgreSQL, MySQL, or SQL Server into Databricks, where frequent full refreshes can quickly become inefficient.

3. Unity Catalog and Security Alignment

Databricks is often used in environments where governance matters. If your team uses Unity Catalog for access control, lineage, auditing, and data discovery, your ingestion tool should fit into that model instead of creating separate governance gaps.

Check how the tool handles authentication, permissions, private networking, secrets, and access to production systems. For enterprise use cases, features like PrivateLink, VPC peering, SSH tunneling, role-based access, and auditability can be just as important as connector count.

4. Latency Requirements: Batch, Near Real-Time, or Streaming

Not every Databricks pipeline needs real-time data. Some reporting use cases are fine with hourly or daily syncs. But other workloads, such as fraud detection, operational dashboards, customer 360 views, personalization, and ML feature updates, may need much fresher data.

Before choosing a tool, be clear about the latency your business actually needs:

• Batch is usually enough for periodic reporting and historical analysis.
• Near real-time works well when teams need frequent updates but not second-by-second changes.
• Streaming or CDC-based pipelines are better when Databricks needs to reflect source changes quickly.

This choice affects tool selection, cost, architecture, and maintenance. Picking a batch-first tool for a real-time use case usually creates workarounds later.

5. Cost Control and Operational Maintenance

Databricks cost is not only about storage. SQL warehouses, jobs, serverless compute, and repeated processing can all add up. Your ETL tool should help reduce unnecessary work by supporting incremental updates, efficient scheduling, retries, schema handling, and pipeline monitoring.

Also consider how much engineering time the tool needs. Open-source tools may offer flexibility, but they can require more setup, upgrades, and monitoring. Fully managed tools reduce operational work, but pricing and flexibility vary. The right choice depends on whether your team values control, simplicity, real-time performance, or lower maintenance.

If your main destination is Databricks and your sources are already supported, native ingestion through Databricks Lakeflow Connect may be enough. If you need CDC, multi-destination delivery, or lower-latency movement across different systems, a third-party platform may still be the better fit.

Best ETL Tools for Databricks Integration in 2026

1. Databricks Lakeflow Connect: Best Native Databricks Ingestion Option

Databricks Lakeflow Connect is Databricks’ native ingestion option for moving data from supported SaaS applications, databases, files, and cloud storage into Databricks. It is governed by Unity Catalog and runs on Databricks serverless compute, which makes it a natural first option for teams already standardized on the Databricks platform.

Databricks also offers a Lakeflow Connect Free Tier with 100 free DBUs per day for eligible managed connectors, which can support ingestion of up to 100 million records per day across eligible sources.

Key Benefits of Using Lakeflow Connect with Databricks

• Native Databricks experience: Lakeflow Connect is built directly into the Databricks ecosystem, so teams can manage ingestion closer to their lakehouse workflows.
• Unity Catalog governance: Ingestion pipelines fit into Databricks’ governance layer for access control, lineage, and discovery.
• Serverless execution: Pipelines run on Databricks serverless compute, reducing infrastructure management for supported ingestion patterns.
• Good fit for Databricks-first teams: If Databricks is your main destination and your sources are covered, Lakeflow Connect can reduce the need for an external ingestion tool.

Lakeflow Connect is a strong fit when Databricks is your primary destination and your required sources are supported. It may not be ideal when you need multi-destination fan-out, sub-second CDC from operational databases, broader connector coverage outside Databricks, or more control over pipeline behavior across multiple platforms.

2. Estuary: Best for Real-Time CDC and Multi-Destination Delivery

Estuary is a real-time data movement platform for teams that need fresh operational data in Databricks and other systems. It supports CDC, streaming, and batch patterns, so teams can move data from databases, SaaS applications, event streams, and files into Databricks without building and maintaining custom pipelines.

Key Benefits of Using Estuary with Databricks

• Real-time CDC and batch support: Capture changes from operational databases and deliver them into Databricks using streaming or scheduled patterns.
• Multi-destination fan-out: Capture data once and materialize it to Databricks, Snowflake, Redshift, PostgreSQL, ClickHouse, Kafka, and other systems.
• Databricks connector support:Estuary’s Databricks connector supports standard updates and delta updates for keeping Databricks tables current without full reloads.
• Enterprise deployment options: Private Deployment and Bring Your Own Cloud options help teams meet stricter security and compliance needs.
• Lower pipeline maintenance: Built-in schema evolution, CDC, backfills, and no-code setup reduce custom engineering work.

Estuary is a strong fit for teams that need real-time CDC into Databricks, multi-destination delivery, and secure deployment options. It may not be ideal for teams that only need simple native ingestion into Databricks from sources already covered by Lakeflow Connect.

2. Fivetran: Best for Managed ELT and Broad SaaS Ingestion

Fivetran is a fully managed data movement platform used by many enterprise teams to move data from SaaS applications, databases, files, and APIs into Databricks. It is known for automated connector maintenance, schema drift handling, and a large connector ecosystem.

