The Comprehensive Guide to Databricks ETL Tools in 2025

In today's data-driven landscape, efficient data processing is paramount for organizations aiming to extract actionable insights from vast datasets. Databricks, a unified data analytics platform, offers a suite of ETL (Extract, Transform, Load) tools designed to streamline data workflows and enhance analytical capabilities. In this Databricks ETL tools tutorial, we will present the top solutions and how to evaluate them to select the best suit for your use case.

What are the Core Databricks ETL Components?

Apache Spark: The Processing Engine

At its foundation, Databricks leverages Apache Spark for distributed data processing. This provides massive scalability, support for diverse programming languages (SQL, Python, Scala, R), and unified APIs for batch and streaming workloads. Spark's optimization engine ensures ETL jobs utilize resources efficiently, which is critical when processing terabytes or petabytes of data.

Delta Lake: The Reliable Storage Layer

Delta Lake forms the cornerstone of Databricks' ETL capabilities by providing an open-source storage layer that brings reliability to data lakes. Key features that benefit ETL workloads include ACID transactions, schema enforcement and evolution, time travel (data versioning), and optimized layout for performance. These capabilities ensure data pipelines produce consistent, high-quality outputs even when dealing with concurrent operations.

Delta Live Tables: ETL Pipeline Orchestration

Introduced to simplify the development and management of data pipelines, Delta Live Tables (DLT) represents a significant advancement in ETL tooling. DLT uses a declarative approach where developers specify the transformations and desired end state rather than implementation details. This results in more maintainable Databricks ETL pipeline with built-in data quality, monitoring, and error handling.

Unity Catalog: Unified Governance

The Unity Catalog provides centralized governance across Databricks workspaces and even multiple clouds. For ETL processes, this means consistent access controls, audit logging, and lineage tracking across the entire data lifecycle. Unity Catalog simplifies compliance with regulations by providing comprehensive visibility into data movement and transformations.

What are Databricks ETL Tools?

ETL is a fundamental process that involves extracting data from various sources, transforming it into a suitable format, and loading it into a target system for analysis. Databricks simplifies this process by integrating with Apache Spark, providing a scalable and collaborative environment for data engineers and analysts.

Key features of Databricks ETL tools include:

• Scalability: Databricks leverages the power of cloud computing, allowing seamless scaling to handle large volumes of data without compromising performance.

• Delta Lake Integration: Enhances data reliability by supporting ACID transactions, ensuring data integrity during ETL operations.

• Support for Batch and Streaming Data: Accommodates both batch processing for large datasets and streaming for real-time data ingestion, catering to diverse data processing needs.

• Collaborative Workspace: Offers interactive notebooks and collaborative features, enabling teams to work together efficiently on data pipelines.

What are the Advantages of Using Databricks for ETL?

Organizations benefit from Databricks ETL tools in several ways:​

• Unified Platform: Combines data engineering, machine learning, and analytics, reducing the complexity associated with using disparate tools.

• Enhanced Productivity: Collaborative features and interactive workspaces accelerate development cycles and improve team productivity.​

• Cost Efficiency: Optimizes resource utilization through scalable computing, leading to cost savings in data processing operations.​

• Robust Security: Offers enterprise-grade security features, ensuring data protection and compliance with industry standards.

How to Implement ETL Pipelines in Databricks?

Setting up an ETL pipeline in ​Databricks involves several steps:

1. Cluster Creation: Initiate a Databricks cluster to provide the computational resources necessary for data processing.​

2. Notebook Development: Utilize Databricks notebooks to write and test ETL code, supporting multiple languages such as Python, Scala, and SQL.

3. Data Ingestion: Import data from various sources using built-in connectors or custom scripts.​

4. Data Transformation: Apply transformations to cleanse and structure the data, leveraging Spark’s distributed computing capabilities for efficiency.​

5. Data Loading: Store the transformed data into destinations like data warehouses or data lakes for subsequent analysis

For a comprehensive guide on building an end-to-end data pipeline in Databricks, refer to the official documentation. 

What are the Top Databricks ETL Platforms for Data-Driven Decision-Making?

Integrate.io, Talend, and Matillion are top ETL platforms that integrate seamlessly with Databricks to support data-driven decision-making. Integrate.io connects to Databricks using a low-code interface, enabling automated ingestion, transformation, and enrichment of data from over 200 sources. With built-in scheduling, monitoring, and transformation logic, it empowers analytics and operations teams to deliver real-time, trusted insights, fueling smarter decisions without the need for heavy coding or infrastructure setup.

