Databricks vs Traditional ETL: What Growing Companies Are Choosing in 2025

Data pipelines used to be simple. Pull from source, transform in batches, load into a warehouse, and run your reports. That model worked when companies were moving slowly, collecting predictable data, and making quarterly decisions. But in 2025, none of that holds up.

Teams are dealing with thousands of events per second. Data comes in every format imaginable. Business questions change weekly. And AI projects can’t afford to wait three days for refreshed data. The cracks start to show when pipelines break during high-traffic campaigns, dashboards return stale insights, or analysts are still working from yesterday’s exports while product teams demand live metrics.

At this stage, most growing companies face a difficult question. Do you keep extending a classic ETL setup with more patches and personnel, or do you invest in something fundamentally different?

Traditional ETL tools still have a place in many environments. But the limitations are clearer now. Development cycles are slow. Streaming is often bolted on rather than built in. Handling unstructured or semi-structured data requires workarounds. And integrating with machine learning workflows can take months of engineering effort.

Databricks approaches data infrastructure differently. It has been designed to handle real-time processing, collaborative development, and machine learning from the start. 

In this article, we will examine what legacy ETL tools were built for, how their architecture impacts business agility, and why companies that want faster, AI-ready infrastructure are now looking toward Databricks. 

What Is Conventional ETL And Why It’s Showing Its Age

For decades, the ETL (Extract, Transform, Load) processes have defined how businesses move and structure their data for reporting and analysis. These systems are designed to:

• Extract data from various source systems.

• Transform the data into a suitable format or structure for querying and analysis.

• Load the transformed data into a data warehouse.

Common tools in this space include Informatica, Talend, and Microsoft SQL Server Integration Services (SSIS). These platforms have been instrumental in enabling organizations to consolidate data from disparate sources, ensuring consistency and reliability in reporting and analytics.

Designed for Batch Processing and Structured Data

Such ETL systems were architected during a time when data volumes were relatively modest, and the primary focus was on structured data from transactional systems. The batch-oriented nature of these tools meant that data processing occurred at scheduled intervals, often during off-peak hours, to minimize impact on operational systems.

This approach was effective when business environments were less dynamic, and real-time data access was not a critical requirement. However, the landscape has now evolved dramatically.

Continued Use and Suitable Applications

Despite the advancements in data processing technologies, these traditional ETL systems remain in use, particularly in scenarios where:

• Data volumes are predictable and manageable.

• Real-time processing is not essential, and latency is acceptable.

• Regulatory compliance necessitates stringent control over data transformations.

• Legacy systems are deeply integrated, making migration complex and costly.

In such contexts, traditional ETL tools continue to provide value by offering stability and a well-understood framework for data integration.

Emerging Limitations in 2025

As organizations strive to become more agile and data-driven, several limitations of conventional ETL systems have become apparent:

• Rigid Schemas: ETL processes require predefined schemas, making it challenging to accommodate changes in data structure or to integrate semi-structured and unstructured data sources.

• Slow Development Cycles: The development and deployment of ETL pipelines can be time-consuming, hindering the ability to respond swiftly to changing business requirements.

• High Maintenance Costs: Maintaining and updating ETL processes often involves significant manual effort, leading to increased operational costs and resource allocation.

• Limited Support for Diverse Data Types: Early-generation ETL tools are primarily optimized for structured data, lacking robust capabilities to handle the variety of data formats prevalent in modern enterprises.

• Not Optimized for AI or ML Use Cases: The batch-oriented nature and processing delays in traditional ETL systems are incompatible with the needs of AI and machine learning applications, which require real-time data access and processing.

Also, the rise of data streaming platforms and the increasing adoption of AI and machine learning in business processes necessitate a reevaluation of existing data integration strategies. Traditional ETL systems, with such inherent limitations, are increasingly seen as obstacles to innovation and agility.

