12 Best ETL Tools for Databricks in 2025: A Marketing Analyst's Guide

Marketing analysts today face a clear challenge: campaign data lives in dozens of platforms — Google Ads, Meta, LinkedIn, Salesforce, HubSpot, TikTok — and Databricks is where that data needs to land for modeling, attribution, and reporting.

But moving data into Databricks isn't a one-click operation. You need an ETL tool that understands marketing schemas, handles API rate limits, preserves historical data when platforms change their structure, and doesn't require a data engineer to babysit every connector.

This guide evaluates 12 ETL solutions built for or compatible with Databricks. Each section covers what the tool does well, where it falls short, and who should consider it. By the end, you'll know which platform fits your team's technical depth, budget, and reporting requirements.

Key Takeaways

✓ Marketing-specific ETL tools like Improvado offer 500+ pre-built connectors and preserve historical data when ad platforms change APIs, eliminating manual schema fixes.

✓ General-purpose tools like Fivetran and Airbyte provide broad connector libraries but often require SQL or Python to map marketing metrics to your data model.

✓ Databricks-native options like Delta Live Tables integrate deeply with your lakehouse but demand Spark knowledge and developer time to build and maintain pipelines.

✓ Open-source frameworks (Apache Spark, NiFi, dbt) give full control but shift maintenance, monitoring, and connector builds entirely to your team.

✓ Evaluate tools on connector coverage for your stack, transformation logic you can manage without engineering, and whether the vendor handles breaking API changes for you.

✓ The right choice depends on whether you need analyst-friendly automation or developer-driven customization — most marketing teams get stuck when they pick a tool built for the wrong persona.

What Is an ETL Tool for Databricks?

An ETL tool for Databricks is software that extracts data from source systems, transforms it into a usable structure, and loads it into Databricks tables. For marketing teams, this means pulling campaign metrics, customer interactions, and conversion events from advertising platforms, CRMs, and analytics tools into a unified lakehouse environment.

Databricks itself is a data platform — it stores and processes data at scale. But it doesn't pull data from Google Ads or Salesforce on its own. That's where ETL tools come in. They handle API authentication, schema mapping, incremental updates, and error recovery so your Databricks tables stay current without manual CSV uploads or custom scripts.

How to Choose an ETL Tool for Databricks: Specific Criteria

Not all ETL tools are built the same. Marketing analysts need to evaluate platforms on five dimensions that directly affect whether your pipelines run reliably without constant intervention.

Connector library depth. Does the tool natively support your ad platforms, attribution tools, and CRMs? Generic connectors that dump raw API responses force you to write transformation logic for every field. Marketing-specific tools map platform schemas to standardized tables automatically.

Transformation layer accessibility. Can you build calculated fields, join datasets, and apply business rules without writing Spark code? Analyst-friendly tools provide visual transformation builders or SQL interfaces. Developer-focused tools assume you'll write Python or Scala.

Historical data preservation. When Facebook changes the Ads API or Google Ads deprecates a metric, does the tool backfill your tables or does your historical data break? Vendors that maintain schema compatibility save dozens of hours per API migration.

Monitoring and error handling. If a connector fails at 3 AM, do you get an alert with a clear fix, or do you discover missing data when a dashboard goes blank? Enterprise tools include built-in monitoring, automatic retries, and support SLAs. Open-source tools require you to build this yourself.

Cost structure. Some vendors charge per row, others per connector, others per data volume. For marketing data — where one campaign can generate millions of impression rows — pricing models that penalize scale become prohibitively expensive as you grow.

Improvado: Marketing-Specific ETL Built for Multi-Channel Attribution

Improvado is an ETL platform designed specifically for marketing analytics. It connects 500+ advertising platforms, analytics tools, and CRMs directly to Databricks with pre-built connectors that map each platform's metrics to a standardized schema.

Pre-built marketing data models eliminate transformation work

Most ETL tools dump raw API data into your warehouse and leave schema design to you. Improvado includes a Marketing Cloud Data Model (MCDM) — pre-built tables for campaigns, ad groups, creatives, conversions, and spend that work across all connected platforms. You don't write SQL to join Google Ads and Meta data; the tool structures it consistently from day one.

The platform preserves historical data when ad platforms change their APIs. When Google Ads deprecates a field or Meta renames a metric, Improvado maps the new schema to your existing tables automatically. You don't wake up to broken dashboards or missing columns.