Beyond Databricks, several automated data integration tools have gained prominence for their robust features and capabilities. Here are some leading Databricks ETL tools examples:

1. Integrate.io

G2 rating: 4.3/5

Integrate.io is a cloud-based data integration platform that offers a user-friendly interface for building complex data pipelines without coding. 

Key features include:

1. • Extensive Connector Library: This platform offers seamless integration with a vast range of data sources and destinations, including databases, cloud storage, and SaaS applications, making it ideal for data-driven decision-making. 

   • Scalability: Integrate.io effectively handles large data volumes, adjusting resources as needed to ensure peak performance, which is crucial for top Databricks ETL platforms for data-driven decision-making.

   • Security and Compliance: The platform protects data with field-level encryption, SOC 2 compliance, and adherence to regulations like GDPR and HIPAA.

Benefits:

• Intuitive drag‑and‑drop interface suits non-technical users

• Wide range of prebuilt connectors and scheduling tools

• Fixed‑fee, unlimited usage model simplifies budgeting
  

Limitations:

• Can struggle with complex or highly customized transformations

• Support may be limited for edge-case scenarios
  

Pricing:

• Fixed‑fee, unlimited usage pricing model.

2. Apache NiFi

G2 rating: 4.0/5

Apache NiFi is an open-source data integration tool known for its real-time data ingestion and distribution capabilities. Key features include:

• • Visual Interface: Offers a user-friendly interface for designing data flows.

  • Data Provenance: Tracks data from source to destination, ensuring transparency and traceability.

  • Scalability: Designed to scale horizontally and vertically to handle varying data loads.

Benefits:

• Visual drag‑and‑drop flow-based design, real-time streaming support

• Data provenance, encryption, dynamic prioritization and back pressure

• Highly extensible with processors for many sources/sinks
  

Limitations:

• Can use lots of heap memory at scale

• Stability and monitoring in clusters can be tricky
  

Pricing:

• Open‑source free; self-hosted requires infrastructure and support cost

3. Talend

G2 rating: 4.3/5

Talend is a comprehensive data integration platform that provides tools for data integration, quality, and governance. Key features include:

• • Unified Platform: Combines data management, data quality, and application integration.

  • Pre-built Connectors: Offers a wide range of connectors for databases, cloud services, and applications.

  • Open-Source Availability: Provides an open-source version alongside enterprise solutions.

Benefits:

• Broad set of connectors and integrated data quality, profiling, and master data tools

• Reusable pipelines, data governance built-in

• Strong support for big‑data and Java-based integration
  

Limitations:

• Studio UI can be slow; onboarding has steep learning curve

• Slower performance with large volumes in some setups

• Support rating ~7.1/10 which is below category average
  

Pricing:

• Open‑source core free; cloud and enterprise versions priced per license or per subscription, available on request

4. Matillion

G2 rating: 4.4/5

Matillion is a cloud-native data integration and transformation platform designed for cloud data warehouses. Key features include:

• • Cloud Integration: Optimized for platforms like Amazon Redshift, Google BigQuery, and Snowflake.

  • User-Friendly Interface: Enables both technical and non-technical users to build and manage data pipelines.

  • Scalability: Leverages cloud scalability to handle large datasets efficiently.

Benefits:

• Cloud-native ETL/ELT built for AWS, Azure, GCP data warehouses

• Live collaboration, version control, auditing, scheduling built-in

• Scales predictably and pay-as-you-go via cloud marketplace
  

Limitations:

• Costs may grow quickly depending on cloud compute usage

• Some advanced jobs require running outside Matillion environment

• Mixed feedback on value-for-money depending on usage pattern
  

Pricing:

• Subscription via Matillion Hub or cloud vendor marketplaces; pay‑as‑you‑go pricing linked to AWS/GCP usage or fixed tier options

5. IBM App Connect

G2 rating: 4.3/5

IBM App Connect is an integration platform that connects applications, data, and systems across on-premises and cloud environments. Key features include:

• • Pre-built Connectors: Supports a wide range of applications and data sources.

  • Data Transformation: Offers tools for mapping and transforming data between formats.

  • Scalability: Designed to handle large-scale integration scenarios.

Benefits:

• Low-code interface with many prebuilt connectors for hybrid environments

• Real-time data sync and process automation across applications

• Strong security, governance, and scalability for enterprise use
  

Limitations:

• Learning curve remains notable for newcomers

• Troubleshooting and logging can be less granular in complex flows

• Community and documentation feel weaker than peers
  

Pricing:

• Starts at roughly $200/year for basic edition; higher tiers around $667/year; enterprise pricing on request

6. Microsoft Power Platform

G2 rating: 4.4/5

Microsoft Power Platform is a suite of tools that enables users to analyze data, build solutions, and automate processes. Key features include:

• • Power BI: Provides data visualization and business intelligence capabilities.