The subsequent section will explore how modern platforms like Databricks address these challenges and offer a path forward for those seeking to modernize their data integration capabilities.

Databricks is Not Just a Tool, It’s a Modern Data Platform

Most companies using traditional ETL tools eventually hit the same problem. Their data infrastructure can’t keep pace with how fast the business moves. Teams begin to stretch what ETL was designed for: adding real-time workarounds, replicating data to multiple systems, and overengineering digital transformation just to support more modern analytics.

This is where Databricks enters the conversation as a platform that rethinks how data should move, be shared, and be applied across an organization.

At its core, Databricks is built for speed, flexibility, and collaboration. It combines the scalability of cloud-based storage, the power of distributed processing, and the development experience needed by today’s data teams. 

Instead of separating systems for ingestion, transformation, modeling, and analysis, Databricks brings them together through what it calls the Lakehouse architecture.

What Is a Lakehouse, and Why Does It Matter Now?

A Lakehouse combines the data lake’s ability to store raw data with the performance and governance typically associated with a warehouse. In practice, this means companies no longer have to duplicate datasets across multiple platforms or choose between speed and flexibility.

It supports:

• Batch and streaming data processing

• Structured, semi-structured, and unstructured data

• Real-time analytics and machine learning on the same platform

• Fine-grained governance and access controls

This unified approach is what allows data engineers, data scientists, and analysts to work from the same environment without delays or duplication.

Key Capabilities That Set Databricks Apart

Delta Lake is a foundational component. It brings reliability to data lakes by offering ACID transactions, schema enforcement, and time travel features that let teams roll back or audit changes. This allows for production-grade pipelines on raw data, which was previously a pain point for teams used to warehouse environments.

Collaborative Workspaces allow engineers and analysts to work together in shared notebooks, whether using SQL, Python, Scala, or R. This reduces handoffs and helps teams move faster.

MLflow, an open-source framework integrated into the platform, simplifies the process of training, tracking, and deploying machine learning models, something that typically requires multiple disconnected tools.

Scalability is built-in. Databricks runs natively on AWS, Azure, and Google Cloud, which means companies can scale compute up or down based on workload, without provisioning ahead of time or locking into vendor-specific services.

As of early 2025, Databricks serves more than 10,000 customers across 100+ countries and has reported over $3 billion in annual recurring revenue. That’s a 60% year-over-year increase, signaling how quickly organizations are transitioning from traditional stack approaches to unified platforms like Databricks.

Why This Matters Before You Compare

Before diving into a feature-by-feature comparison of Databricks and traditional ETL, it’s important to see that they weren’t built to solve the same problems. 

ETL tools were created for a world of limited scale, limited formats, and limited ambition. Databricks reflects the needs of companies that expect real-time pipelines, cross-team collaboration, and AI-driven products to be the norm, not the exception.

In the next section, we’ll break down how these two approaches differ in architecture, speed, cost, data types, and team experience, so you can see what fits your business now and what will still work two years from now.

Databricks vs Traditional ETL: A Feature-by-Feature Comparison

Databricks vs ETL: Quick Comparison

Architecture and Data Processing

Traditional ETL systems, such as Informatica and Talend, are designed around a batch-processing paradigm. They extract data from source systems, transform it according to predefined rules, and load it into a data warehouse. This process is typically scheduled at regular intervals, leading to latency in data availability.

Databricks, on the other hand, employs a Lakehouse architecture, combining the best features of data lakes and data warehouses. This architecture supports both batch and real-time data processing, enabling organizations to handle diverse data types and volumes efficiently.

Data Types and Flexibility

ETL tools are optimized for structured data and often struggle with semi-structured or unstructured data formats. This limitation can hinder organizations from leveraging the full spectrum of their data assets.

Databricks supports a wide range of data types, including structured, semi-structured, and unstructured data. Its support for various data formats allows for greater flexibility in data ingestion and processing.