For teams managing complex attribution models, Improvado supports 46,000+ marketing metrics and dimensions out of the box. You can pull granular data — creative-level engagement, geo-specific conversion rates, hour-by-hour spend — without custom API calls.

Not ideal for non-marketing data sources

Improvado's connector library focuses on marketing and sales platforms. If you need to integrate ERP systems, IoT sensors, or internal databases, you'll need to request a custom connector build (delivered in 2–4 weeks) or use a different tool for those sources.

The platform is priced for mid-market and enterprise teams. Small businesses running a handful of ad accounts may find more cost-effective solutions in general-purpose ETL tools, though they'll trade off the marketing-specific automation.

Fivetran: Broad Connector Library with Automated Schema Drift Detection

Fivetran is a general-purpose ETL platform with 400+ connectors spanning databases, SaaS applications, and advertising platforms. It handles schema changes automatically, adding new columns to your Databricks tables when source systems introduce new fields.

Automated maintenance for evolving source schemas

Fivetran monitors each data source for schema changes and updates your warehouse tables without manual intervention. When Salesforce adds a custom field or Shopify introduces a new order attribute, the connector appends the column to your existing table and backfills historical records.

The platform uses log-based replication for databases, capturing changes at the transaction level. For marketing teams pulling data from PostgreSQL or MySQL databases that store customer interactions, this provides near-real-time sync without impacting source system performance.

Transformation logic requires separate tooling

Fivetran loads raw data into Databricks but doesn't include a built-in transformation layer. You'll need to use dbt, Databricks SQL, or custom Spark jobs to map advertising platform fields to your reporting schema. For analysts without SQL experience, this creates a dependency on engineering resources.

Marketing-specific connectors — Google Ads, Meta, LinkedIn — provide basic metrics but don't normalize data across platforms. You'll write joins and field mappings manually to compare campaign performance across channels.

Airbyte: Open-Source ETL with Custom Connector Framework

Airbyte is an open-source data integration platform with 300+ pre-built connectors and a framework for building custom sources. It runs as a self-hosted application or managed cloud service, loading data into Databricks via JDBC or cloud storage.

Custom connector development for niche platforms

Airbyte's Connector Development Kit (CDK) lets you build connectors for proprietary APIs or niche advertising platforms not covered by commercial vendors. The framework uses Python and includes templates for REST APIs, GraphQL endpoints, and bulk data exports.

For marketing teams using regional ad networks or custom attribution platforms, this flexibility solves the "unsupported source" problem. But it requires developer time to build, test, and maintain each connector as APIs evolve.

Maintenance overhead for self-hosted deployments

Self-hosted Airbyte requires infrastructure management — provisioning servers, monitoring uptime, handling version upgrades. When a connector breaks due to an API change, you're responsible for debugging and patching it.

The managed cloud version eliminates infrastructure work but charges per data volume, which can become expensive for high-frequency marketing data like impressions or clickstream events.

Matillion: Cloud-Native ETL with Visual Transformation Builder

Matillion is a cloud-native ETL platform designed for data warehouses and lakehouses. It provides a drag-and-drop interface for building pipelines and includes pre-built connectors for advertising platforms, databases, and SaaS applications.

Visual pipeline builder for analyst-friendly transformations

Matillion's transformation layer uses a visual canvas where you drag components to join datasets, filter rows, and calculate new fields. Analysts can build complex logic — multi-touch attribution models, customer lifetime value calculations — without writing SQL or Spark code.

The platform pushes transformation work down to Databricks, executing queries as native Spark jobs. This approach uses your existing compute resources efficiently and avoids data movement between systems.

Limited granularity in marketing connectors

Matillion's advertising platform connectors cover major networks — Google Ads, Meta, LinkedIn — but don't expose all available dimensions and metrics. For example, you may not get creative-level engagement data or hourly spend breakdowns without custom API calls.

The tool is optimized for batch processing. If you need near-real-time data sync for intraday campaign optimization, you'll need to configure short sync intervals, which increases compute costs.

Apache Spark: Low-Level Framework for Custom Data Pipelines

Apache Spark is the distributed processing engine that powers Databricks. You can use Spark directly to build ETL pipelines, reading data from APIs or cloud storage, transforming it with Python or Scala code, and writing results to Delta tables.

Full control over extraction and transformation logic

Writing Spark jobs from scratch gives you complete flexibility. You define exactly how data is extracted, validated, transformed, and loaded. For teams with complex business rules or non-standard data sources, this eliminates the constraints of pre-built connectors.