For a 2026 evaluation, Fivetran should also be viewed as more than a traditional ELT tool. Fivetran completed its merger with dbt Labs on June 1, 2026, after the all-stock deal was announced in October 2025. It also expanded its platform through the 2025 acquisitions of Census for reverse ETL and data activation, and Tobiko Data, the company behind SQLMesh and SQLGlot, for advanced transformation workflows.

Key Benefits of Using Fivetran with Databricks

• Managed Databricks ingestion: Fivetran supports Databricks as a destination and can help teams move data into Databricks tables without maintaining custom pipelines.
• Large connector ecosystem: It covers many SaaS applications, databases, and enterprise systems, which makes it useful for teams with a broad source mix.
• Low maintenance: Automated connector updates, schema drift handling, and managed infrastructure reduce day-to-day pipeline work.
• Expanded platform direction: With dbt Labs, Census, and Tobiko Data now part of the story, Fivetran is positioning itself around ingestion, transformation, and activation rather than only source-to-warehouse ELT.

Fivetran is a strong fit for teams that want managed, low-maintenance pipelines into Databricks and are comfortable with its platform model. It may not be ideal for teams that need sub-second CDC, fine-grained control over pipeline behavior, or very predictable costs at high data volume. Since pricing models can change, especially after major platform changes, buyers should verify current pricing directly before making a decision.

3. Airbyte: Best for Open-Source Flexibility

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: Best for Visual Transformation Workflows

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.

5. Informatica: Best for Enterprise Governance and Legacy Systems

Informatica is a long-established enterprise data integration and data management platform used by large organizations with complex governance, compliance, and legacy system requirements. It is often considered by enterprises that need more than basic ingestion, including data quality, metadata management, cataloging, lineage, privacy controls, and master data management.

A key 2026 update is ownership. Salesforce completed its acquisition of Informatica on November 18, 2025. For enterprise buyers, this matters because Informatica is now part of Salesforce’s broader data and AI strategy, not just an independent integration vendor.

Key Benefits of Using Informatica with Databricks

• Enterprise data management depth: Informatica brings capabilities across integration, data quality, metadata, cataloging, governance, privacy, and MDM.
• Governance-focused workflows: It is suited for organizations that need strict controls around lineage, access, auditing, and compliance.
• Legacy and enterprise source coverage: Informatica is often used in environments with older databases, ERP systems, regulated data, and complex enterprise architecture.
• Salesforce ecosystem alignment: Now that Informatica is part of Salesforce, it may be especially relevant for organizations already invested in Salesforce Data Cloud, Agentforce, MuleSoft, Tableau, or broader Salesforce data initiatives.

Informatica is a good fit for large enterprises that need deep governance and data management around Databricks pipelines. It may not be ideal for smaller teams or fast-moving data teams that want quick setup, simpler pricing, or lightweight real-time ingestion without a large enterprise implementation.

6. Skyvia: Best for No-Code ETL, ELT, and Sync

Skyvia is a no-code cloud ETL and ELT platform for teams that want to move data into Databricks without managing infrastructure or building custom ingestion pipelines. It supports ETL, ELT, reverse ETL, synchronization, and automation through a visual interface that's easy to use for both business and technical teams.

Key Benefits of Using Skyvia with Databricks

• Broad connector coverage: 200+ connectors for SaaS apps, databases, cloud warehouses, and flat files, including Salesforce, HubSpot, SQL Server, PostgreSQL, BigQuery, and more.
• No-code ETL and ELT: build pipelines for Databricks through a visual interface, without managing infrastructure or writing extensive custom code.
• On-premises connectivity: Skyvia Agent enables secure access to internal databases and systems without exposing infrastructure publicly.
• Ease of use: fast onboarding and a clean interface make Skyvia easy to adopt for both data teams and business users.

Skyvia is a good fit for teams that want a simple no-code ETL, ELT, sync, and automation platform for Databricks. It may not be ideal for teams that need advanced streaming CDC, complex engineering control, or highly customized real-time pipelines.

Comparison Table: ETL Tools for Databricks

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

Conclusion

The best ETL tool for Databricks depends on how your team uses the lakehouse. If Databricks is your only destination and your sources are supported, Lakeflow Connect is the most natural place to start. If you need managed SaaS ingestion, Fivetran is a strong option. If you want open-source flexibility, Airbyte may fit better. For transformation-heavy workflows, Matillion is worth considering, while Informatica is better suited to large enterprises with governance and legacy system requirements. Skyvia is useful for teams that want simple no-code ETL, ELT, sync, and automation.

For teams that need real-time CDC into Databricks, multi-destination delivery, and secure deployment options, Estuary is a strong fit.

Want to move real-time data into Databricks without building custom pipelines? Start with Estuary’s Databricks connector.