Scalability and Performance

Scaling legacy ETL processes can be challenging and often requires significant infrastructure investment. Performance can degrade as data volumes grow, leading to longer processing times and potential bottlenecks.

Databricks is built on a distributed computing framework, allowing it to scale horizontally and handle large datasets efficiently. Its cloud-native design ensures consistent performance even as data volumes increase.

Integration with AI and Machine Learning

ETL processes are not inherently designed to support AI and machine learning workflows. Integrating these capabilities often requires additional tools and complex configurations.

Databricks offers built-in support for machine learning through its MLflow platform, facilitating the entire ML lifecycle from experimentation to deployment. This integration simplifies the development of AI-driven applications.

Collaboration and Development Environment

Traditional ETL tools often have limited support for collaborative development, making it challenging for data engineers, analysts, and scientists to work together seamlessly.

Databricks provides collaborative workspaces with interactive notebooks that support multiple languages, enabling cross-functional teams to collaborate effectively on data projects.

Cost and Resource Management

Maintaining traditional ETL infrastructure can be costly, with expenses related to hardware, software licenses, and personnel. Scaling resources to meet demand often involves significant capital investment.

Databricks' cloud-based model allows for dynamic resource allocation, enabling organizations to scale resources up or down based on workload requirements. This flexibility can lead to cost savings and more efficient resource utilization.

Governance and Security

ETL systems may lack advanced governance features, making it difficult to enforce data access controls and compliance policies.

Databricks includes robust governance and security features, such as role-based access controls and data lineage tracking, helping organizations meet compliance requirements and maintain data integrity.

To sum up, while traditional ETL tools have served organizations well in the past, the evolving data landscape requires more flexible, scalable, and integrated solutions. Databricks addresses these needs by offering a unified platform that supports diverse data types, real-time processing, and advanced analytics, positioning itself as a modern alternative to traditional ETL systems.

Where Traditional ETL Still Makes Sense

Not every business needs to migrate away from traditional ETL immediately. In fact, there are still scenarios where tools like Informatica, Talend, or SSIS continue to deliver value, especially in environments that are stable, regulated, or limited in scope.

Low-Change, Structured Environments

In companies where data models are consistent, volumes are modest, and transformation needs are well understood, old ETL workflows still do the job. This is often true for long-standing ERP or transactional systems that feed nightly reports. 

The cost of moving to a modern platform may not justify the return if the current process delivers what’s needed and the business has no plans to scale aggressively or integrate machine learning.

Highly Regulated Workflows

Industries such as banking, insurance, and healthcare sometimes rely on traditional ETL tools because of their long-standing compliance certifications and well-documented controls. These environments often need strict auditability, lineage tracking, and reproducibility. 

For example, if data is transformed in predictable ways and those transformations must be approved by regulators or auditors, a traditional ETL system may be easier to align with documentation and compliance practices already in place.

Cost-Sensitive, Non-Critical Workloads

There are also instances where the pipeline in question doesn’t support core business operations. In such cases, using traditional ETL may be a practical decision based on licensing already paid for or the absence of any real performance pressure. 

If the data loads once per day and latency doesn’t affect decision-making, moving it to a more advanced platform won’t offer much business gain.

Short-Term Tactical Use

Sometimes, businesses deploy traditional ETL tools in temporary or bridge scenarios. For example, during a migration phase when certain systems are not yet integrated with newer platforms, a lightweight ETL workflow can help maintain continuity. 

This only works when teams are clear that the tool is being used as a stopgap, not a long-term architectural decision.

That said, these cases are becoming exceptions rather than the rule. Most growing companies are working toward more integrated, scalable, and responsive data platforms, and even in regulated or low-volume environments, future demands often exceed what traditional ETL can handle. 

In the next section, we will explore what’s pushing organizations to make that shift now rather than later.