Spark handles large-scale transformations efficiently. You can process billions of rows, apply machine learning models, or run custom aggregations that would be difficult to express in a visual ETL tool.

Requires dedicated engineering resources

Building and maintaining Spark ETL pipelines demands developer expertise. You're responsible for API authentication, error handling, incremental updates, and monitoring. When an ad platform changes its API, you patch your code manually.

For marketing teams without in-house data engineers, the time investment becomes a bottleneck. Simple tasks — adding a new data source, fixing a broken connector — require development sprints instead of configuration changes.

Databricks Delta Live Tables: Native Lakehouse ETL with Declarative Pipelines

Delta Live Tables (DLT) is Databricks' managed ETL framework. You define pipelines using SQL or Python, and DLT handles orchestration, schema enforcement, and data quality checks automatically.

Native integration with Databricks features

DLT pipelines run directly within your Databricks workspace, using your existing compute clusters and storage. You don't move data between systems or manage external ETL infrastructure. Pipelines update incrementally, processing only new or changed records to minimize compute costs.

The framework includes built-in data quality enforcement. You define expectations — "revenue must be positive," "email addresses must be valid" — and DLT logs violations, quarantines bad records, or fails the pipeline based on your rules.

Doesn't extract data from external APIs

DLT transforms and loads data within Databricks, but it doesn't pull data from external sources. You still need a separate tool or custom code to extract data from Google Ads, Salesforce, or other marketing platforms and land it in cloud storage or Databricks tables before DLT can process it.

For marketing teams, this means using DLT alongside another ETL tool — one that handles extraction, and DLT for transformation. That adds architectural complexity and requires coordinating two systems.

Talend: Enterprise Data Integration with Governance Features

Talend is an enterprise data integration platform with ETL, data quality, and governance tools. It supports on-premises and cloud deployments, connecting to Databricks via JDBC or cloud storage connectors.

Data governance and lineage tracking

Talend includes metadata management and lineage tracking, showing how data flows from source systems through transformations to final reports. For marketing teams managing compliance requirements — GDPR, CCPA — this provides audit trails for every data element.

The platform's data quality tools profile incoming data, flag anomalies, and enforce validation rules before loading data into Databricks. You can catch issues — duplicate records, missing campaign IDs, malformed dates — before they reach your analytics layer.

Steep learning curve for analysts

Talend's interface is built for data engineers, not marketing analysts. Configuring connectors, building transformations, and debugging pipelines requires familiarity with ETL concepts and Java-based components. Analysts typically depend on IT teams to build and modify pipelines.

The platform's licensing model is enterprise-focused, with pricing that reflects its breadth of features. Smaller marketing teams may find the cost and complexity exceed their needs.

Stitch: Simplified ETL with Fast Setup

Stitch is a cloud ETL service (owned by Talend) designed for quick deployment. It offers 130+ connectors and loads data into Databricks with minimal configuration, targeting teams that need basic replication without complex transformation logic.

Quick deployment for standard sources

Stitch pipelines go live in minutes. You authenticate a data source, select tables or metrics to sync, and the tool replicates data to Databricks on a schedule you define. For marketing teams that need raw advertising data in their warehouse quickly, this removes setup friction.

The platform handles incremental updates automatically, syncing only new or changed records after the initial load. This reduces data transfer costs and keeps tables current without full refreshes.

No transformation layer included

Stitch replicates data as-is. You get the exact structure provided by the source API, which often means nested JSON fields, inconsistent naming conventions, and platform-specific schemas. Marketing analysts need to use dbt or SQL queries to transform this into a usable reporting schema.

The connector library is narrower than competitors like Fivetran or Improvado. If you use niche advertising platforms or custom attribution tools, you may not find a pre-built connector.

Informatica: Legacy Enterprise ETL with Cloud Extensions

Informatica is an established enterprise data integration platform with ETL, data quality, and master data management tools. It connects to Databricks through cloud connectors or JDBC, supporting both on-premises and cloud data sources.

Enterprise-grade features for complex environments

Informatica handles complex integration scenarios — merging data from legacy on-premises systems with modern cloud applications, applying intricate transformation logic, enforcing enterprise data governance policies. For large organizations with hybrid infrastructure, this breadth is valuable.

The platform includes AI-powered data mapping, suggesting transformations based on source and target schemas. This accelerates pipeline development when you're integrating new data sources.

High cost and implementation complexity

Informatica deployments typically require professional services and months of implementation work. The platform's feature set is vast, and configuring it for marketing analytics use cases demands specialized expertise.