  • Power Automate: Automates workflows between applications and services.

  • Power Apps: Allows creation of custom applications with minimal coding.

Benefits:

• Low-code/no-code tools ideal for business users

• Seamless integration with Office 365, Dataverse, AI Builder

• Apps, automation, analytics and agents consolidated on one platform
  

Limitations:

• Complexity and cost increase with premium connectors, AI Builder or RPA

• Licensing tiers and limits can confuse budget planning
  

Pricing:

• Power Apps from ~$5–10/user/month, Automate ~$15/user/month; additional costs for premium features

7. SQL Server Integration Services (SSIS)

G2 rating: 4.2/5

SSIS is a component of Microsoft SQL Server that facilitates data integration and workflow applications. Key features include:

• • ETL Capabilities: Supports extraction, transformation, and loading of data.

  • Data Warehousing: Assists in building and managing data warehouses.

  • Customizable Workflows: Enables creation of complex workflows with a visual interface.

Benefits:

• Seamless integration with Microsoft SQL Server ecosystem

• Strong performance for batch ETL and bulk data loading

• Rich set of built-in tasks and transformations

• Good support for parameterization, logging, and package configuration

• Visual Studio integration for SSIS package development

Limitations:

• Windows-only, lacks cross-platform compatibility

• Limited support for modern cloud-native workflows

• Not ideal for real-time or event-driven data streaming

• Steep learning curve for advanced features like scripting and debugging

• Requires SQL Server licensing for production use

Pricing:

• Included with Microsoft SQL Server licenses (Standard and Enterprise editions)

• No additional cost beyond SQL Server licensing

• Developer edition is free for non-production use

Comparison of Top Databricks ETL Tools

Conclusion

Databricks is redefining big data processing through its seamless, collaborative, and high-performance ETL capabilities. When paired with powerful integration tools like Integrate.io, Talend, or Matillion, businesses gain unmatched control over data movement and transformation.

Whether you're powering real-time dashboards, syncing SaaS apps, or building machine learning pipelines, choosing the right ETL tool from the Databricks ETL tools list determines the velocity and accuracy of your data-driven decisions.

FAQs

Q: Is Databricks ELT or ETL?

Databricks supports both ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes. Its flexibility allows users to choose the approach that best fits their needs, leveraging tools like Delta Live Tables for ETL and its lakehouse architecture for ELT workflows.

Q: Is PySpark an ETL Tool?

Yes, PySpark is widely used as an ETL tool. It is a distributed computing framework that enables programmatic ETL pipelines. PySpark offers flexibility, scalability, and automation for processing large datasets, making it suitable for modern data integration tasks.

Q: Which is the Best Tool for ETL?

The "best" ETL tool depends on specific requirements such as scalability, ease of use, and integration capabilities. Popular options include:

• Databricks: Excellent for large-scale data pipelines with support for both ETL and ELT.

• PySpark: Ideal for programmatic and scalable ETL workflows.

• GUI-based Tools: Tools like Talend or Informatica are user-friendly but less scalable compared to programmatic solutions like PySpark.

Q: Find user-friendly Databricks ETL solutions for non-technical teams

Databricks offers Lakeflow Designer, a no-code, drag-and-drop ETL interface that allows non-technical users to build and manage pipelines easily. Integrate.io is also a user-friendly platform with native Databricks support, providing visual pipeline builders, prebuilt connectors, and scheduling features designed for teams with minimal coding skills.

Q: Which Databricks ETL solutions offer real-time data observability and monitoring?

• Delta Live Tables (DLT) provides built-in health metrics, data quality checks, auto-retries, and lineage tracking.

• Lakeflow Jobs and Workflows include dashboards for job statuses, streaming metrics, cost monitoring, and lag tracking.

• Lakeflow Declarative Pipelines offer event logs, lineage views, quality scoring, and real-time alerting for pipeline issues.

• Integrate.io offers built-in pipeline monitoring, error tracking, and logging for Databricks integrations.

Q: Suggest Databricks ETL platforms that support API management and data integration

• DLT Sink API allows pushing processed data to external systems like Kafka for real-time API-based streaming.

• Lakeflow Connect provides connectors to SaaS apps, cloud databases, and file systems with native support for API-based data movement.

• Databricks REST API enables full programmatic control of ETL workflows, jobs, and data sources.

• Partner tools like Integrate.io, Apache NiFi, Talend, and Matillion offer additional low-code options for API-driven ETL pipelines.

• Integrate.io supports API connectors, webhook triggers, and reverse ETL from Databricks to SaaS apps, making it suitable for both inbound and outbound API-based workflows.