Why Growing Companies Are Switching in 2025

In 2025, a significant shift is occurring in how mid-market and enterprise companies approach their data infrastructure. The transition from traditional ETL tools to modern platforms like Databricks is driven by several key factors:

Scaling Challenges

As businesses grow, the limitations of traditional ETL systems become more apparent. These systems often struggle with large-scale data processing and analytics, leading to performance bottlenecks. 

Modern platforms offer scalable solutions that support analytics natively. With Databricks real-time analytics, businesses can process streaming data efficiently and respond faster, without being hindered by legacy infrastructure.

Integration of Machine Learning Use Cases

The rise of AI and machine learning has made it essential for companies to have data platforms that support these technologies. Traditional ETL tools are not inherently designed to support AI and ML workflows, making integration complex and time-consuming. 

Databricks, with its built-in support for machine learning through platforms like MLflow, simplifies the development and deployment of AI-driven applications, enabling businesses to leverage advanced analytics more effectively.

Dashboard Latency

Today, real-time data access is crucial for decision-making. Traditional ETL processes, which are often batch-oriented, can lead to delays in data availability. This latency hampers the ability to make timely decisions based on current data. 

Modern data platforms provide real-time data processing capabilities, ensuring that dashboards and reports reflect the most up-to-date information.

Fragmented Data Teams

Traditional ETL systems often result in siloed data teams, with data engineers, analysts, and scientists working in separate environments. This fragmentation can lead to inefficiencies and miscommunication. 

Modern platforms like Databricks offer collaborative workspaces that support multiple programming languages and tools, fostering better collaboration among data professionals and streamlining workflows.

The shift to platforms like Databricks in 2025 is driven by the need for scalability, integration of AI and ML, real-time data access, improved collaboration, and alignment with industry trends. 

As companies continue to grow and adapt to the evolving data landscape, adopting modern data platforms becomes not just beneficial but essential for sustained success.

What the Transition Actually Looks Like

For companies looking to modernize their data stack, the shift from traditional ETL to Databricks rarely happens overnight. It’s not a rip-and-replace effort; rather, it is a phased transition that balances modernization with continuity.

A Common Path: Pilot > Phased Migration > Full Modernization

Most teams start small. A pilot project is typically scoped around a high-friction pipeline that’s causing reporting delays or blocking ML adoption. This pilot serves two purposes: it demonstrates the impact of real-time, scalable infrastructure and gives internal teams a safe environment to learn new workflows.

Once the pilot proves successful, the next phase is typically a progressive migration of additional pipelines. Low-risk, low-dependency workflows often come first. Over time, more business-critical processes are brought in, with adjustments made to architecture, monitoring, and governance as scale increases.

Full modernization involves consolidating workflows, turning off legacy ETL dependencies, and redesigning the data platform to fully take advantage of Databricks' capabilities. By this stage, data teams have typically adopted new habits, restructured collaboration patterns, and streamlined deployment processes.

What Grows With the Platform

Two components that tend to evolve alongside the migration are Delta Live Tables and Unity Catalog.

• Delta Live Tables (DLT) helps simplify pipeline development and management by allowing teams to define transformations declaratively. It reduces maintenance overhead and brings structure to pipeline orchestration, especially in teams transitioning away from hardcoded batch logic.

• Unity Catalog offers centralized governance across workspaces, making it easier to apply access controls and audit policies consistently. For organizations with complex compliance needs or multi-team data environments, Unity Catalog provides a foundation for scalable governance.

Bridging Tools Between Legacy and Modern Workflows

Many organizations don’t jump straight into Databricks for all workloads. Transitional tools like cloud-based data integration platforms (Fivetran, Airbyte) or hybrid orchestration tools (Apache Airflow) often help bridge the gap. These tools can move data from legacy systems into the lakehouse without full rebuilds, reducing the pressure on internal teams and allowing for incremental shifts.

Some teams also use staging layers or read-only replicas from traditional warehouses to allow legacy and modern systems to coexist temporarily.