Licensing costs are enterprise-tier. For marketing teams focused specifically on advertising and CRM data, lighter-weight tools deliver comparable results at a fraction of the cost and complexity.

Apache NiFi: Flow-Based Data Routing with Real-Time Capabilities

Apache NiFi is an open-source data integration platform that routes and transforms data using a visual flow-based interface. It supports real-time data movement and connects to Databricks via REST APIs or cloud storage.

Real-time data routing for event streams

NiFi processes data as it arrives, making it suitable for real-time use cases — streaming clickstream events, processing webhook notifications from ad platforms, or routing data based on dynamic conditions. Marketing teams using event-driven architectures benefit from this low-latency processing.

The visual interface shows data flows as a directed graph, making it easier to understand how data moves through transformation steps compared to reading code.

Operational overhead for production deployments

Running NiFi in production requires infrastructure management — provisioning clusters, configuring high availability, monitoring performance. For marketing teams without DevOps resources, this operational burden diverts focus from analytics.

The platform is flexible but not opinionated. You build everything from scratch, including error handling, retry logic, and data validation. This flexibility becomes complexity when you need reliable, maintainable pipelines.

AWS Glue: Serverless ETL for AWS-Native Environments

AWS Glue is Amazon's managed ETL service, designed for data lakes built on S3 and analytics workloads in AWS. It connects to Databricks running on AWS and handles schema discovery, job scheduling, and serverless compute.

Serverless compute with pay-per-use pricing

Glue eliminates infrastructure management. You define ETL jobs using Python or Scala, and AWS provisions compute resources automatically when jobs run. You pay only for the time your jobs execute, which can be cost-effective for intermittent workloads.

The service integrates deeply with other AWS tools — S3 for storage, Athena for querying, IAM for access control. If your marketing data already lives in AWS, Glue fits naturally into your architecture.

Limited pre-built connectors for marketing platforms

Glue connects easily to AWS services and JDBC databases, but it doesn't include native connectors for advertising platforms like Google Ads or Meta. You'll need to write custom code to extract data from these APIs, handle authentication, and manage rate limits.

For marketing teams, this means Glue works well as a transformation and orchestration layer, but you'll need another tool or custom development to get data from ad platforms into AWS in the first place.

dbt: Transformation-Focused Tool for Analytics Engineering

dbt (data build tool) is an open-source framework for transforming data inside your warehouse. It doesn't extract data from sources, but it organizes and automates the SQL queries that turn raw data into analytics-ready tables.

Version-controlled transformations with testing

dbt treats SQL transformations as code, storing them in Git repositories with version history, peer review, and automated testing. You define models — SELECT statements that create derived tables — and dbt handles dependencies, running transformations in the correct order.

The framework includes data quality tests: assert that revenue is never null, campaign IDs are unique, or conversion dates fall within valid ranges. These tests run automatically, catching data issues before they reach dashboards.

Doesn't extract data from external sources

dbt assumes data already exists in your warehouse. You need a separate ETL tool to pull data from Google Ads, Salesforce, or other marketing platforms into Databricks before dbt can transform it.

For teams without SQL expertise, writing and maintaining dbt models requires a learning curve. The framework is powerful but developer-focused, not designed for analysts who prefer visual interfaces or no-code tools.

ETL Tools for Databricks: Comparison Table

How to Get Started with ETL for Databricks

Audit your data sources. List every platform you need to connect — advertising networks, CRMs, analytics tools, databases. Note which ones change their APIs frequently (social platforms) versus stable sources (internal databases). This inventory determines whether you need a tool with deep marketing connectors or a general-purpose platform.

Define who builds and maintains pipelines. If marketing analysts will configure connectors and transformations, choose a tool with a no-code interface and pre-built models. If data engineers will own the pipelines, developer-focused tools like Spark or Airbyte become viable. Mismatching tool complexity to team skills creates bottlenecks.

Evaluate transformation requirements. Do you need simple field mapping, or complex logic like multi-touch attribution and customer lifetime value calculations? Tools like Improvado and Matillion handle complex transformations visually. Tools like Stitch and Fivetran require you to build transformation logic separately in dbt or SQL.

Test with a pilot data source. Start with one high-value connector — Google Ads or Salesforce — and run it through a proof-of-concept. Measure setup time, data accuracy, and how much manual work is required to get usable tables. This reveals hidden complexity before you commit to a platform.

