As modern data teams grapple with growing complexity, one foundational question continues to emerge: ETL or ELT? While the two may look similar at a glance, the order in which data is transformed makes a world of difference—impacting everything from speed and scalability to cost and compliance.
With the rise of powerful cloud platforms like Snowflake and Databricks, the traditional ETL model is being challenged. Modern data teams are embracing ELT for its flexibility and cloud-native advantages—but that doesn’t mean ETL is obsolete.
In this article, we break down how ETL and ELT work, when to use each approach, and how modern platforms like Coalesce are reshaping the future of data transformation.
What is ETL?
A legacy approach to data integration
Developers highly leveraged an ETL (Extract, Transform, Load) approach during a time when traditional data warehouses were both expensive and had limited processing power. In this model, data comes from source systems. It is transformed on a separate processing server or staging area. Then, the team loads it into the data warehouse for analysis.
This model worked well for earlier enterprise needs. It allowed data engineers to clean, join, and enrich data before it reached the analytics layer. Only structured, validated data entered the warehouse, making it easier to manage data quality and compliance.
When and why ETL still works
ETL is still a good choice for older systems and strict compliance needs. It’s especially useful when data needs to be cleaned or masked before entering the cloud—whether for privacy, security, or regulatory reasons.
It also works well for predictable batch jobs and complex data processes that might be expensive or difficult to run directly in a cloud data warehouse. Some teams are also starting to use ETL for data lake or lakehouse setups, where preparing data before loading makes sense.
However, ETL can be rigid. If transformation rules change, the whole pipeline often needs to be run again from the start—including extracting the data—which adds delays and slows things down in fast-moving environments.
What is ELT?
ELT (Extract, Load, Transform) reverses the final two steps of the ETL process. Data is extracted and loaded raw into a cloud data warehouse—such as Snowflake—before any transformation occurs. Once you store the data, the warehouse’s native compute engine executes the transformations.
This model has become popular because cloud platforms separate storage and compute, making it easier and more cost-effective to store and process large volumes of data. ELT enables teams to keep full source data available, which offers greater flexibility in how and when they transform it.
Key benefits of ELT
ELT allows organizations to ingest data quickly and transform it in stages. Raw data stays in the warehouse. This allows teams to fix errors or change business logic. They do not need to re-ingest the data from its source.
This model also scales naturally with modern data environments. Cloud data warehouses can process changes quickly and in parallel, making ELT ideal for handling large and varied data sets, such as JSON files, logs, or API outputs. This flexibility is especially valuable for powering real-time dashboards and enabling self-service analytics.
ETL vs. ELT: What’s the real difference?
| Category | ETL – Transform before loading | ELT – Transform after loading |
| Transformation timing & location | Data is transformed before it enters the warehouse, usually on a separate server. | Data is transformed inside the warehouse after loading, using warehouse compute. |
| Performance & capacity to scale | Can become slow and hard to scale as data volumes grow; requires manual infrastructure scaling. | Uses cloud-native platforms that scale automatically and efficiently. |
| Flexibility & data access | Filters and formats data early, limiting access to raw details. | Keeps raw data available for full discoverability and testing. |
Transformation timing and location
The core difference between ETL and ELT lies in the timing and location of the transformation step. ETL performs transformations outside the warehouse, before data is loaded. ELT performs transformations inside the warehouse, after raw data has already been ingested.
This affects how teams design their pipelines. ETL pipelines are typically more rigid and sequential. ELT pipelines allow for a more modular, flexible approach—better aligned with agile development practices.
Performance and capacity to scale
ETL can become a performance bottleneck as data volumes grow. Since all transformations happen before loading, ETL jobs may take longer and often require manual infrastructure scaling. ELT, by contrast, takes advantage of cloud platforms that scale automatically and use distributed compute for faster processing. However, that convenience comes with a trade-off: you no longer manage infrastructure, but you do pay for the compute you use—making cost optimization an important part of any ELT strategy.
Flexibility and data access
ETL filters and formats data before it reaches analysts, which can limit what’s available for exploration. ELT preserves the raw data, allowing for broader use cases, from debugging to machine learning. This flexibility gives data teams more options and more confidence in their models.
When to use ETL or ELT?
When to use ETL
ETL is still the right choice in several scenarios:
- When data must be cleaned or anonymized before entering a warehouse, for compliance or security reasons
- In legacy environments with on-premises infrastructure or where cloud compute isn’t available
- For well-defined, stable pipelines—or highly complex transformations that benefit from external processing power and can take advantage of specialized infrastructure or parallel compute not available in the warehouse
In these cases, ETL provides strong control and pre-load governance, though often at the cost of speed and adaptability.
When to use ELT
ELT is ideal for cloud-native data teams that value agility, scalability, and speed. It’s particularly well suited for:
- High-volume, high-velocity data from SaaS applications, event streams, or web platforms
- Evolving data models that require frequent updates or experimentation
- Real-time analytics and self-service BI, where raw data access is a strategic advantage
ELT enables rapid ingestion and flexible modelling, helping teams respond faster to business needs.