Strategic Considerations Before You Decide

Modernizing your data platform is not just about what Databricks can do. It’s about whether it fits your organization’s readiness, needs, and growth trajectory.

Before making the switch from traditional ETL to Databricks (or any other modern stack), here are some key areas to evaluate:

Cost: Infrastructure and People

Databricks offers flexibility, but that doesn’t automatically mean lower costs. If your team isn’t ready to operate in a distributed, code-first environment, you might face inefficiencies early on. Successful teams often plan upfront for Databricks cost optimization by right-sizing clusters, automating resource scaling, and aligning workloads to usage patterns. The payoff is long-term efficiency but only if the architecture and team readiness are in sync.

Data Team Maturity

A migration to Databricks works best when your team has some experience with Python, Spark, or cloud-based development workflows. If your current team primarily maintains GUI-based ETL tools, the learning curve will be real and needs to be planned for. The return will come, but only if there's bandwidth to support the change.

Data Volumes and Velocity

If your datasets are small, relatively static, and updated once daily, Databricks might be overkill. But if you’re handling growing volumes, real-time feeds, or event-based data, it becomes harder to justify sticking with tools built for slower cycles. Databricks thrives in high-velocity environments where change is constant and decisions depend on current data.

Real-Time vs Batch Needs

The core value of Databricks is unlocked when your business requires streaming data, low-latency insights, or continuous integration of multiple feeds. If most of your workflows are batch-based and can tolerate overnight refreshes, you’ll need to weigh whether this is the right moment to modernize, or whether a hybrid model makes more sense.

AI and ML Alignment

If you are investing in predictive modeling, customer intelligence, or operational forecasting, Databricks offers the architecture to support that growth. Traditional ETL tools weren’t built with these use cases in mind. Databricks, with its MLflow integration and native notebook environment, is designed for model development at scale.

Interoperability with Existing Platforms

Consider how Databricks will fit with the rest of your stack. Will it complement your use of Snowflake, AWS Glue, or Azure Data Factory? Are you looking to replace those tools, or layer Databricks on top? Many companies use Databricks as a processing engine while keeping parts of their reporting layer in existing BI tools. Understanding that boundary up front will help avoid duplication and unnecessary cost.

Also Read : Databricks vs. Snowflake: A C-Suite Guide for 2025

The Role of Certified Databricks Consulting Partners

A poorly planned migration can disrupt operations or cause costly delays. That’s why many organizations partner with experts who have done it before, not just to configure Databricks, but to guide architecture design, testing plans, dependency mapping, and data governance.

Partners help reduce learning curves, identify critical edge cases, and accelerate deployment without introducing risk.

As a certified Databricks Consulting Partner, Closeloop has guided multiple enterprise migrations from legacy ETL platforms to Databricks, supporting everything from pilot launches to full platform overhauls. 

If you are considering this shift, our Databricks consulting services can help you define a path that fits both your tech stack and your team’s readiness.

Takeaway: Choosing What Moves the Business Forward

The decision to move away from traditional ETL tools is not about following trends. It’s about choosing systems that actually support how your teams need to work today and tomorrow.

Traditional ETL still works in some settings, especially where data is predictable and static. But for growing companies that are scaling fast, launching ML initiatives, or struggling with fragmented workflows, these systems often slow things down. They weren’t built for real-time decision-making, continuous data updates, or cross-functional collaboration.

Databricks offers a different model, one that matches the pace of modern businesses. It gives teams a platform they can build on, experiment in, and scale with. And while the transition takes planning, the long-term value often comes not just from speed or efficiency, but from enabling entirely new possibilities.

If you are unsure whether to stick, upgrade, or rethink your stack, you don’t have to map it out alone. Closeloop works with organizations at every stage of this decision, whether it’s running a pilot, evaluating use cases, or planning a phased migration. We help you cut through the noise, assess readiness, and define what modernization should actually look like for your business.

Want to explore whether Databricks fits your roadmap? Let’s connect.