Plan for API changes. Ask vendors how they handle breaking API changes from source platforms. Do they update connectors automatically and preserve historical data, or do you need to manually fix schema mismatches? For marketing data, where platforms change APIs frequently, this support model determines long-term maintenance burden.

Calculate total cost of ownership. Compare not just software licensing, but also the engineering time required to configure, monitor, and maintain pipelines. A tool with a higher license cost but lower maintenance needs often delivers better ROI than a cheaper option that consumes developer time every week.

Conclusion

Choosing an ETL tool for Databricks comes down to three questions: how many marketing-specific connectors you need, who on your team will build and maintain pipelines, and whether you want a vendor to handle API changes or manage them yourself.

Marketing teams running multi-channel campaigns benefit most from tools that understand advertising schemas, normalize metrics across platforms, and preserve historical data when APIs evolve. General-purpose tools work well if you have engineering resources to build transformation logic and maintain custom connectors.

The right choice depends on your team's technical depth and how much time you want to spend on data plumbing versus analysis. Evaluate tools on the specific connectors you need, the transformation complexity you can realistically manage, and the total cost of keeping pipelines running reliably as your data sources grow.

Frequently Asked Questions

What is the difference between ETL and ELT for Databricks?

ETL transforms data before loading it into Databricks, processing it in the ETL tool's environment. ELT loads raw data into Databricks first, then transforms it using Databricks' compute resources. ELT is more common with modern data lakehouses because Databricks handles large-scale transformations efficiently. Marketing teams often use ELT when they need flexibility to re-transform data as business logic changes without re-extracting from source APIs.

Can I use multiple ETL tools with Databricks simultaneously?

Yes, Databricks accepts data from multiple ETL tools concurrently. You might use Improvado for marketing data, Fivetran for database replication, and dbt for transformations. This multi-tool approach works when different sources require specialized connectors, but it adds complexity in monitoring, cost management, and ensuring consistent data quality across pipelines. Teams typically consolidate to fewer tools as they mature to reduce operational overhead.

How do ETL tools handle Databricks Unity Catalog?

Modern ETL tools connect to Databricks via Unity Catalog, writing data to managed tables with centralized governance. This integration enforces access controls, lineage tracking, and data classification policies defined in Unity Catalog. When evaluating tools, verify they support Unity Catalog authentication and respect catalog-level permissions — this prevents data governance gaps where ETL processes bypass organizational access policies.

What happens when an ad platform changes its API?

Vendor-managed ETL tools monitor API changes and update connectors automatically, preserving historical data by mapping deprecated fields to new schema structures. Self-managed tools (Spark, Airbyte) require you to update extraction code manually when APIs change. For marketing teams, this difference determines whether a platform API update causes hours of emergency fixes or happens transparently. Ask vendors for their SLA on connector updates after breaking API changes.

How much historical data can ETL tools backfill into Databricks?

Backfill limits depend on the source platform's API, not the ETL tool. Google Ads typically allows 4 years of historical data, Meta provides 2 years, and some analytics tools limit exports to 13 months. ETL tools retrieve whatever the API permits during initial sync. Marketing-specific platforms like Improvado preserve 2 years of historical data even when APIs change schema, preventing data loss during migrations.

Do ETL tools work with Databricks serverless compute?

Yes, ETL tools load data into Databricks tables regardless of the underlying compute model. Databricks serverless, SQL warehouses, and classic clusters all read from the same Delta tables. The ETL tool doesn't interact with compute resources directly — it writes data to storage, and Databricks compute reads it. This separation means you can change Databricks compute configurations without reconfiguring ETL pipelines.

What is the typical data latency for marketing ETL pipelines?

Most marketing ETL tools sync data every 1–24 hours, depending on pricing tier and source API rate limits. Ad platforms like Google Ads and Meta update metrics with 24–48 hour latency due to attribution windows and conversion tracking delays, so more frequent syncs don't always provide fresher data. Real-time use cases — like intraday budget pacing — require streaming connectors or API-based event pipelines, which few marketing ETL tools support natively.

How do I monitor ETL pipeline failures in Databricks?

Managed ETL tools include built-in monitoring dashboards, alerting you via email or Slack when pipelines fail. They log error details, retry failed jobs automatically, and provide support SLAs. Self-managed pipelines (Spark, Airflow) require you to build monitoring using Databricks Jobs APIs, CloudWatch, or external observability tools. Marketing teams without DevOps resources benefit from vendor-managed monitoring to avoid discovering data gaps days after they occur.