Hybrid approaches: ETL and ELT together
In practice, many enterprises use a combination of both. For example, sensitive HR or finance data might go through an ETL process for compliance, while product analytics data from a web app flows into the warehouse via ELT.
This hybrid model lets teams optimize based on source type, regulatory requirements, and processing needs. The key to success is orchestration—ensuring that all data, no matter how it arrives, is transformed consistently and governed effectively.
Migrating from ETL to ELT: a strategic shift
Many organizations are moving from legacy ETL tools to modern ELT workflows—but making this shift successfully requires careful planning and team alignment.
A good first step is to identify pipelines with high-volume, well-structured data, as these are typically the easiest to migrate. Running ETL and ELT processes in parallel allows teams to validate results, ensuring parity before fully retiring legacy tools.
Equally important is team readiness. ELT requires engineers to manage transformations within the data warehouse using tools like SQL or Coalesce. Providing training, documentation, and a modular framework for transformation can ease the transition and promote long-term success.
At Group 1001, this approach delivered remarkable results. By modernising their stack with Snowflake, Fivetran, and Coalesce, the data engineering team cut iteration cycles from three months to just two days, with a 10x productivity boost.
For teams considering a similar path, our webinar with Group 1001’s Head of Data Engineering offers a firsthand look at the challenges and payoffs of transitioning from legacy ETL to a modern ELT stack—and how to build for scale without the need for a massive team.
How Coalesce supports modern ELT (and ETL)
Coalesce is a data transformation platform built specifically for cloud environments. It empowers teams to scale data modelling by simplifying and accelerating transformation workflows leveraging the ELT approach.
Coalesce runs all transformations natively within cloud data warehouses like Snowflake. This offers the power and flexibility of ELT, without sacrificing the structure and visibility often associated with ETL platforms.
Its metadata-driven interface supports column-level automation, reusable templates, and bulk editing capabilities for your most complex data transformations. Teams can apply transformations visually or in code, depending on their preference or expertise. And thanks to seamless integrations with tools like Fivetran, Coalesce enables fully orchestrated, end-to-end ELT pipelines with minimal manual overhead.
For teams migrating from legacy systems, Coalesce provides a flexible foundation to modernise transformation without losing control. For teams already working in ELT, it improves speed, clarity, and maintainability at scale.
Cost optimization considerations for data transformation in Snowflake
As more organisations adopt ELT and shift transformation workloads into cloud data warehouses like Snowflake, cost optimization becomes a critical concern. While ELT improves agility and scalability, it can also increase compute consumption—especially if data teams don’t follow best practices for workload design and orchestration.
Coalesce helps reduce transformation costs by simplifying decisions around how data is materialised (e.g. choosing views over tables where appropriate), enabling incremental loading strategies, and breaking down complex queries into modular, reusable pipeline components. These techniques ensure that data pipelines run efficiently, avoid unnecessary compute, and remain easy to manage over time.
To further reduce data latency and eliminate manual coordination between ingestion and transformation jobs, teams can now take advantage of the native Coalesce + Fivetran integration. This integration allows transformation workflows in Coalesce to trigger automatically after each Fivetran sync—enabling near real-time reporting without the need for a third-party orchestrator.
Beyond cost and speed, pipeline design directly impacts long-term maintainability and scalability. Many teams rely on SQL transformation techniques like using Common Table Expressions (CTEs), which can become difficult to debug, optimize, or reuse. A pipeline-based approach—where each step is modular and metadata-driven—offers clearer lineage, parallel execution, and easier performance tuning. Coalesce makes this shift seamless, enabling teams to convert CTE-heavy SQL into maintainable, orchestrated pipelines in just a few clicks.
How leading teams use Coalesce
Coalesce isn’t just built for the modern data stack—it’s already powering transformation at scale across industries. From insurance to banking to restaurants, here’s how top organisations are rethinking data workflows with Coalesce:
- Group 1001: Achieved a 10x productivity boost and cut reporting cycles from 3 months to 2 days by modernizing its data stack with Coalesce, Snowflake, and Fivetran.
👉 Read case study › - United Community Banks: Reduced complex JSON modeling time by 75% and sped up repetitive node creation 2x using Coalesce templates and native integrations.
👉 Read case study › - Alterman: Tripled data team productivity and cut JSON parsing time by up to 3x, helping this 100-year-old construction firm go fully modern in months.
👉 Read case study › - Denny’s: Migrated 25 dimension tables 3x faster, cut documentation time by 65%, and enabled real-time reporting with automated lineage and Snowflake-native execution.
👉 Read case study ›
Conclusion
Choosing between ETL and ELT isn’t about one being better than the other—it’s about selecting the right method for your data, team, and infrastructure. ETL offers control and compliance in more traditional settings. ELT delivers speed and scale in the cloud. Most enterprises will benefit from a mix of both, and the flexibility to evolve as needs change.
With modern transformation platforms like Coalesce, organisations no longer have to compromise. Whether building a new ELT workflow or upgrading an ETL legacy system, Coalesce helps teams transform data faster, with less effort and more control—bringing speed, flexibility, and governance to every step of the process.
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